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BackgroundAdults undergoing spine surgery often have underlying osteoporosis, which may be a risk factor for postoperative complications. Although these associations have been described, osteoporosis remains profoundly underdiagnosed and undertreated in the spine surgery population. A thorough, comprehensive systematic review summarizing the relationships between bone mineral density (BMD) and specific complications of lumbar fusion surgery could be a valuable resource for raising awareness and supporting clinical practice changes.MethodsPubMed, Embase, and Web of Science databases were searched for original clinical research articles reporting on BMD, or surrogate measure, as a predictor of complications in adults undergoing elective lumbar fusion for degenerative disease or deformity. Endpoints included cage subsidence, screw loosening, pseudarthrosis, vertebral fracture, junctional complications, and reoperation.ResultsA total of 71 studies comprising 12,278 patients were included. Overall, considerable heterogeneity in study populations, methods of bone health assessment, and definition and evaluation of clinical endpoints precluded meta-analysis. Nevertheless, low BMD was associated with higher rates of implant failures like cage subsidence and screw loosening, which were often diagnosed with concomitant pseudarthrosis. Osteoporosis was also a significant risk factor for proximal junctional kyphosis, particularly due to fracture. Many studies found surgical site-specific BMD to best predict focal complications. Functional outcomes were inconsistently addressed.ConclusionsOur findings suggest osteoporosis is a significant risk factor for mechanical complications of lumbar fusion. These results emphasize the importance of preoperative osteoporosis screening, which allows for medical and surgical optimization of high-risk patients. This review also highlights current practical challenges facing bone health evaluation in patients undergoing elective surgery. Future prospective studies using standardized methods are necessary to strengthen existing evidence, identify optimal predictive thresholds, and establish specialty-specific practice guidelines. In the meantime, an awareness of the surgical implications of osteoporosis and utility of preoperative screening can provide for more informed, effective patient care.

Purpose: This study aimed to develop machine learning methods to estimate bone mineral density and detect osteopenia/osteoporosis from conventional lumbar MRI (T1-weighted and T2-weighted images) and planar radiography in combination with clinical data and imaging parameters of the acquisition protocol. Methods: A database of 429 patients subjected to lumbar MRI, radiographs and dual-energy x-ray absorptiometry within 6 months was created from an institutional database. Several machine learning models were trained and tested (373 patients for training, 86 for testing) with the following objectives: (1) direct estimation of the vertebral bone mineral density; (2) classification of T-score lower than − 1 or (3) lower than − 2.5. The models took as inputs either the images or radiomics features derived from them, alone or in combination with metadata (age, sex, body size, vertebral level, parameters of the imaging protocol). Results: The best-performing models achieved mean absolute errors of 0.15–0.16 g/cm2 for the direct estimation of bone mineral density, and areas under the receiver operating characteristic curve of 0.82 (MRIs) − 0.80 (radiographs) for the classification of T-scores lower than − 1, and 0.80 (MRIs) − 0.65 (radiographs) for T-scores lower than − 2.5. Conclusions: The models showed good discriminative performances in detecting cases of low bone mineral density, and more limited capabilities for the direct estimation of its value. Being based on routine imaging and readily available data, such models are promising tools to retrospectively analyse existing datasets as well as for the opportunistic investigation of bone disorders. [ABSTRACT FROM AUTHOR]

Background: Cystic fibrosis bone disease (CFBD) is a common comorbidity in adult people with cystic fibrosis (pwCF), resulting in an increased risk of bone fractures. This study evaluated the capacity of artificial intelligence (AI)-assisted low-dose chest CT (LDCT) opportunistic screening for detecting low bone mineral density (BMD) in adult pwCF. Methods: In this retrospective single-center study, 65 adult pwCF (mean age 30.1 ± 7.5 years) underwent dual-energy X-ray absorptiometry (DXA) of the lumbar vertebrae L1 to L4 to determine BMD and corresponding z-scores and completed LDCTs of the chest within three months as part of routine clinical care. A fully automated CT-based AI algorithm measured the attenuation values (Hounsfield units [HU]) of the thoracic vertebrae Th9–Th12 and first lumbar vertebra L1. The ability of the algorithm to diagnose CFBD was assessed using receiver operating characteristic (ROC) curves. Results: HU values of Th9 to L1 and DXA-derived BMD and the corresponding z-scores of L1 to L4 showed a strong correlation (all p < 0.05). The area under the curve (AUC) for diagnosing low BMD was highest for L1 (0.796; p = 0.001) and Th11 (0.835; p < 0.001), resulting in a specificity of 84.9% at a sensitivity level of 75%. The HU threshold values for distinguishing normal from low BMD were <197 (L1) and <212 (Th11), respectively. Conclusions: Routine LDCT of the chest with the fully automated AI-guided determination of thoracic and lumbar vertebral attenuation values is a valuable tool for predicting low BMD in adult pwCF, with the best results for Th11 and L1. However, further studies are required to define clear threshold values. [ABSTRACT FROM AUTHOR]

Purpose: This scoping review aimed to assess the current research on artificial intelligence (AI)--enhanced opportunistic screening approaches for stratifying osteoporosis and osteopenia risk by evaluating vertebral trabecular bone structure in CT scans. Methods: PubMed, Scopus, and Web of Science databases were systematically searched for studies published between 2018 and December 2023. Inclusion criteria encompassed articles focusing on AI techniques for classifying osteoporosis/osteopenia or determining bone mineral density using CT scans of vertebral bodies. Data extraction included study characteristics, methodologies, and key findings. Results: Fourteen studies met the inclusion criteria. Three main approaches were identified: fully automated deep learning solutions, hybrid approaches combining deep learning and conventional machine learning, and non-automated solutions using manual segmentation followed by AI analysis. Studies demonstrated high accuracy in bone mineral density prediction (86-96%) and classification of normal versus osteoporotic subjects (AUC 0.927-0.984). However, significant heterogeneity was observed in methodologies, workflows, and ground truth selection. Conclusions: The review highlights AI's promising potential in enhancing opportunistic screening for osteoporosis using CT scans. While the field is still in its early stages, with most solutions at the proof-of-concept phase, the evidence supports increased efforts to incorporate AI into radiologic workflows. Addressing knowledge gaps, such as standardizing benchmarks and increasing external validation, will be crucial for advancing the clinical application of these AI-enhanced screening methods. Integration of such technologies could lead to improved early detection of osteoporotic conditions at a low economic cost. [ABSTRACT FROM AUTHOR]

Background: Hypertension is a recognized risk factor that underlies the epidemic of cardiovascular diseases. Guidelines, including those from the European Society of Hypertension, recommend opportunistic screening for hypertension in all adults. However, there have been no institution-based studies on the prevalence of hypertension and its associated factors with an opportunistic screening program in Ethiopia. Hence, this study aimed to assess the prevalence of newly diagnosed hypertension and its associated factors in an opportunistic screening program in Ethiopia. Methods: This was an institution-based cross-sectional study conducted on adult participants in an opportunistic hypertension screening program at Yekatit 12 Hospital Medical College, Addis Ababa, Ethiopia, from November 1, 2023, to February 1, 2024. Data were collected using a structured questionnaire, constructed as per the WHO STEPwise approach to non-communicable disease risk factor surveillance (STEPS). The data was analyzed using Statistical Package for Social Sciences (SPSS), version 26. Descriptive analysis was used to compile the sociodemographic and clinical characteristics of the participants, and logistic regression analyses were performed to determine the factors associated with hypertension. Results: A total of 301 adult participants were included in this study. The mean age of the participants was 47.6 years (standard deviation: 13.5), and 62.5% were males. The prevalence of newly diagnosed hypertension was 36.2% (95% confidence interval [CI]: 5.6, 66.8). Male sex (adjusted odds ratio (AOR) = 2.06, 95% (CI): 1.05, 4.04), being married (AOR = 4.8, 95% CI: 1.84, 2.77) or widowed (AOR = 5.14, 95% CI: 1.23, 1.46), less frequent intake of vegetables and/or fruits [< 3 days per week (AOR = 2.88, 95% CI: 1.12, 7.39), and 3 to 5 days per week (AOR = 2.22, 95% CI: 1.02, 4.86)], physical inactivity (AOR = 2.26, 95% CI: 1.21, 4.22), and body mass index (AOR = 1.17, 95% CI: 1.09, 1.26), had significant associations with hypertension. Conclusion: This study demonstrated a high prevalence of newly diagnosed hypertension in an opportunistic screening program in Addis Ababa, Ethiopia. It also revealed that most of the factors significantly associated with hypertension were modifiable, underscoring the importance of promoting lifestyle changes. Most importantly, expanding institution-based opportunistic screening programs could be an effective approach to maximize the detection of hypertension and improve access to its treatment. [ABSTRACT FROM AUTHOR]

Osteoporotic vertebral compression fractures (OVCFs) are a highly prevalent source of morbidity and mortality, and preventive treatment has been demonstrated to be both effective and cost effective. To take advantage of the information available on existing chest and abdominal radiographs, the authors' study group has developed software to access these radiographs for OVCFs with high sensitivity and specificity using an established artificial intelligence deep learning algorithm. The aim of this analysis was to assess the potential cost-effectiveness of implementing this software. A deterministic expected-value cost-utility model was created, combining a tree model and a Markov model, to compare the strategies of opportunistic screening for OVCFs against usual care. Total costs and total quality-adjusted life-years were calculated for each strategy. Screening and treatment costs were considered from a limited societal perspective, at 2022 prices. In the base case, assuming a cost of software implantation of $10 per patient screened, the screening strategy dominated the nonscreening strategy: it resulted in lower cost and increased quality-adjusted life-years. The lower cost was due primarily to the decreased costs associated with fracture treatment and decreased probability of requiring long-term care in patients who received preventive treatment. The screening strategy was dominant up to a cost of $46 per patient screened. Artificial intelligence–based opportunistic screening for OVCFs on existing radiographs can be cost effective from a societal perspective. [Display omitted] [ABSTRACT FROM AUTHOR]

Abstract Background Hypertension is a recognized risk factor that underlies the epidemic of cardiovascular diseases. Guidelines, including those from the European Society of Hypertension, recommend opportunistic screening for hypertension in all adults. However, there have been no institution-based studies on the prevalence of hypertension and its associated factors with an opportunistic screening program in Ethiopia. Hence, this study aimed to assess the prevalence of newly diagnosed hypertension and its associated factors in an opportunistic screening program in Ethiopia. Methods This was an institution-based cross-sectional study conducted on adult participants in an opportunistic hypertension screening program at Yekatit 12 Hospital Medical College, Addis Ababa, Ethiopia, from November 1, 2023, to February 1, 2024. Data were collected using a structured questionnaire, constructed as per the WHO STEPwise approach to non-communicable disease risk factor surveillance (STEPS). The data was analyzed using Statistical Package for Social Sciences (SPSS), version 26. Descriptive analysis was used to compile the sociodemographic and clinical characteristics of the participants, and logistic regression analyses were performed to determine the factors associated with hypertension. Results A total of 301 adult participants were included in this study. The mean age of the participants was 47.6 years (standard deviation: 13.5), and 62.5% were males. The prevalence of newly diagnosed hypertension was 36.2% (95% confidence interval [CI]: 5.6, 66.8). Male sex (adjusted odds ratio (AOR) = 2.06, 95% (CI): 1.05, 4.04), being married (AOR = 4.8, 95% CI: 1.84, 2.77) or widowed (AOR = 5.14, 95% CI: 1.23, 1.46), less frequent intake of vegetables and/or fruits [

Abstract Background In an ageing population, low impact fragility fractures are becoming increasingly common. However, fracture risk can be reduced where low bone density can be identified at an early stage. In this study we aim to demonstrate that IBEX Bone Health (IBEX BH) can provide a clinically useful prediction from wrist radiographs of aBMD and T-score at the ultra-distal (UD) and distal-third (DT) regions of the radius. Methods A 261-participant single-centre, non-randomised, prospective, study was carried out to compare a) IBEX BH, a quantitative digital radiography software device, to b) Dual-energy X-ray Absorptiometry (DXA). A total of 257 participants with wrist digital radiograph (DR), forearm DXA pairs were included in the analysis after exclusions. Results The adjusted R2 value for IBEX BH outputs to the radial areal bone mineral density (aBMD) produced by a GE Lunar DXA system for the UD region is 0.87 (99% Confidence Interval (CI) [0.84, 0.89]). The adjusted R2 value for IBEX BH outputs to aBMD for the DT region is 0.88 (99% CI [0.85, 0.90]). The Area Under the Receiver Operating Characteristic curve (AUC) for the forearm T-score ≤ − 2.5 risk prediction model at the UD region is 0.95 (99% CI [0.93, 0.98]). The AUC for the forearm T-score ≤ − 2.5 risk prediction model at the DT region is 0.98 (99% CI [0.97, 0.99]). Conclusion From a DR of the wrist, IBEX BH provides a clinically useful i) estimate of aBMD at the two regions of interest on the radius and ii) risk prediction model of forearm T-score ≤ − 2.5 at the UD and DT regions.

Background Osteoporosis is the most common metabolic bone disease and can cause fragility fractures. Despite this, screening utilization rates for osteoporosis remain low among populations at risk. Automated bone mineral density (BMD) estimation using computed tomography (CT) can help bridge this gap and serve as an alternative screening method to dual-energy X-ray absorptiometry (DXA). Methods The feasibility of an opportunistic and population agnostic screening method for osteoporosis using abdominal CT scans without bone densitometry phantom-based calibration was investigated in this retrospective study. A total of 268 abdominal CT-DXA pairs and 99 abdominal CT studies without DXA scores were obtained from an oncology specialty clinic in the Republic of Korea. The center axial CT slices from the L1, L2, L3, and L4 lumbar vertebrae were annotated with the CT slice level and spine segmentation labels for each subject. Deep learning models were trained to localize the center axial slice from the CT scan of the torso, segment the vertebral bone, and estimate BMD for the top four lumbar vertebrae. Results Automated vertebra-level DXA measurements showed a mean absolute error (MAE) of 0.079, Pearson’s r of 0.852 (P

Opportunistic osteoporosis screening using deep learning (DL) analysis of low-dose chest CT (LDCT) scans is a potentially promising approach for the early diagnosis of this condition. We explored bone mineral density (BMD) profiles across all adult ages and prevalence of osteoporosis using LDCT with DL in a Korean population. This retrospective study included 1915 participants from two hospitals who underwent LDCT during general health checkups between 2018 and 2021. Trabecular volumetric BMD of L1-2 was automatically calculated using DL and categorized according to the American College of Radiology quantitative computed tomography diagnostic criteria. BMD decreased with age in both men and women. Women had a higher peak BMD in their twenties, but lower BMD than men after 50. Among adults aged 50 and older, the prevalence of osteoporosis and osteopenia was 26.3% and 42.0%, respectively. Osteoporosis prevalence was 18.0% in men and 34.9% in women, increasing with age. Compared to previous data obtained using dual-energy X-ray absorptiometry, the prevalence of osteoporosis, particularly in men, was more than double. The automated opportunistic BMD measurements using LDCT can effectively predict osteoporosis for opportunistic screening and identify high-risk patients. Patients undergoing lung cancer screening may especially profit from this procedure requiring no additional imaging or radiation exposure. [ABSTRACT FROM AUTHOR]

The opportunistic use of radiological examinations for disease detection can potentially enable timely management. We assessed if an index created by an AI software to quantify chest radiography (CXR) findings associated with heart failure (HF) could distinguish between patients who would develop HF or not within a year of the examination. Our multicenter retrospective study included patients who underwent CXR without an HF diagnosis. We included 1117 patients (age 67.6 ± 13 years; m:f 487:630) that underwent CXR. A total of 413 patients had the CXR image taken within one year of their HF diagnosis. The rest (n = 704) were patients without an HF diagnosis after the examination date. All CXR images were processed with the model (qXR-HF, Qure.AI) to obtain information on cardiac silhouette, pleural effusion, and the index. We calculated the accuracy, sensitivity, specificity, and area under the curve (AUC) of the index to distinguish patients who developed HF within a year of the CXR and those who did not. We report an AUC of 0.798 (95%CI 0.77–0.82), accuracy of 0.73, sensitivity of 0.81, and specificity of 0.68 for the overall AI performance. AI AUCs by lead time to diagnosis (<3 months: 0.85; 4–6 months: 0.82; 7–9 months: 0.75; 10–12 months: 0.71), accuracy (0.68–0.72), and specificity (0.68) remained stable. Our results support the ongoing investigation efforts for opportunistic screening in radiology. [ABSTRACT FROM AUTHOR]

Background: In an ageing population, low impact fragility fractures are becoming increasingly common. However, fracture risk can be reduced where low bone density can be identified at an early stage. In this study we aim to demonstrate that IBEX Bone Health (IBEX BH) can provide a clinically useful prediction from wrist radiographs of aBMD and T-score at the ultra-distal (UD) and distal-third (DT) regions of the radius. Methods: A 261-participant single-centre, non-randomised, prospective, study was carried out to compare a) IBEX BH, a quantitative digital radiography software device, to b) Dual-energy X-ray Absorptiometry (DXA). A total of 257 participants with wrist digital radiograph (DR), forearm DXA pairs were included in the analysis after exclusions. Results: The adjusted R2 value for IBEX BH outputs to the radial areal bone mineral density (aBMD) produced by a GE Lunar DXA system for the UD region is 0.87 (99% Confidence Interval (CI) [0.84, 0.89]). The adjusted R2 value for IBEX BH outputs to aBMD for the DT region is 0.88 (99% CI [0.85, 0.90]). The Area Under the Receiver Operating Characteristic curve (AUC) for the forearm T-score ≤ − 2.5 risk prediction model at the UD region is 0.95 (99% CI [0.93, 0.98]). The AUC for the forearm T-score ≤ − 2.5 risk prediction model at the DT region is 0.98 (99% CI [0.97, 0.99]). Conclusion: From a DR of the wrist, IBEX BH provides a clinically useful i) estimate of aBMD at the two regions of interest on the radius and ii) risk prediction model of forearm T-score ≤ − 2.5 at the UD and DT regions. [ABSTRACT FROM AUTHOR]

Background: Non-communicable diseases (NCDs) are responsible for 51% of total mortality in South Africa, with a rising burden of hypertension (HTN) and diabetes mellitus (DM). Incorporating NCDs and COVID-19 screening into mass activities such as COVID-19 vaccination programs could offer significant long-term benefits for early detection interventions. However, there is limited knowledge of the associated costs and resources required. We evaluated the cost of integrating NCD screening and COVID-19 antigen rapid diagnostic testing (Ag-RDT) into a COVID-19 vaccination program. Methods: We conducted a prospective cost analysis at three public sector primary healthcare clinics and one academic hospital in Johannesburg, South Africa, conducting vaccinations. Participants were assessed for eligibility and recruited during May-Dec 2022. Costs were estimated from the provider perspective using a bottom-up micro-costing approach and reported in 2022 USD. Results: Of the 1,376 enrolled participants, 240 opted in to undergo a COVID-19 Ag-RDT, and none tested positive for COVID-19. 138 (10.1%) had elevated blood pressure, with 96 (70%) having no prior HTN diagnosis. 22 (1.6%) were screen-positive for DM, with 12 (55%) having no prior diagnosis. The median cost per person screened for NCDs was $1.70 (IQR: $1.38-$2.49), respectively. The average provider cost per person found to have elevated blood glucose levels and blood pressure was $157.99 and $25.19, respectively. Finding a potentially new case of DM and HTN was $289.65 and $36.21, respectively. For DM and DM + HTN screen-positive participants, diagnostic tests were the main cost driver, while staff costs were the main cost driver for DM- and HTN screen-negative and HTN screen-positive participants. The median cost per Ag-RDT was $5.95 (IQR: $5.55-$6.25), with costs driven mainly by test kit costs. Conclusions: We show the cost of finding potentially new cases of DM and HTN in a vaccine queue, which is an essential first step in understanding the feasibility and resource requirements for such initiatives. However, there is a need for comparative economic analyses that include linkage to care and retention data to fully understand this cost and determine whether opportunistic screening should be added to general mass health activities. [ABSTRACT FROM AUTHOR]

This study investigates how a lack of social support differentially affects men and women's colorectal cancer (CRC) screening participation, considering different screening strategies implemented across European countries. Although health sociology has stressed gender differences in social support and its effects on health behaviours, this was overlooked by cancer screening research. Using a data set of 65,961 women and 55,602 men in 31 European countries, we analysed the effect of social support variables on CRC screening uptake. We found that living alone and lower perceived social support were associated with lower screening uptake for both men and women. These effects were, however, stronger among men. Population‐based screening programmes mitigated these effects, particularly for women, but not for men living alone. In countries with opportunistic screening programmes, social support variables remained associated with screening uptake. We conclude that cancer screening interventions should pay attention to social support and its gender‐differentiated effects. [ABSTRACT FROM AUTHOR]

Objective: To compare the coronal plane with axial and sagittal planes in opportunistic screening of osteoporosis using computed tomography (CT). Materials and methods: A total of 100 patients aged ≥ 50 years who underwent both lumbar spine CT and dual-energy X-ray absorptiometry within 3 months were included. Osteoporosis was diagnosed based on dual-energy X-ray absorptiometry results. The CT number was measured at the center of the vertebral body in coronal, axial, and sagittal planes. To compare the coronal plane with axial and sagittal planes in diagnosing osteoporosis, the areas under the receiver operating characteristic curve (AUC) were compared and intraclass correlation coefficient (ICC) was calculated. The optimal cutoff values were calculated using Youden's index. Results: The AUC of the coronal plane (0.80; 95% confidence interval [CI], 0.71–0.89) was not significantly different from that of the axial plane (0.78; 95% CI, 0.68–0.87; P = 0.39) and that of the sagittal plane (0.78; 95% CI, 0.69–0.87; P = 0.68). Excellent concordance rates were observed between coronal and axial planes with ICC of 0.95 (95% CI, 0.92–0.96) and between coronal and sagittal planes with ICC of 0.93 (95% CI, 0.85–0.96). The optimal cutoff values for the coronal, axial, and sagittal planes were 110, 112, and 112 HU, respectively. Conclusion: The coronal plane does not significantly differ from axial and sagittal planes in opportunistic screening of osteoporosis. Thus, the coronal plane as well as axial and sagittal planes can be used interchangeably in measuring bone mineral density using CT. [ABSTRACT FROM AUTHOR]

Magnetic resonance imaging (MRI) of the brain has been implemented to evaluate multiple intracranial pathologies. Non-contrast T2-weighted images are a routinely acquired sequence in almost all neuroimaging protocols. It is not uncommon to encounter various cerebrovascular lesions incidentally on brain imaging. Neuroradiologists should evaluate the routine T2-weighted images for incidental cerebrovascular lesions, irrespective of the primary indication of the study. Vascular structures typically demonstrate a low signal flow-void on the T2-weighted images. In our experience, large cerebrovascular abnormalities are easily visible to a typical neuroradiologist. In this article, we present the spectrum of the characteristic imaging appearance of various intracranial cerebrovascular lesions on routine non-contrast T2-weighted MRI. These include aneurysm, arteriovenous malformation, arterial occlusion, capillary telangiectasia, cavernous malformation, dural arteriovenous fistula, moyamoya, proliferative angiopathy, and vein of Galen malformation. [ABSTRACT FROM AUTHOR]

Rationale and objectivesSpinal osteoporotic compression fractures (OCFs) can be an early biomarker for osteoporosis but are often subtle, incidental, and underreported. To ensure early diagnosis and treatment of osteoporosis, we aimed to build a deep learning vertebral body classifier for OCFs as a critical component of our future automated opportunistic screening tool.Materials and methodsWe retrospectively assembled a local dataset, including 1790 subjects and 15,050 vertebral bodies (thoracic and lumbar). Each vertebral body was annotated using an adaption of the modified-2 algorithm-based qualitative criteria. The Osteoporotic Fractures in Men (MrOS) Study dataset provided thoracic and lumbar spine radiographs of 5994 men from six clinical centers. Using both datasets, five deep learning algorithms were trained to classify each individual vertebral body of the spine radiographs. Classification performance was compared for these models using multiple metrics, including the area under the receiver operating characteristic curve (AUC-ROC), sensitivity, specificity, and positive predictive value (PPV).ResultsOur best model, built with ensemble averaging, achieved an AUC-ROC of 0.948 and 0.936 on the local dataset's test set and the MrOS dataset's test set, respectively. After setting the cutoff threshold to prioritize PPV, this model achieved a sensitivity of 54.5% and 47.8%, a specificity of 99.7% and 99.6%, and a PPV of 89.8% and 94.8%.ConclusionOur model achieved an AUC-ROC>0.90 on both datasets. This testing shows some generalizability to real-world clinical datasets and a suitable performance for a future opportunistic osteoporosis screening tool.

Introduction: In recent decades, attendance to organized breast cancer screening has been decreasing in European countries. This could be partly due to an increase in the use of opportunistic screening. The aim of this study was to assess the coverage of imaging in and outside the screening program in Finland during the period of 1999–2018. We also compared the usage of imaging services across sociodemographic groups in the more recent years (2017–2018). Methods: Our initial data consisted of 1,159,000 screening-target-aged women (50–69 years) in 1999–2018 and 1,849,000 women aged 30–89 years in 2017–2018. Data on organized breast cancer screening program was drawn from the Finnish Cancer Registry and supplemented with comprehensive individual data on mammograms and ultrasounds performed outside the program. Results: Among the screening-aged women (50–69), a clear decline in the overall imaging coverage was observed during the study period (from 89 to 85%). The use of outside imaging increased slightly but not enough to compensate for the overall decrease. There were large differences in coverages between sociodemographic groups. Compared to manual workers and the unemployed, upper-level employees were around two times more active in using outside imaging (8.2% vs. 3.6% and 4.3%, respectively). Interpretation: Overall breast imaging coverage has slowly decreased during the 2000s, while outside imaging has increased slightly. The coverage of imaging in and outside the program largely followed the same trends, with the highest usage concentrating on higher socioeconomical groups, native speakers and highly educated women.

Rationale and objectivesOsteoporosis affects 9% of individuals over 50 in the United States and 200 million women globally. Spinal osteoporotic compression fractures (OCFs), an osteoporosis biomarker, are often incidental and under-reported. Accurate automated opportunistic OCF screening can increase the diagnosis rate and ensure adequate treatment. We aimed to develop a deep learning classifier for OCFs, a critical component of our future automated opportunistic screening tool.Materials and methodsThe dataset from the Osteoporotic Fractures in Men Study comprised 4461 subjects and 15,524 spine radiographs. This dataset was split by subject: 76.5% training, 8.5% validation, and 15% testing. From the radiographs, 100,409 vertebral bodies were extracted, each assigned one of two labels adapted from the Genant semiquantitative system: moderate to severe fracture vs. normal/trace/mild fracture. GoogLeNet, a deep learning model, was trained to classify the vertebral bodies. The classification threshold on the predicted probability of OCF outputted by GoogLeNet was set to prioritize the positive predictive value (PPV) while balancing it with the sensitivity. Vertebral bodies with the top 0.75% predicted probabilities were classified as moderate to severe fracture.ResultsOur model yielded a sensitivity of 59.8%, a PPV of 91.2%, and an F1 score of 0.72. The areas under the receiver operating characteristic curve (AUC-ROC) and the precision-recall curve were 0.99 and 0.82, respectively.ConclusionOur model classified vertebral bodies with an AUC-ROC of 0.99, providing a critical component for our future automated opportunistic screening tool. This could lead to earlier detection and treatment of OCFs.

To evaluate the effectiveness of the S1 vertebral bone quality (VBQ) score in assessing bone quality among patients with vertebral fragility fractures (VFF). Additionally, whether the combination of S1 VBQ and Hounsfield unit (HU) values improves the predictive accuracy of VFF. Using lumbar noncontrast computed tomography and T1-weighted magnetic resonance imaging, we measured L1 HU values, S1 VBQ, and L1–L4 VBQ. To assess their predictive performance for VFF, we constructed receiver operating characteristic curves. We also compared the diagnostic efficacy of HU values with that of S1 VBQ and L1–-L4 VBQ values for the joint diagnosis of VFF. The Delong test was used to compare the value of individual or combined predictions of VFF. In comparison to the nonfracture group, all patients exhibited markedly elevated S1 VBQ and L1–-L4 VBQ and notably reduced HU values (P < 0.001). Multivariate analysis revealed that elevated S1 VBQ, increased L1–-L4 VBQ, and decreased HU values independently correlated with VFF development. The areas under the curve for VFF prediction were 0.806 for S1 VBQ, 0.799 for L1–-L4 VBQ, and 0.820 for HU values. According to the Delong test, the combination of HU values with S1 VBQ/L1–-L4 VBQ significantly improved the diagnostic accuracy. The simplified S1 VBQ is a valuable tool for predicting the occurrence of VFF and can be used as an alternative to the L1–-L4 VBQ. In addition, the combination of S1 VBQ and HU values can significantly improve the predictive value of VFF. [ABSTRACT FROM AUTHOR]

• Real and imagined challenges have limited pediatric lipid screening. • Implementation science provides opportunities to shrink screening gaps in care. • Creative and collaborative efforts empower partnerships to improve screening. Recent guidance by the United States Preventive Services Task Force has renewed the debate surrounding the benefits of pediatric lipid screening. This commentary reviews the evolution of the pediatric lipid screening recommendations in the United States, followed by an exploration of real and imagined challenges that prevent optimal cholesterol screening rates in children. Real challenges substantively prevent the uptake of these guidelines into practice; imagined challenges, such as identifying the best age to screen, are often context-dependent and can also be surmounted. Experiences from other countries identify potential facilitators to improving screening and additional barriers. Implementation science provides guidance on overcoming the real barriers, translating evidence-based recommendations into clinical practice, and informing the next wave of solutions to overcome these challenges. [ABSTRACT FROM AUTHOR]

Background: Screening of high-risk patients is advocated to achieve early detection and treatment of clinical atrial fibrillation (AF). The Dutch-GERAF study will address two major issues. Firstly, the effectiveness and feasibility of an opportunistic screening strategy for clinical AF will be assessed in frail older patients and, secondly, observational data will be gathered regarding the efficacy and safety of oral anticoagulation (OAC). Methods: This is a multicentre study on opportunistic screening of geriatric patients for clinical AF using a smartphone photoplethysmography (PPG) application. Inclusion criteria are age ≥ 65 years and the ability to perform at least three PPG recordings within 6 months. Exclusion criteria are the presence of a cardiac implantable device, advanced dementia or a severe tremor. The PPG application records patients' pulse at their fingertip and determines the likelihood of clinical AF. If clinical AF is suspected after a positive PPG recording, a confirmatory electrocardiogram is performed. Patients undergo a comprehensive geriatric assessment and a frailty index is calculated. Risk scores for major bleeding (MB) are applied. Standard laboratory testing and additional laboratory analyses are performed to determine the ABC-bleeding risk score. Follow-up data will be collected at 6 months, 12 months and 3 years on the incidence of AF, MB, hospitalisation, stroke, progression of cognitive disorders and mortality. Discussion: The Dutch-GERAF study will focus on frail older patients, who are underrepresented in randomised clinical trials. It will provide insight into the effectiveness of screening for clinical AF and the efficacy and safety of OAC in this high-risk population. Trial registration: NCT05337202. [ABSTRACT FROM AUTHOR]

Background: Many individuals with hypertension remain undiagnosed. We aimed to develop a predictive model for hypertension using diagnostic codes from prevailing electronic medical records in Swedish primary care. Methods: This sex- and age-matched case-control (1:5) study included patients aged 30–65 years living in the Stockholm Region, Sweden, with a newly recorded diagnosis of hypertension during 2010–19 (cases) and individuals without a recorded hypertension diagnosis during 2010–19 (controls), in total 507,618 individuals. Patients with diagnoses of cardiovascular diseases or diabetes were excluded. A stochastic gradient boosting machine learning model was constructed using the 1,309 most registered ICD-10 codes from primary care for three years prior the hypertension diagnosis. Results: The model showed an area under the curve (95 % confidence interval) of 0.748 (0.742–0.753) for females and 0.745 (0.740–0.751) for males for predicting diagnosis of hypertension within three years. The sensitivity was 63 % and 68 %, and the specificity 76 % and 73 %, for females and males, respectively. The 25 diagnoses that contributed the most to the model for females and males all exhibited a normalized relative influence >1 %. The codes contributing most to the model, all with an odds ratio of marginal effects >1 for both sexes, were dyslipidaemia, obesity, and encountering health services in other circumstances. Conclusions: This machine learning model, using prevailing recorded diagnoses within primary health care, may contribute to the identification of patients at risk of unrecognized hypertension. The added value of this predictive model beyond information of blood pressure warrants further study.

Abstract Background The incidence of breast cancer among Chinese women has gradually increased in recent years. This study aims to analyze the situation of breast cancer screening programs in China and compare the cancer detection rates (CDRs), early-stage cancer detection rates (ECDRs), and the proportions of early-stage cancer among different programs. Methods We conducted a systematic review and meta-analysis of studies in multiple literature databases. Studies that were published between January 1, 2010 and June 30, 2023 were retrieved. A random effects model was employed to pool the single group rate, and subgroup analyses were carried out based on screening model, time, process, age, population, and follow-up method. Results A total of 35 studies, including 47 databases, satisfied the inclusion criteria. Compared with opportunistic screening, the CDR (1.32‰, 95% CI: 1.10‰–1.56‰) and the ECDR (0.82‰, 95% CI: 0.66‰–0.99‰) were lower for population screening, but the proportion of early-stage breast cancer (80.17%, 95% CI: 71.40%–87.83%) was higher. In subgroup analysis, the CDR of population screening was higher in the urban group (2.28‰, 95% CI: 1.70‰–2.94‰), in the breast ultrasonography (BUS) in parallel with mammography (MAM) group (3.29‰, 95% CI: 2.48‰–4.21‰), and in the second screening follow-up group (2.47‰, 95% CI: 1.64‰–3.47‰), and the proportion of early-stage breast cancer was 85.70% (95% CI: 68.73%–97.29%), 88.18% (95% CI: 84.53%–91.46%), and 90.05% (95% CI: 84.07%–94.95%), respectively. Conclusion There were significant differences between opportunistic and population screening programs. The results of these population screening studies were influenced by the screening process, age, population, and follow-up method. In the future, China should carry out more high-quality and systematic population-based screening programs to improve screening coverage and service.

Abstract Background The Pharmacy Diabetes Screening Trial (PDST) evaluated three approaches to screening for undiagnosed type 2 diabetes mellitus (T2DM) in community pharmacy: (1) paper-based risk assessment (AUSDRISK) alone; and AUSDRISK followed by a point of care test if AUSDRISK ≥ 12; with either (2) HbA1c; or (3) small capillary blood glucose Test (scBGT). This paper reports the perspectives and experiences of the pharmacy screening service of two key stakeholder groups: screening participants and general practitioners (GPs). Methods All referred participants (n = 2242) received an online survey to determine the outcome of the referral, as well as their level of satisfaction with the service. In addition, a random sample of 2,989 (20%) of non-referred participants were surveyed to determine their overall experience and level of satisfaction with the service. GPs to whom participants were referred were contacted to establish if, since the date of the screening service, their patient had (1) been to see them; (2) had further tests performed (FBG, RBG, OGTT, HbA1c); or (3) been diagnosed with diabetes or prediabetes. Descriptive statistics were reported for quantitative data. Factors associated with visiting the GP following screening were assessed using multivariable logistic regression. Qualitative data were analysed using content analysis. Results Response rates 16% (n = 369) and 17% (n = 520) were achieved for the three-month referred and non-referred participant surveys, respectively. Over 90% of respondents were very positive about the screening service (n = 784/853) and would recommend it to a family member or friend (n = 784/853). Participants also reported making significant improvements in diet and exercise, because of the screening. Among referred respondents, those who received a POC test were twice as likely to visit their GP compared to those who received a risk assessment only (OR 2.11 95% CI 1.46–3.06). GPs (15.8% response rate, n = 57/361) indicated that the referral worked well and that recommendations for follow-up care by the pharmacist were appropriate. Conclusion Opportunistic screening of individuals during routine encounters with the community pharmacy in a previously undiagnosed population has been shown to foster positive engagement with consumers and GPs, which may assist in reducing the burden of T2DM on the individual and the community.

Abstract Background Colorectal cancer (CRC) screening faces two major challenges: insufficient screening coverage and poor adherence. A smartphone applet named “Early Screening Assistant (ESA)” was developed to create an online risk-assessment and fecal occult blood test (FOBT) at home. This retrospective study was designed to evaluate whether the new CRC screening strategy can improve the colonoscopy participation rate (PR) and lesion detection rate (DR). Methods In total, 6194 individuals who accepted normal health examinations and CRC screening based on the ESA from June 2020 to May 2022 were assigned to the ESA group. Accordingly, 7923 inhabitants who only accepted normal health examinations were assigned to the control group. The colonoscopy PR and neoplastic lesion DR were then compared between the two groups. Results Overall, a higher proportion of subjects in the ESA group (285 of 6194 [4.6%]) completed colonoscopy than in the control group (126 of 7923, [1.6%]), p < 0.01). The neoplastic lesion DR also significantly increased in the ESA group (76 of 6194 [1.22%]) compared with the control group (15 of 7923 [0.19%]) (p < 0.01). The adjusted diagnostic sensitivity and specificity of the “Online assessment + FOBT at home” were 41.5% and 62.6% for neoplastic lesions, respectively. Conclusions This retrospective cohort study confirmed that the new CRC screening strategy based on the “Online assessment + FOBT at home” can improve colonoscopy participation and the neoplastic lesion detection rate and may represent a promising screening strategy for CRC. Trial registration This study was registered in China Clinical Trial Registry ( https://www.chictr.org.cn ) on 29/09/2022. Registration number: ChiCTR2200064186.

Background: The incidence of breast cancer among Chinese women has gradually increased in recent years. This study aims to analyze the situation of breast cancer screening programs in China and compare the cancer detection rates (CDRs), early-stage cancer detection rates (ECDRs), and the proportions of early-stage cancer among different programs. Methods: We conducted a systematic review and meta-analysis of studies in multiple literature databases. Studies that were published between January 1, 2010 and June 30, 2023 were retrieved. A random effects model was employed to pool the single group rate, and subgroup analyses were carried out based on screening model, time, process, age, population, and follow-up method. Results: A total of 35 studies, including 47 databases, satisfied the inclusion criteria. Compared with opportunistic screening, the CDR (1.32‰, 95% CI: 1.10‰–1.56‰) and the ECDR (0.82‰, 95% CI: 0.66‰–0.99‰) were lower for population screening, but the proportion of early-stage breast cancer (80.17%, 95% CI: 71.40%–87.83%) was higher. In subgroup analysis, the CDR of population screening was higher in the urban group (2.28‰, 95% CI: 1.70‰–2.94‰), in the breast ultrasonography (BUS) in parallel with mammography (MAM) group (3.29‰, 95% CI: 2.48‰–4.21‰), and in the second screening follow-up group (2.47‰, 95% CI: 1.64‰–3.47‰), and the proportion of early-stage breast cancer was 85.70% (95% CI: 68.73%–97.29%), 88.18% (95% CI: 84.53%–91.46%), and 90.05% (95% CI: 84.07%–94.95%), respectively. Conclusion: There were significant differences between opportunistic and population screening programs. The results of these population screening studies were influenced by the screening process, age, population, and follow-up method. In the future, China should carry out more high-quality and systematic population-based screening programs to improve screening coverage and service. [ABSTRACT FROM AUTHOR]

Background: Certain adipose tissue depots infer higher cardiometabolic risk than body mass index (BMI). Purpose: To assess breast adipose tissue (BrAT) attenuation as a novel imaging biomarker for cardiometabolic risk. Material and Methods: We studied 151 women (mean age = 56 ± 1 years) across the weight spectrum. BrAT attenuation, abdominal adipose tissue cross-sectional areas (CSA), and attenuation were quantified using non-contrast computed tomography (CT) scans. Cardiometabolic risk factors were assessed from medical records. Results: BrAT attenuation was lower in obese women compared to lean women. BrAT attenuation was inversely associated with BMI, waist circumference, abdominal fat CSA, fasting glucose, and triglycerides (P ≤ 0.02), and positively associated with abdominal adipose tissue attenuation (P < 0.001). BrAT attenuation had a sensitivity of 90% but a specificity of only 35% in detecting the metabolic syndrome (area under the curve = 0.63). Conclusion: BrAT attenuation is associated with cardiometabolic risk markers and could serve as an imaging biomarker for opportunistic risk assessment in patients undergoing CT examination of the chest. [ABSTRACT FROM AUTHOR]

Lumbar vertebral bone attenuation, measured in Hounsfield units (HU) can indirectly indicate the bone mineral density (BMD). The aim of this study is to determine the optimal HU threshold on abdominal computed tomography (CT) scans to detect osteopathy in patients with chronic pancreatitis (CP). This cross-sectional study included patients with CP who underwent CT scans to measure HU at L1 to L4 vertebrae. The mean lumbar vertebral attenuation of female renal transplant donors, aged 20–30 years was utilized to calculate the T-scoreHU of all patients at each vertebral level. Receiver operator characteristic analysis was used to determine the HU and T-scoreHU for diagnosis of osteopathy in patients with CP. Dual-energy X-ray absorptiometry value was used to categorize osteopenia and osteoporosis. A total of 175 patients (mean age, 34.5 ± 10.9 years; 72 % males) and 33 female renal transplant donors (mean age, 28 ± 2.4 years) were included. A threshold HU value 212 or T scoreHU of −1.80 at L1 vertebra was found to have a 78 % sensitivity and 70 % specificity for differentiating between osteoporosis and non-osteoporosis (osteopenia and normal BMD). Similarly, a threshold HU value of 254 or a T-scoreHU of −0.46 at L1 vertebra had 78 % sensitivity and 71 % specificity for distinguishing between normal and low BMD (osteoporosis and osteopenia). Abdominal CT images, which are routinely performed in chronic pancreatitis, can be used for opportunistic screening of osteoporosis and osteopenia without additional cost or radiation exposure. [ABSTRACT FROM AUTHOR]

Background: While various interventions have been conducted to decrease cervical cancer's burden in Nigeria, no study has examined the trends in cervical cancer screening uptake over time. The present study sought to fill this gap in knowledge using data collected at Jos University Teaching Hospital (JUTH) in Nigeria. Methods: Data collected continuously between 2006 and 2016 were analyzed to identify trends in screening uptake, changes in risk factors for cervical cancer, and to identify factors for women screened at Jos University Teaching Hospital (JUTH) in Jos, Nigeria. Categorical analyses and logistic regression models were used to describe patient characteristics by year, and to identify factors associated with repeated screening uptake. Results: A total of 14,088 women who were screened between 2006 and 2016 were included in the database; 2,800 women had more than one screening visit. Overall, screening uptake differed significantly by year. On average women were first screened at age 38. About 2% of women screened were women living with HIV. Most women (86%) had normal pap smear at first screening, with the greatest decreased risk of abnormalities observed between 2011 and 2014. Odds of a follow-up screening after a normal result decreased significantly between 2008 and 2016 compared to women screened in 2006 and 2007. Finally, women living with HIV had increased odds of follow-up screening after having a normal pap smear. Conclusions: These findings contribute to our understanding of the potential social and health system barriers to cervical cancer control in Nigeria. The findings may assist policy makers to design interventions to increase access and compliance to recommended screening schedules in this vulnerable population. [ABSTRACT FROM AUTHOR]

Background: Colorectal cancer (CRC) screening faces two major challenges: insufficient screening coverage and poor adherence. A smartphone applet named "Early Screening Assistant (ESA)" was developed to create an online risk-assessment and fecal occult blood test (FOBT) at home. This retrospective study was designed to evaluate whether the new CRC screening strategy can improve the colonoscopy participation rate (PR) and lesion detection rate (DR). Methods: In total, 6194 individuals who accepted normal health examinations and CRC screening based on the ESA from June 2020 to May 2022 were assigned to the ESA group. Accordingly, 7923 inhabitants who only accepted normal health examinations were assigned to the control group. The colonoscopy PR and neoplastic lesion DR were then compared between the two groups. Results: Overall, a higher proportion of subjects in the ESA group (285 of 6194 [4.6%]) completed colonoscopy than in the control group (126 of 7923, [1.6%]), p < 0.01). The neoplastic lesion DR also significantly increased in the ESA group (76 of 6194 [1.22%]) compared with the control group (15 of 7923 [0.19%]) (p < 0.01). The adjusted diagnostic sensitivity and specificity of the "Online assessment + FOBT at home" were 41.5% and 62.6% for neoplastic lesions, respectively. Conclusions: This retrospective cohort study confirmed that the new CRC screening strategy based on the "Online assessment + FOBT at home" can improve colonoscopy participation and the neoplastic lesion detection rate and may represent a promising screening strategy for CRC. Trial registration: This study was registered in China Clinical Trial Registry (https://www.chictr.org.cn) on 29/09/2022. Registration number: ChiCTR2200064186. [ABSTRACT FROM AUTHOR]

Summary: Vertebral bone quality (VBQ) score is an opportunistic measure of bone mineral density using routine preoperative MRI in spine surgery. VBQ score positively correlates with age and is reproducible across serial scans. However, extrinsic factors, including MRI machine and protocol, affect the VBQ score and must be standardized. Purpose: The purposes of this study were to determine whether VBQ score increased with age and whether VBQ remained consistent across serial MRI studies obtained within 3 months. Methods: This retrospective study evaluated 136 patients, age 20–69, who received two T1-weighted lumbar MRI within 3 months of each other between January 2011 and December 2021. VBQ(L1-4) score was calculated as the quotient of L1–L4 signal intensity (SI) and L3 cerebral spinal fluid (CSF) SI. VBQ(L1) score was calculated as the quotient of L1 SI and L1 CSF SI. Regression analysis was performed to determine correlation of VBQ(L1-4) score with age. Coefficient of variation (CV) was used to determine reproducibility between VBQ(L1-4) scores from serial MRI scans. Results: One hundred thirty-six patients (mean ± SD age 44.9 ± 12.5 years; 53.7% female) were included in this study. Extrinsic factors affecting the VBQ score included patient age, MRI relaxation time, and specific MRI machine. When controlling for MRI relaxation/echo time, the VBQ(L1-4) score was positively correlated with age and had excellent reproducibility in serial MRI with CV of 0.169. There was excellent agreement (ICC > 0.9) of VBQ scores derived from the two formulas, VBQ(L1) and VBQ(L1-4). Conclusion: Extrinsic factors, including MRI technical factors and age, can impact the VBQ(L1-4) score and must be considered when using this tool to estimate bone mineral density (BMD). VBQ(L1-4) score was positively correlated with age. Reproducibility of the VBQ(L1-4) score across serial MRI is excellent especially when controlling for technical factors, supporting use of the VBQ score in estimating BMD. The VBQ(L1) score was a reliable alternative to the VBQ(L1-4) score. [ABSTRACT FROM AUTHOR]

Simple Summary: In Sri Lanka, the authors are encouraging a cost-effective way to reduce the number of people developing oral cancer, which is a common cancer among men. The authors wanted to find out how well dentists can find signs of oral cancer and potential problems in their patients, and they did this by asking dentists questions in an online survey. Surprisingly, most of the dentists thought it would be better to only check people who are at a higher risk for oral cancer instead of checking everyone. Only about two thirds of the dentists regularly look for signs of oral cancer when people come for dental check-ups. The authors found that these dentists, on average, had noticed about 34 patients with concerning signs in the past year. Almost all the dentists believed that they needed more training to spot these problems. So, while opportunistic screening at the dentist's office is good, dental professionals also need to focus on other ways to prevent oral cancer, like checking high-risk groups and giving dentists more confidence to spot potential issues. In Sri Lanka, opportunistic screening is encouraged as a cost-effective tool to bring down the prevalence of oral cancer, which is the most common cancer among males. The objectives of the study were to determine the practices, attitudes, and level of competency of dental surgeons regarding the early detection of oral cancer and oral potentially malignant disorders (OPMDs) through opportunistic screening. A prospective study was conducted online via a Google form using a pretested, self-administered questionnaire of 22 close-ended questions and 3 open-ended questions. Out of the 137 dental surgeons who responded, 88% (121/137) of the participants believed that screening high-risk target groups would be more effective in the early detection of oral cancer rather than opportunistic screening. Only 64% (88/137) of the participants frequently check for oral cancer and OPMDs when patients visit for dental treatment. Participants recalled an average of 34 patients (4628/137) with clinically suspicious lesions being diagnosed during examination at general dental practice during the past year, and 98% (134/137) of the participants believed that they should receive additional training in order to identify and diagnose clinically suspicious OPMDs and oral cancer. Opportunistic screening in general dental practice as an oral-cancer prevention strategy is appreciable, but due emphasis should be given to other prevention strategies such as population screening and screening high-risk target groups. The level of confidence of general dental practitioners in the early detection of oral cancer has to be raised in order to achieve higher standards in oral cancer prevention through opportunistic screening. [ABSTRACT FROM AUTHOR]

Background: The Pharmacy Diabetes Screening Trial (PDST) evaluated three approaches to screening for undiagnosed type 2 diabetes mellitus (T2DM) in community pharmacy: (1) paper-based risk assessment (AUSDRISK) alone; and AUSDRISK followed by a point of care test if AUSDRISK ≥ 12; with either (2) HbA1c; or (3) small capillary blood glucose Test (scBGT). This paper reports the perspectives and experiences of the pharmacy screening service of two key stakeholder groups: screening participants and general practitioners (GPs). Methods: All referred participants (n = 2242) received an online survey to determine the outcome of the referral, as well as their level of satisfaction with the service. In addition, a random sample of 2,989 (20%) of non-referred participants were surveyed to determine their overall experience and level of satisfaction with the service. GPs to whom participants were referred were contacted to establish if, since the date of the screening service, their patient had (1) been to see them; (2) had further tests performed (FBG, RBG, OGTT, HbA1c); or (3) been diagnosed with diabetes or prediabetes. Descriptive statistics were reported for quantitative data. Factors associated with visiting the GP following screening were assessed using multivariable logistic regression. Qualitative data were analysed using content analysis. Results: Response rates 16% (n = 369) and 17% (n = 520) were achieved for the three-month referred and non-referred participant surveys, respectively. Over 90% of respondents were very positive about the screening service (n = 784/853) and would recommend it to a family member or friend (n = 784/853). Participants also reported making significant improvements in diet and exercise, because of the screening. Among referred respondents, those who received a POC test were twice as likely to visit their GP compared to those who received a risk assessment only (OR 2.11 95% CI 1.46–3.06). GPs (15.8% response rate, n = 57/361) indicated that the referral worked well and that recommendations for follow-up care by the pharmacist were appropriate. Conclusion: Opportunistic screening of individuals during routine encounters with the community pharmacy in a previously undiagnosed population has been shown to foster positive engagement with consumers and GPs, which may assist in reducing the burden of T2DM on the individual and the community. [ABSTRACT FROM AUTHOR]

Purpose: One or more vertebrae are sometimes excluded from dual-energy X-ray absorptiometry (DXA) analysis if the bone mineral density (BMD) T-score estimates are not consistent with the other lumbar vertebrae BMD T-score estimates. The goal of this study was to build a machine learning framework to identify which vertebrae would be excluded from DXA analysis based on the computed tomography (CT) attenuation of the vertebrae. Methods: Retrospective review of 995 patients (69.0% female) aged 50 years or greater with CT scans of the abdomen/pelvis and DXA within 1 year of each other. Volumetric semi-automated segmentation of each vertebral body was performed using 3D-Slicer to obtain the CT attenuation of each vertebra. Radiomic features based on the CT attenuation of the lumbar vertebrae were created. The data were randomly split into training/validation (90%) and test datasets (10%). We used two multivariate machine learning models: a support vector machine (SVM) and a neural net (NN) to predict which vertebra(e) were excluded from DXA analysis. Results: L1, L2, L3, and L4 were excluded from DXA in 8.7% (87/995), 9.9% (99/995), 32.3% (321/995), and 42.6% (424/995) patients, respectively. The SVM had a higher area under the curve (AUC = 0.803) than the NN (AUC = 0.589) for predicting whether L1 would be excluded from DXA analysis (P = 0.015) in the test dataset. The SVM was better than the NN for predicting whether L2 (AUC = 0.757 compared to AUC = 0.478), L3 (AUC = 0.699 compared to AUC = 0.555), or L4 (AUC = 0.751 compared to AUC = 0.639) were excluded from DXA analysis. Conclusions: Machine learning algorithms could be used to identify which lumbar vertebrae would be excluded from DXA analysis and should not be used for opportunistic CT screening analysis. The SVM was better than the NN for identifying which lumbar vertebra should not be used for opportunistic CT screening analysis. [ABSTRACT FROM AUTHOR]

Purpose: This study aimed to investigate the performance of deep learning algorithms in the opportunistic screening for primary angle-closure disease (PACD) using combined anterior segment parameters. Methods: This was an observational, cross-sectional hospital-based study. Patients with PACD and healthy controls who underwent comprehensive eye examinations, including gonioscopy and anterior segment optical coherence tomography (ASOCT) examinations under both light and dark conditions, were consecutively enrolled from the Department of Ophthalmology at the Beijing Tongren Hospital between November 2020 and June 2022. The anterior chamber, anterior chamber angle, iris, and lens parameters were assessed using ASOCT. To build the prediction models, backward logistic regression was utilized to select the variables to discriminate patients with PACD from normal participants, and the area under the receiver operating characteristic curve was used to evaluate the efficacy of the opportunistic screening. Results: The data from 199 patients (199 eyes) were included in the final analysis and divided into two groups: PACD (109 eyes) and controls (90 eyes). Angle opening distance at 500 μm, anterior chamber area, and iris curvature measured in the light condition were included in the final prediction models. The area under the receiver operating characteristic curve was 0.968, with a sensitivity of 91.74 % and a specificity of 91.11 %. Conclusion: ASOCT-based algorithms showed excellent diagnostic performance in the opportunistic screening for PACD. These results provide a promising basis for future research on the development of an angle-closure probability scoring system for PACD screening.

Aim to study the prevalence of paracardial fat as a risk factor for cardiovascular diseases in Moscow population using an automated analysis of the results of radiological examinations. Material and methods. The research was designed as descriptive, retrospective epidemiological study. The results of chest computed tomography of 113,408 patients served as the study data. The data was analyzed by AI services in an automated mode for the presence of paracardial fat and calculation of its volume. Results. The paracardial fat was detected in 66.5% of the examined persons. The proportion of men was 45.7%, women 54.3% (p0.001). The volume of paracardial fat fluctuated in the range from 1.0 to 1517.0 ml; the average value was 282.1 ml. The average volume of paracardial fat in men (326.0 ml) was significantly larger than in women (244.7 ml) in each age group. The clinically significant volume of paracardial fat (200 ml) was detected in 33,081 individuals (in 64.0% of people having this risk factor). The risk factor was clinically significant in 71.1% of men and in 57.9% of women (p0.001). Conclusion. The prevalence of paracardial fat in Moscow population was 5.97 per 1000 individuals. A clinically significant volume of paracardial fat was most often found in both sexes in the elderly (78.7%) and senile age groups (78.2%). Each 5 years of age increased the probability of this risk factor incidence by 1.282 times in general; and the risk of developing its clinical form by 2.981 times in particular.

Abstract Background South Africa grapples with a substantial burden of non-communicable diseases (NCDs), particularly type 2 diabetes (diabetes) and hypertension. However, these conditions are often underdiagnosed and poorly managed, further exacerbated by the strained primary healthcare (PHC) system and the disruptive impact of the COVID-19 pandemic. Integrating NCD screening with large-scale healthcare initiatives, such as COVID-19 vaccination campaigns, offers a potential solution, especially in low- and middle-income countries (LMICs). We investigated the feasibility and effectiveness of this integration. Methods A prospective cohort study was conducted at four government health facilities in Johannesburg, South Africa. NCD screening was incorporated into the COVID-19 vaccination campaign. Participants underwent COVID-19 rapid tests, blood glucose checks, blood pressure assessments, and anthropometric measurements. Those with elevated blood glucose or blood pressure values received referrals for diagnostic confirmation at local PHC centers. Results Among 1,376 participants screened, the overall diabetes prevalence was 4.1%, combining previously diagnosed cases and newly identified elevated blood glucose levels. Similarly, the hypertension prevalence was 19.4%, comprising pre-existing diagnoses and newly detected elevated blood pressure cases. Notably, 46.1% of participants displayed waist circumferences indicative of metabolic syndrome, more prevalent among females. Impressively, 7.8% of all participants screened were potentially newly diagnosed with diabetes or hypertension. Approximately 50% of individuals with elevated blood glucose or blood pressure successfully linked to follow-up care within four weeks. Conclusion Our study underscores the value of utilizing even brief healthcare interactions as opportunities for screening additional health conditions, thereby aiding the identification of previously undiagnosed cases. Integrating NCD screenings into routine healthcare visits holds promise, especially in resource-constrained settings. Nonetheless, concerted efforts to strengthen care linkage are crucial for holistic NCD management and control. These findings provide actionable insights for addressing the NCD challenge and improving healthcare delivery in LMICs.

Background: Bladder cancer is the sixth most common malignancy in the United States (US). Despite its high prevalence and the significant potential benefits of early detection, no reliable, cost-effective screening algorithm exists for asymptomatic patients at risk. Nonetheless, reports of incidentally identified early bladder cancer on CT/MRI scans performed for other indications are emerging in the literature. This represents a new opportunity for early detection, with over 80 million CT scans performed in the US yearly, 40% of which are abdominopelvic CTs. This investigation aims to define the imaging features of early bladder cancer, with the mission of facilitating early diagnosis. Methods: Following IRB approval with a waiver of informed consent, a retrospective review was performed, identifying 624 patients with non-muscle-invasive bladder cancer diagnosed at Johns Hopkins Hospital between 2000 and 2019. Of these patients, 99 patients underwent pelvic CT within the 5 years preceding pathologic diagnosis. These imaging studies were reviewed retrospectively to evaluate for the presence and features of any focal bladder wall abnormality. Results: Median age at the time of pathologic diagnosis was 70 years (range: 51–88 years), and 82% (81/99) of patients were male. A total of 226 CT studies were reviewed. The number of studies per patient ranged from 1 to 33. Median time interval between all available imaging and pathologic diagnosis was 14 months. A total of 62% (141/226) of the scans reviewed were performed for indications other than suspected urinary tract cancer (UTC). A bladder wall mass was visualized in 67% (66/99) of patients and on 35% (78/226) of scans performed before diagnosis. The majority (84%, 67/80) of masses were intraluminal. Mean transverse long- and short-axis measurements were 24 mm and 17 mm, respectively, with long dimension measurements ranging between 5 and 59 mm. Conclusions: Early bladder cancer was visualized on CT preceding pathologic diagnosis in more than 2/3 of patients, and the majority of scans were performed for indications other than suspected urinary tract cancer/UTC symptoms. These results suggest that cross-sectional imaging performed for other indications can serve as a resource for opportunistic bladder cancer screening, particularly in high-risk patients.

Opportunistic osteoporosis screening using deep learning (DL) analysis of low-dose chest CT (LDCT) scans is a potentially promising approach for the early diagnosis of this condition. We explored bone mineral density (BMD) profiles across all adult ages and prevalence of osteoporosis using LDCT with DL in a Korean population. This retrospective study included 1915 participants from two hospitals who underwent LDCT during general health checkups between 2018 and 2021. Trabecular volumetric BMD of L1-2 was automatically calculated using DL and categorized according to the American College of Radiology quantitative computed tomography diagnostic criteria. BMD decreased with age in both men and women. Women had a higher peak BMD in their twenties, but lower BMD than men after 50. Among adults aged 50 and older, the prevalence of osteoporosis and osteopenia was 26.3% and 42.0%, respectively. Osteoporosis prevalence was 18.0% in men and 34.9% in women, increasing with age. Compared to previous data obtained using dual-energy X-ray absorptiometry, the prevalence of osteoporosis, particularly in men, was more than double. The automated opportunistic BMD measurements using LDCT can effectively predict osteoporosis for opportunistic screening and identify high-risk patients. Patients undergoing lung cancer screening may especially profit from this procedure requiring no additional imaging or radiation exposure.

The opportunistic use of radiological examinations for disease detection can potentially enable timely management. We assessed if an index created by an AI software to quantify chest radiography (CXR) findings associated with heart failure (HF) could distinguish between patients who would develop HF or not within a year of the examination. Our multicenter retrospective study included patients who underwent CXR without an HF diagnosis. We included 1117 patients (age 67.6 ± 13 years; m:f 487:630) that underwent CXR. A total of 413 patients had the CXR image taken within one year of their HF diagnosis. The rest (n = 704) were patients without an HF diagnosis after the examination date. All CXR images were processed with the model (qXR-HF, Qure.AI) to obtain information on cardiac silhouette, pleural effusion, and the index. We calculated the accuracy, sensitivity, specificity, and area under the curve (AUC) of the index to distinguish patients who developed HF within a year of the CXR and those who did not. We report an AUC of 0.798 (95%CI 0.77–0.82), accuracy of 0.73, sensitivity of 0.81, and specificity of 0.68 for the overall AI performance. AI AUCs by lead time to diagnosis (

Abstract Background Many people were found with pulmonary nodules during physical examinations. It is of great practical significance to discriminate benign and malignant nodules by using data mining technology. Methods The subjects' demographic data, baseline examination results, and annual follow‐up low‐dose spiral computerized tomography (LDCT) results were recorded. The findings from annual physical examinations of positive nodules, including highly suspicious nodules and clinically tentative benign nodules, was analyzed. The extreme gradient boosting (XGBoost) model was constructed and the Grid Search CV method was used to select the super parameters. External unit data were used as an external validation set to evaluate the generalization performance of the model. Results A total of 135,503 physical examinees were enrolled. Baseline testing found that 27,636 (20.40%) participants had clinically tentative benign nodules and 611 (0.45%) participants had highly suspicious nodules. The proportion of highly suspicious nodules in participants with negative baseline was about 0.12%–0.46%, which was lower than the baseline level except the follow‐up of >5 years. In the 27,636 participants with clinically tentative benign nodules, only in the first year of LDCT re‐examination was the proportion of highly suspicious nodules (1.40%) significantly greater than that of baseline screening (0.45%) (p 0.05). Furthermore, 322 cases with benign nodules and 196 patients with malignant nodules confirmed by surgery and pathology were compared. A model and the top 15 most important clinical variables were determined by XGBoost algorithm. The area under the curve (AUC) of the model was 0.76 [95% CI: 0.67–0.84], and the accuracy was 0.75. The sensitivity and specificity of the model under this threshold were 0.78 and 0.73, respectively. In the validation of model using external data, the AUC was 0.87 and the accuracy was 0.80. The sensitivity and specificity were 0.83 and 0.77, respectively. Conclusions It is important that pulmonary nodules could be more accurately identified at the first LDCT examination. A model with 15 variables which are routinely measured in the clinic could be helpful to distinguish benign and malignant nodules. It could help the radiological team issue a more accurate report; and it may guide the clinical team regarding LDCT follow‐up.

Background. Chest low-dose computed tomography (LDCT) is used in lung cancer screening, but the study data can also be used to assess the liver condition, including the hepatic steatosis (HS) detection. However, radiologists often do not pay attention to liver changes due to the focus on the chest. Objective: to determine the prevalence of HS during chest LDST among lung cancer screening patients using a computer vision (CV) system. Material and methods. For a retrospective study, 300 chest LDCT were taken from Moscow lung cancer screening in 2018–2020. Hepatic attenuation analysis was performed by CV, the values < 40 HU were considered as HS. The text protocols of CT scans were analysed and compared with decreased hepatic attenuation revealed by CV system. Results. 291 patients were analysed, the median age for the sample was 65 [61; 70] years. The mean hepatic attenuation was 55.6 ± 14.8 HU. Hepatic attenuation < 40 HU was found in 13% patients (23 (16.1%) males and 14 (9.5%) females), a statistically significant difference was revealed among these patients (p = 0.04). Six (4.2%) males and 4 (2.7%) females were at risk for HS (40–45 HU). The examination of text protocols showed no pathology discovered in all cases. Conclusion. The prevalence of CT signs for HS among the lung cancer screening group in Moscow was 13%. The absence of HS in text protocols highlights the importance of using CV systems in the routine practice.

Background: South Africa grapples with a substantial burden of non-communicable diseases (NCDs), particularly type 2 diabetes (diabetes) and hypertension. However, these conditions are often underdiagnosed and poorly managed, further exacerbated by the strained primary healthcare (PHC) system and the disruptive impact of the COVID-19 pandemic. Integrating NCD screening with large-scale healthcare initiatives, such as COVID-19 vaccination campaigns, offers a potential solution, especially in low- and middle-income countries (LMICs). We investigated the feasibility and effectiveness of this integration. Methods: A prospective cohort study was conducted at four government health facilities in Johannesburg, South Africa. NCD screening was incorporated into the COVID-19 vaccination campaign. Participants underwent COVID-19 rapid tests, blood glucose checks, blood pressure assessments, and anthropometric measurements. Those with elevated blood glucose or blood pressure values received referrals for diagnostic confirmation at local PHC centers. Results: Among 1,376 participants screened, the overall diabetes prevalence was 4.1%, combining previously diagnosed cases and newly identified elevated blood glucose levels. Similarly, the hypertension prevalence was 19.4%, comprising pre-existing diagnoses and newly detected elevated blood pressure cases. Notably, 46.1% of participants displayed waist circumferences indicative of metabolic syndrome, more prevalent among females. Impressively, 7.8% of all participants screened were potentially newly diagnosed with diabetes or hypertension. Approximately 50% of individuals with elevated blood glucose or blood pressure successfully linked to follow-up care within four weeks. Conclusion: Our study underscores the value of utilizing even brief healthcare interactions as opportunities for screening additional health conditions, thereby aiding the identification of previously undiagnosed cases. Integrating NCD screenings into routine healthcare visits holds promise, especially in resource-constrained settings. Nonetheless, concerted efforts to strengthen care linkage are crucial for holistic NCD management and control. These findings provide actionable insights for addressing the NCD challenge and improving healthcare delivery in LMICs. [ABSTRACT FROM AUTHOR]

Purpose: To predict whether a patient has osteoporosis/osteopenia using the attenuation of trabecular bone obtained from knee computed tomography (CT) scans. Methods: Retrospective analysis of 273 patients who underwent contemporaneous knee CT scans and dual-energy X-ray absorptiometry (DXA) within 1 year. Volumetric segmentation of the trabecular bone of the distal femur, proximal tibia, patella, and proximal fibula was performed to obtain the bone CT attenuation. The data was randomly split into training/validation (78%) and test (22%) datasets and the performance in the test dataset were evaluated. The predictive properties of the CT attenuation of each bone to predict osteoporosis/osteopenia were assessed. Multivariable support vector machines (SVM) and random forest classifiers (RF) were used to predict osteoporosis/osteopenia. Results: Patients with a mean age (range) of 67.9 (50–87) years, 85% female were evaluated. Seventy-seven (28.2%) of patients had normal bone mineral density (BMD), 140 (51.3%) had osteopenia, and 56 (20.5%) had osteoporosis. The proximal tibia had the best predictive ability of all bones and a CT attenuation threshold of 96.0 Hounsfield Units (HU) had a sensitivity of.791, specificity of.706, and area under the curve (AUC) of.748. The AUC for the SVM with cubic kernel classifier (AUC =.912) was better than the RF classifier (AUC =.683, P <.001) and better than using the CT attenuation threshold of 96.0 HU at the proximal tibia (AUC =.748, P =.025). Conclusions: Opportunistic screening for osteoporosis/osteopenia can be performed using knee CT scans. Multivariable machine learning models are more predictive than the CT attenuation of a single bone. Visual Abstract [ABSTRACT FROM AUTHOR]

Purpose: This study investigated the vertebral bone quality (VBQ) score as a potential tool for opportunistic osteoporosis screening and its correlation with dual-energy X-ray absorptiometry (DXA) values. Methods: In a single-center retrospective cohort of 130 patients, VBQ and DXA measures were compared using various statistical analyses. The optimal VBQ threshold for predicting osteoporosis was determined using receiver operating characteristic (ROC) analysis. Results: VBQ exhibited a significant negative association with DXA values, suggesting that higher VBQ scores are indicative of lower bone density. Age and VBQ were significant predictors of osteoporosis, with both increasing the log-odds of the condition. An optimal VBQ threshold of 2.7 was determined, demonstrating fair discriminatory power and high negative predictive value. Conclusion: The study highlighted the potential of VBQ as a diagnostic tool for osteoporosis with high intra- and inter-observer reliability. The optimal VBQ threshold of 2.7 can aid in ruling out osteoporosis and identifying individuals for further evaluation. [ABSTRACT FROM AUTHOR]