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Abstract Introduction Most studies on body composition in kidney cancer have been conducted among patients with metastatic disease. Given that aggressive tumours can adversely impact body composition and even non‐metastatic tumours can be aggressive, we evaluated associations between pre‐surgical body composition features and tumour pathological features in patients with non‐metastatic clear cell renal cell cancer (ccRCC). Methods The Resolve Cohort consists of 1239 patients with non‐metastatic ccRCC who underwent nephrectomy at Memorial Sloan Kettering Cancer Center between 2000 and 2020. The cross‐sectional areas and radiodensities of skeletal muscle, visceral adipose, and subcutaneous adipose tissues were determined from pre‐surgical computed tomography (CT) scans at the third lumbar vertebrae using Automatica software. Pearson's correlation coefficients describe inter‐relationships among BMI and body composition variables, while odds ratios (OR) and 95% confidence intervals (CI) estimate associations between continuous body composition features (per 1‐standard deviation) and advanced stage (Stage III vs. Stages I–II) and high Fuhrman grade (Grades 3–4 vs. 1–2) from multivariable logistic regression models that considered the potential impact of biological sex, contrast enhanced CTs, and early age at onset of ccRCC. Results The cohort was predominantly male (69%), white (89%), and had a median age of 58. The proportion of patients presenting with advanced stage and high‐grade disease were 31% and 51%, respectively. In models that adjusted for demographics and all body composition variables simultaneously, decreasing skeletal muscle radiodensity (i.e., more fat infiltration) but increasing visceral adipose tissue radiodensity (i.e., more lipid depletion) were associated with advanced tumour features. Per 8.4 HU decrease in skeletal muscle radiodensity, the odds of presenting with advanced stage was 1.61 (95% CI: 1.34–1.93). Per 7.22 HU increase in visceral adipose tissue radiodensity, the odds of presenting with advanced stage was 1.45 (95% CI: 1.22–1.74). Skeletal muscle index (i.e., sarcopenia) was not associated with either tumour feature. Similar associations were observed for Fuhrman grade, a more direct marker of tumour aggressiveness. Associations did not differ by sex, contrast use, or age at onset of ccRCC. Conclusions Lipid infiltrated skeletal muscle, but lipid depleted visceral adipose tissue were independently associated with advanced tumour features in non‐metastatic ccRCC. Findings highlight the importance of evaluating the full range of body composition features simultaneously in multivariable models. Interpreting pre‐surgical CTs for body composition for patients may be a novel and non‐invasive way to identify patients with aggressive renal tumours, which is clinically relevant as renal biopsies are not routinely performed.

Recent advancements in machine learning have played a crucial role in the healthcare industry, particularly in predicting heart disease with assorted datasets. Despite the registration results indicating promising accuracy and resilience in heart disease prediction models, it is still low compared to what we expected. In this article, a new technique has been demonstrated to predict heart disease based on state-of-the-art machine learning with a focus mostly on Hyper Adaboost classifiers. We evaluate the performance of several key machine learning algorithms, including Random Forest, Extra Trees, LightGBM, Decision Tree, and Hyper Adaboost, on well-known heart disease datasets. Our results are mixed, but they suggest that the Hyper Adaboost classifier provides stronger accuracy, with performance metrics consistently exceeding 97%, much better than similar models. This research highlights the promise of newer packages under Hyper for detecting and screening individuals with heart disease early, which can offer insights into advancing methodologies in future studies as well as clinical use. Our technique exhibited strong predictive power, encouraging additional study with a larger and more comprehensive dataset for the validation of our model.

There is a need to develop user-friendly imaging tools estimating robust quantitative biomarkers (QIBs) from multiparametric (mp)MRI for clinical applications in oncology. Quantitative metrics derived from (mp)MRI can monitor and predict early responses to treatment, often prior to anatomical changes. We have developed a vendor-agnostic, flexible, and user-friendly MATLAB-based toolkit, MRI-Quantitative Analysis and Multiparametric Evaluation Routines (“MRI-QAMPER”, current release v3.0), for the estimation of quantitative metrics from dynamic contrast-enhanced (DCE) and multi-b value diffusion-weighted (DW) MR and MR relaxometry. MRI-QAMPER’s functionality includes generating numerical parametric maps from these methods reflecting tumor permeability, cellularity, and tissue morphology. MRI-QAMPER routines were validated using digital reference objects (DROs) for DCE and DW MRI, serving as initial approval stages in the National Cancer Institute Quantitative Imaging Network (NCI/QIN) software benchmark. MRI-QAMPER has participated in DCE and DW MRI Collaborative Challenge Projects (CCPs), which are key technical stages in the NCI/QIN benchmark. In a DCE CCP, QAMPER presented the best repeatability coefficient (RC = 0.56) across test–retest brain metastasis data, out of ten participating DCE software packages. In a DW CCP, QAMPER ranked among the top five (out of fourteen) tools with the highest area under the curve (AUC) for prostate cancer detection. This platform can seamlessly process mpMRI data from brain, head and neck, thyroid, prostate, pancreas, and bladder cancer. MRI-QAMPER prospectively analyzes dose de-escalation trial data for oropharyngeal cancer, which has earned it advanced NCI/QIN approval for expanded usage and applications in wider clinical trials.

IntroductionMore than 76,000 women die yearly from preeclampsia and hypertensive disorders of pregnancy. Early diagnosis and management of preeclampsia can improve outcomes for both mother and baby. In this study, we developed artificial intelligence models to detect and predict preeclampsia from electrocardiograms (ECGs) in point-of-care settings.MethodsTen-second 12-lead ECG data was obtained from two large health care settings: University of Tennessee Health Science Center (UTHSC) and Atrium Health Wake Forest Baptist (AHWFB). UTHSC data was split into 80% training and 20% holdout data. The model used a modified ResNet convolutional neural network, taking one-dimensional raw ECG signals comprising 12 channels as an input, to predict risk of preeclampsia. Sub-analyses were performed to assess the predictive accuracy for preeclampsia prediction within 30, 60, or 90 days before diagnosis.ResultsThe UTHSC cohort included 904 ECGs from 759 females (78.8% African American) with a mean ± sd age of 27.3 ± 5.0 years. The AHWFB cohort included 817 ECGs from 141 females (45.4 African American) with a mean ± sd age of 27.4 ± 5.9 years. The cross-validated ECG-AI model yielded an AUC (95% CI) of 0.85 (0.77-0.93) on UTHSC holdout data, and an AUC (95% CI) of 0.81 (0.77-0.84) on AHWFB data. The sub-analysis of different time windows before preeclampsia prediction resulted in AUCs (95% CI) of 0.92 (0.84-1.00), 0.89 (0.81-0.98) and 0.90 (0.81-0.98) when tested on ECGs 30 days, 60 days and 90 days, respectively, before diagnosis. When assessed on early onset preeclampsia (preeclampsia diagnosed at

Abstract Little is known about electrocardiogram (ECG) markers of Parkinson’s disease (PD) during the prodromal stage. The aim of the study was to build a generalizable ECG-based fully automatic artificial intelligence (AI) model to predict PD risk during the prodromal stage, up to 5 years before disease diagnosis. This case–control study included samples from Loyola University Chicago (LUC) and University of Tennessee-Methodist Le Bonheur Healthcare (MLH). Cases and controls were matched according to specific characteristics (date, age, sex and race). Clinical data were available from May, 2014 onward at LUC and from January, 2015 onward at MLH, while the ECG data were available as early as 1990 in both institutes. PD was denoted by at least two primary diagnostic codes (ICD9 332.0; ICD10 G20) at least 30 days apart. PD incidence date was defined as the earliest of first PD diagnostic code or PD-related medication prescription. ECGs obtained at least 6 months before PD incidence date were modeled to predict a subsequent diagnosis of PD within three time windows: 6 months–1 year, 6 months–3 years, and 6 months–5 years. We applied a novel deep neural network using standard 10-s 12-lead ECGs to predict PD risk at the prodromal phase. This model was compared to multiple feature engineering-based models. Subgroup analyses for sex, race and age were also performed. Our primary prediction model was a one-dimensional convolutional neural network (1D-CNN) that was built using 131 cases and 1058 controls from MLH, and externally validated on 29 cases and 165 controls from LUC. The model was trained on 90% of the MLH data, internally validated on the remaining 10% and externally validated on LUC data. The best performing model resulted in an external validation AUC of 0.67 when predicting future PD at any time between 6 months and 5 years after the ECG. Accuracy increased when restricted to ECGs obtained within 6 months to 3 years before PD diagnosis (AUC 0.69) and was highest when predicting future PD within 6 months to 1 year (AUC 0.74). The 1D-CNN model based on raw ECG data outperformed multiple models built using more standard ECG feature engineering approaches. These results demonstrate that a predictive model developed in one cohort using only raw 10-s ECGs can effectively classify individuals with prodromal PD in an independent cohort, particularly closer to disease diagnosis. Standard ECGs may help identify individuals with prodromal PD for cost-effective population-level early detection and inclusion in disease-modifying therapeutic trials.

BackgroundCardiovascular diseases contribute to premature mortality globally, resulting in substantial social and economic burdens. The Global Burden of Disease (GBD) Study reported that in 2019 alone, heart attack and strokes accounted for the deaths of 18.6 million individuals. Ischemic heart diseases, including acute myocardial infarction (AMI), accounted for 182 million disability-adjusted life years (DALYs) and it is leading cause of death worldwide.AimThe aim of this study is to present the burden of AMI in Kazakhstan and describe the outcome of hospitalized patients.MethodsThe data of 79,172 people admitted to hospital with ICD-10 diagnosis I21 between 2014 and 2019 was derived from the Unified National Electronic Health System and retrospectively analyzed.ResultsThe majority of the cohort (53,285, 67%) were men, with an average age of 63 (±12) years, predominantly of Kazakh (38,057, 48%) and Russian (24,583, 31%) ethnicities. Hypertension was the most common comorbidity (61,972, 78%). In males, a sharp increase in incidence is present after 40 years, while for females, the morbidity increases gradually after 55. Throughout the observation period, all-cause mortality rose from 101 to 210 people per million population (PMP). In 2019, AMI account for 169,862 DALYs in Kazakhstan, with a significant proportion (79%) attributed to years of life lost due to premature death (YLDs). Approximately half of disease burden due to AMI (80,794 DALYs) was in age group 55–69 years. Although incidence is higher for men, they have better survival rates than women. In terms of revascularization procedures, coronary artery bypass grafting yielded higher survival rates compared to percutaneous coronary intervention (86.3% and 80.9% respectively) during the 5-year follow-up.ConclusionThis research evaluated the burden and disability-adjusted life years of AMI in Kazakhstan, the largest Central Asian country. The results show that more effective disease management systems and preventive measures at earlier ages are needed.

Background: It has been reported that a deadly virus known as COVID-19 has arisen in China and has spread rapidly throughout the country. The globe was shattered, and a large number of people on the planet died. It quickly became an epidemic due to the absence of apparent symptoms and causes for patients, confusion appears due to the lack of sufficient laboratory results, and its intelligent algorithms were used to make decisions on clinical outcomes. Methods: This study developed a new framework for medical datasets with high missing values based on deep-learning optimization models. The robustness of our model is achieved by combining: Data Missing Care (DMC) Framework to overcome the problem of high missing data in medical datasets, and Grid-Search optimization used to develop an improved deep predictive training model for patients with COVID-19 by setting multiple hyperparameters and tuning assessments on three deep learning algorithms: ANN (Artificial Neural Network), CNN (Convolutional Neural Network), and Recurrent Neural Networks (RNN). Results: The experiment results conducted on three medical datasets showed the effectiveness of our hybrid approach and an improvement in accuracy and efficiency since all the evaluation metrics were close to ideal for all deep learning classifiers. We got the best evaluation in terms of accuracy 98%, precession 98.5%, F1-score 98.6%, and ROC Curve (95% to 99%) for the COVID-19 dataset provided by GitHub. The second dataset is also Covid-19 provided by Albert Einstein Hospital with high missing data after applying our approach the accuracy reached more than 91%. Third dataset for Cervical Cancer provided by Kaggle all the evaluation metrics reached more than 95%. Conclusions: The proposed formula for processing this type of data can replace the traditional formats in optimization while providing high accuracy and less time to classify patients. Whereas, the experimental results of our approach, supported by comprehensive statistical analysis, can improve the overall evaluation performance of the problem of classifying medical data sets with high missing values. Therefore, this approach can be used in many areas such as energy management, environment, and medicine.

Addressing the challenges in diagnosing and classifying self-care difficulties in exceptional children’s healthcare systems is crucial. The conventional diagnostic process, reliant on professional healthcare personnel, is time-consuming and costly. This study introduces an intelligent approach employing expert systems built on artificial intelligence technologies, specifically random forest, decision tree, support vector machine, and bagging classifier. The focus is on binary and multi-label SCADI datasets. To enhance model performance, we implemented resampling and data shuffling methods to tackle data imbalance and generalization issues, respectively. Additionally, a hyper framework feature selection strategy was applied, using mutual-information statistics and random forest recursive feature elimination (RF-RFE) based on a forward elimination method. Prediction performance and feature significance experiments, employing Shapley value explanation (SHAP), demonstrated the effectiveness of the proposed model. The framework achieved a remarkable overall accuracy of 99% for both datasets used with the fewest number of unique features reported in contemporary literature. The use of hyperparameter tuning for RF modeling further contributed to this significant improvement, suggesting its potential utility in diagnosing self-care issues within the medical industry.

This review focuses on the principles, applications, and performance of mpMRI for bladder imaging. Quantitative imaging biomarkers (QIBs) derived from mpMRI are increasingly used in oncological applications, including tumor staging, prognosis, and assessment of treatment response. To standardize mpMRI acquisition and interpretation, an expert panel developed the Vesical Imaging–Reporting and Data System (VI-RADS). Many studies confirm the standardization and high degree of inter-reader agreement to discriminate muscle invasiveness in bladder cancer, supporting VI-RADS implementation in routine clinical practice. The standard MRI sequences for VI-RADS scoring are anatomical imaging, including T2w images, and physiological imaging with diffusion-weighted MRI (DW-MRI) and dynamic contrast-enhanced MRI (DCE-MRI). Physiological QIBs derived from analysis of DW- and DCE-MRI data and radiomic image features extracted from mpMRI images play an important role in bladder cancer. The current development of AI tools for analyzing mpMRI data and their potential impact on bladder imaging are surveyed. AI architectures are often implemented based on convolutional neural networks (CNNs), focusing on narrow/specific tasks. The application of AI can substantially impact bladder imaging clinical workflows; for example, manual tumor segmentation, which demands high time commitment and has inter-reader variability, can be replaced by an autosegmentation tool. The use of mpMRI and AI is projected to drive the field toward the personalized management of bladder cancer patients.

The research aims to disclose academicians' perceptions of "Online Teaching Tools" (OTT) within the UTAUT-2. In the study, a qualitative research method was implemented. The case study pattern has also been used for the study. The study group of the research was determined through maximum variation sampling from purposive sampling methods. Twenty-six academicians working at the faculties of education of two universities in the Western Black Sea Region of Turkey constitute the study group of the study. A semi-structured interview form was employed as a data collection tool. The data were analyzed inductively. MAXQDA 20 program was implemented in data analysis. In terms of performance expectancy, OTT can be integrated into teaching methods and techniques. In terms of effort expectancy, it has also been found that the use of OTT is mostly straightforward, and the fact that it is time-consuming, and challenging does not mean negative and technical problems might occur. Regarding social influence, academicians receive support and advice on using OTT. In terms of facilitating conditions, most academicians perceive themselves as sufficient. In terms of hedonic motivation, academicians are of the opinion that that OTT become enjoyable, primarily if it is used via student participation. It is shown that the difference in the exchange rate within Turkey increases fees as a price value. In further studies, it is possible to refer to the opinions of academicians from various fields based on the relevant theory via the following qualitative and quantitative methods.

The primary purpose of this research was to determine the digital literacy proficiency perceptions of primary school teachers working in Eskisehir and compare them based on certain variables. The mixed method was used as a research method; the data were collected using the Digital Literacy Assessment Scale developed by Acar (2015) for quantitative findings and in-depth interviews for qualitative results. The quantitative study group consisted of 733 primary school teachers. In the qualitative dimension of the research, there were data from 7 primary school teachers who agreed to fill out the structured interview form and then take part in an in-depth interview. Quantitative data of the study were analyzed using SPSS software. Qualitative data were analyzed with the descriptive analysis method and interpreted according to their relationship with the research questions. The result showed that the primary school teachers perceive themselves as highly sufficient in regard to the different competence areas of digital literacy and that they dominate these competence areas to a great extent. There were significant differences between the digital literacy levels of the primary school teachers and their age, gender, educational status, seniority in the profession, personal computer possession, constant internet connection, technologies/practices used in education, and technological education. However, the grade level taught was not an influential factor in their digital literacy levels. Moreover, in-service training should be organized to provide digital literacy, using Web 2.0 tools, and integrating technology into the classroom environment, starting with those who have more service than their years of seniority in the profession.

The acquisition of the competencies of sustainability literacy through education requires an understanding of how sustainability literacy is shaped. In this connection, in the current study, the interaction between the following dimensions of sustainability literacy was investigated; SA (sustainability attitude), SB (sustainability behavior) and SK (sustainability knowledge). The study was conducted as a survey study on university students in the UK and Turkey within the context of a project entitled "Ecological Literacy Education" and supported by TUBITAK (Turkish Scientific and Technological Research Organization). The participants (n:1023) of the study are students attending several departments of Plymouth University in the UK and Mugla Sitki Kocman University in Turkey. The data were collected by using the Scale of Sustainability Literacy and analyzed using AMOS version 24. Although it was understood that there were mutual and positive correlations between the dimensions of sustainability literacy, it was revealed that the correlations between some sub-dimensions of these dimensions showed variation. The results of the study are expected to contribute to revealing the interaction between the dimensions of sustainability literacy in more detail and accordingly to the effectiveness of sustainability education.

This study aims to investigate pre-service teachers' integration of technology and how the integration of technology influences the level of cognitive demands of the mathematics tasks in their mathematics technology activities. The purpose of this study was to investigate the various levels of cognitive demands of mathematical tasks created or modified by pre-service mathematics teachers for technology activities. This study presents case studies of pre-service teachers chosen from a group of participants, and these PSTs come from a variety of backgrounds. Showcase Portfolios and lesson plans were gathered in order to comprehend the selection/creation of mathematical tasks and how they were intended to be implemented in the classroom by PSTs. This study offers suggestions for teacher preparation to integrate technology into mathematics instruction in ways that support students' learning through a review of results, the cognitive demands of mathematical tasks in PSTs' technology activities.

This study examines the effects of digital game-based and different educational programs on the phonological awareness skills of 60-72-month-old children. The study group of the research consists of 60-72-month-old children studying in kindergartens affiliated with primary schools. The study group was divided into three experimental groups and one control group. There were 22 children in experimental group 1, 16 in experimental group 2, 17 in experimental group 3, and 17 in the control group, totaling 72 children. In the study, a phonological awareness education program was applied to experimental group 1, a digital game-supported phonological awareness education program was applied to experimental group 2, a digital game-based phonological awareness education program was applied to experimental group 3, and no intervention was applied to the control group. A quasiexperimental design was used as the research model. The Study used the Early Literacy Skills Assessment Tool (ELSA) and the Early Literacy Test for Preschoolers (ELTKC) as data collection tools. A pre-test was administered to the study and control groups, and after the pre-test, educational programs were applied to the experimental groups for ten weeks. After the educational programs were implemented, a post-test was administered to the experimental and control groups, and a retention test was administered to the experimental groups. According to the results of the study, it was determined that there was a significant difference between the experimental groups and between the experimental groups and the control group.

Medical imaging data annotation is expensive and time-consuming. Supervised deep learning approaches may encounter overfitting if trained with limited medical data, and further affect the robustness of computer-aided diagnosis (CAD) on CT scans collected by various scanner vendors. Additionally, the high false-positive rate in automatic lung nodule detection methods prevents their applications in daily clinical routine diagnosis. To tackle these issues, we first introduce a novel self-learning schema to train a pre-trained model by learning rich feature representatives from large-scale unlabeled data without extra annotation, which guarantees a consistent detection performance over novel datasets. Then, a 3D feature pyramid network (3DFPN) is proposed for high-sensitivity nodule detection by extracting multi-scale features, where the weights of the backbone network are initialized by the pre-trained model and then fine-tuned in a supervised manner. Further, a High Sensitivity and Specificity (HS2) network is proposed to reduce false positives by tracking the appearance changes among continuous CT slices on Location History Images (LHI) for the detected nodule candidates. The proposed method’s performance and robustness are evaluated on several publicly available datasets, including LUNA16, SPIE-AAPM, LungTIME, and HMS. Our proposed detector achieves the state-of-the-art result of 90.6% sensitivity at 1/8 false positive per scan on the LUNA16 dataset. The proposed framework’s generalizability has been evaluated on three additional datasets (i.e., SPIE-AAPM, LungTIME, and HMS) captured by different types of CT scanners.

Cancer care increasingly relies on imaging for patient management. The two most common cross-sectional imaging modalities in oncology are computed tomography (CT) and magnetic resonance imaging (MRI), which provide high-resolution anatomic and physiological imaging. Herewith is a summary of recent applications of rapidly advancing artificial intelligence (AI) in CT and MRI oncological imaging that addresses the benefits and challenges of the resultant opportunities with examples. Major challenges remain, such as how best to integrate AI developments into clinical radiology practice, the vigorous assessment of quantitative CT and MR imaging data accuracy, and reliability for clinical utility and research integrity in oncology. Such challenges necessitate an evaluation of the robustness of imaging biomarkers to be included in AI developments, a culture of data sharing, and the cooperation of knowledgeable academics with vendor scientists and companies operating in radiology and oncology fields. Herein, we will illustrate a few challenges and solutions of these efforts using novel methods for synthesizing different contrast modality images, auto-segmentation, and image reconstruction with examples from lung CT as well as abdome, pelvis, and head and neck MRI. The imaging community must embrace the need for quantitative CT and MRI metrics beyond lesion size measurement. AI methods for the extraction and longitudinal tracking of imaging metrics from registered lesions and understanding the tumor environment will be invaluable for interpreting disease status and treatment efficacy. This is an exciting time to work together to move the imaging field forward with narrow AI-specific tasks. New AI developments using CT and MRI datasets will be used to improve the personalized management of cancer patients.

Abstract Combatting the current global epidemic of obesity requires that people have a realistic understanding of what a healthy body size looks like. This is a particular issue in different population sub-groups, where there may be increased susceptibility to obesity-related diseases. Prior research has been unable to systematically assess body size judgement due to a lack of attention to gender and race; our study aimed to identify the contribution of these factors. Using a data-driven multi-variate decision tree approach, we varied the gender and race of image stimuli used, and included the same diversity among participants. We adopted a condition-rich categorization visual task and presented participants with 120 unique body images. We show that gender and weight categories of the stimuli affect accuracy of body size perception. The decision pattern reveals biases for male bodies, in which participants showed an increasing number of errors from leaner to bigger bodies, particularly under-estimation errors. Participants consistently mis-categorized overweight male bodies as normal weight, while accurately categorizing normal weight. Overweight male bodies are now perceived as part of an expanded normal: the perceptual boundary of normal weight has become wider than the recognized BMI category. For female bodies, another intriguing pattern emerged, in which participants consistently mis-categorized underweight bodies as normal, whilst still accurately categorizing normal female bodies. Underweight female bodies are now in an expanded normal, in opposite direction to that of males. Furthermore, an impact of race type and gender of participants was also observed. Our results demonstrate that perceptual weight categorization is multi-dimensional, such that categorization decisions can be driven by ultiple factors.

Study objective: To determine whether there has been growth in publications on the use of artificial intelligence in cardiology and oncology, we assessed historical trends in publications related to artificial intelligence applications in cardiology and oncology, which are the two fields studying the leading causes of death worldwide. Upward trends in publications may indicate increasing interest in the use of artificial intelligence in these crucial fields. Design/setting: To evaluate evidence of increasing publications on the use of artificial intelligence in cardiology and oncology, historical trends in related publications on PubMed (the biomedical repository most frequently used by clinicians and scientists in these fields) were reviewed. Results: Findings indicated that research output related to artificial intelligence (and its subcategories) generally increased over time, particularly in the last five years. With some initial degree of vacillation in publication trends, a slight qualitative inflection was noted in approximately 2015, in general publications and especially for oncology and cardiology, with subsequent consistent exponential growth. Publications predominantly focused on “machine learning” (n = 20,301), which contributed to the majority of the accelerated growth in the field, compared to “artificial intelligence” (n = 4535), “natural language processing” (n = 2608), and “deep learning” (n = 4459). Conclusion: Trends in the general biomedical literature and particularly in cardiology and oncology indicated exponential growth over time. Further exponential growth is expected in future years, as awareness and cross-disciplinary collaboration and education increase. Publications specifically on machine learning will likely continue to lead the way.

Several observation protocols in different theoretical frameworks and components have been designed and validated by teacher trainers and professional development providers to capture and categorize observational data on the characteristics and level of inquiry in science practices. However, certain constraints limit their wide use, such as the neglect of certain indicators of scientific inquiry, the scoring of just summative goals, or the necessity for extensive observer training. The present study, therefore, aims to propose a tool that assists professional development providers and mentors in diagnosing the closeness of practices to scientific inquiry in the context of various settings, from traditional didactic lectures to scientific inquiry, making it easily operational for self and peer evaluations as well as 360-degree video feedback. The method concentrated on multiple observations provides supportive evidence about the tool's internal consistency coefficient, the relationship between its components, and the inter-rater reliability coefficient between multiple scorers. The results highlight that the tool, which includes the components of course structure, course overview, and teacher-student communication, has the potential to contribute to the growing library of observation protocols.

Early intervention in kidney cancer helps to improve survival rates. Abdominal computed tomography (CT) is often used to diagnose renal masses. In clinical practice, the manual segmentation and quantification of organs and tumors are expensive and time-consuming. Artificial intelligence (AI) has shown a significant advantage in assisting cancer diagnosis. To reduce the workload of manual segmentation and avoid unnecessary biopsies or surgeries, in this paper, we propose a novel end-to-end AI-driven automatic kidney and renal mass diagnosis framework to identify the abnormal areas of the kidney and diagnose the histological subtypes of renal cell carcinoma (RCC). The proposed framework first segments the kidney and renal mass regions by a 3D deep learning architecture (Res-UNet), followed by a dual-path classification network utilizing local and global features for the subtype prediction of the most common RCCs: clear cell, chromophobe, oncocytoma, papillary, and other RCC subtypes. To improve the robustness of the proposed framework on the dataset collected from various institutions, a weakly supervised learning schema is proposed to leverage the domain gap between various vendors via very few CT slice annotations. Our proposed diagnosis system can accurately segment the kidney and renal mass regions and predict tumor subtypes, outperforming existing methods on the KiTs19 dataset. Furthermore, cross-dataset validation results demonstrate the robustness of datasets collected from different institutions trained via the weakly supervised learning schema.

Background: COVID-19 (Coronavirus Disease-2019) is a pandemic disease, infecting more than 26.5 million people. Since there is no specific and effective treatment; early diagnosis and optimal isolation of the patient are of vital importance. Real-time polymerase chain reaction-based (RT-PCR) analyses do not achieve sufficient sensitivity in the diagnosis of the disease. Methods: The data from 2217 patients diagnosed as COVID-19 between March 2020 and June 2020 and hospitalized or discharged with home isolation were retrospectively analyzed. Demographic data, comorbidities, PCR results, initial computed tomography (CT), laboratory values, Lactate Dehydrogenase (LDH)/Lymphocyte ratio, initial treatments and last status were recorded. The diagnostic sensitivity of LDH/Lymphocyte ratio, which is the main purpose of the study, was analyzed statistically. Results: In order to test the effectiveness of LDH/Lymphocyte ratio for COVID-19 for diagnostic purposes, CT results were considered as gold standard. The area under the curve (AUC) was found to be 0.706 (p < 0.001; cut-off > 0.06) (Sensitivity: 76.4, specificity: 59.60). For the evaluation of LDH/Lymphocyte ratio in terms of survival, AUC was found to be 0.749 (p < 0.001; cut-off > 0.21) (Sensitivity: 70.59, specificity: 73.88). Conclusion: Studies based on radiological findings have demonstrated that CT involvement has higher sensitivity. LDH/Lymphocyte ratio was analyzed in terms of diagnosis and mortality with using specific CT involvement as gold standard method which was found to be a more sensitive due to PCR false negativity; 0.06 and 0.21 were obtained as cut off values for diagnosis and mortality.

Cardiovascular disease is a leading cause of death among cancer survivors, second only to cancer recurrence or development of new tumors. Cardio-oncology has therefore emerged as a relatively new specialty focused on prevention and management of cardiovascular consequences of cancer therapies. Yet challenges remain regarding precision and accuracy with predicting individuals at highest risk for cardiotoxicity. Barriers such as access to care also limit screening and early diagnosis to improve prognosis. Thus, developing innovative approaches for prediction and early detection of cardiovascular illness in this population is critical. In this review, we provide an overview of the present state of machine learning applications in cardio-oncology. We begin by outlining some factors that should be considered while utilizing machine learning algorithms. We then examine research in which machine learning has been applied to improve prediction of cardiac dysfunction in cancer survivors. We also highlight the use of artificial intelligence (AI) in conjunction with electrocardiogram (ECG) to predict cardiac malfunction and also atrial fibrillation (AF), and we discuss the potential role of wearables. Additionally, the article summarizes future prospects and critical takeaways for the application of machine learning in cardio-oncology. This study is the first in a series on artificial intelligence in cardio-oncology, and complements our manuscript on echocardiography and other forms of imaging relevant to cancer survivors cared for in cardiology clinical practice.

Abstract Background Parkinson’s Disease (PD) is a clinically diagnosed neurodegenerative disorder that affects both motor and non-motor neural circuits. Speech deterioration (hypokinetic dysarthria) is a common symptom, which often presents early in the disease course. Machine learning can help movement disorders specialists improve their diagnostic accuracy using non-invasive and inexpensive voice recordings. Method We used “Parkinson Dataset with Replicated Acoustic Features Data Set” from the UCI-Machine Learning repository. The dataset included 44 speech-test based acoustic features from patients with PD and controls. We analyzed the data using various machine learning algorithms including Light and Extreme Gradient Boosting, Random Forest, Support Vector Machines, K-nearest neighborhood, Least Absolute Shrinkage and Selection Operator Regression, as well as logistic regression. We also implemented a variable importance analysis to identify important variables classifying patients with PD. Results The cohort included a total of 80 subjects: 40 patients with PD (55% men) and 40 controls (67.5% men). Disease duration was 5 years or less for all subjects, with a mean Unified Parkinson’s Disease Rating Scale (UPDRS) score of 19.6 (SD 8.1), and none were taking PD medication. The mean age for PD subjects and controls was 69.6 (SD 7.8) and 66.4 (SD 8.4), respectively. Our best-performing model used Light Gradient Boosting to provide an AUC of 0.951 with 95% confidence interval 0.946–0.955 in 4-fold cross validation using only seven acoustic features. Conclusions Machine learning can accurately detect Parkinson’s disease using an inexpensive and non-invasive voice recording. Light Gradient Boosting outperformed other machine learning algorithms. Such approaches could be used to inexpensively screen large patient populations for Parkinson’s disease.

Background: Atrial fibrillation (AF) is one of the most common cardiovascular problems, and its asymptomatic tendency makes AF detection challenging. Machine and deep learning methods are commonly used in AF detection. Objective: The purpose of this study was to evaluate the information provided by convolutional neural network (CNN) and random forest (RF) machine learning models for AF classification. Methods: We manually extracted 166 time–frequency domains and linear and nonlinear features to classify single-lead electrocardiograms (ECGs) as normal, AF, other, or noisy sinus rhythms. We selected a subset of 56 robust features using a genetic algorithm that was used in the RF model. In a separate study, a 1-dimensional, 12-layer CNN was designed on the raw ECG rhythms. Four features from the output layer and 128 features from the fully connected layer of CNN were explored independently for classification. The models were trained and internally validated on 8,528 ECGs and externally validated on a hidden dataset containing 3,658 ECGs. Next,we analyzed the correlation between engineered and CNN-learned features. Results: An RF classifier trained with 56-engineered features resulted in an F1 score of 0.91, 0.78, and 0.72 for normal, AF, and other rhythms, respectively. However, an ensemble of support vector machine and the CNN model resulted in an F1 score of 0.92, 0.87, and 0.80, respectively. Conclusion: We explored various features and machine learning models to identify AF rhythms using short (9–61 seconds) single-lead ECG recordings. Our results showed that the proposed CNN model abstracted distinctive features for AF classification.

Abstract Background Desmoplastic small round cell tumor (DSRCT) is a rare, aggressive neoplasm seen in children and young adults, usually manifested by involvement of abdominal serosa. Here, we present an unusual case of primary DSRCT of kidney. Case presentation The patient was an 8-year-old girl with a large renal mass which was confused with primitive neuroectodermal tumor (PNET) in the needle biopsy. The tumor had a variegated histology revealing frequent pseudo-rosette formations, pseudopapillary architecture, rhabdoid, clear or pleomorphic cells in addition to typical small round cell morphology and desmoplasia. It showed immunohistochemical features of DSRCT, and EWSR1 re-arrangement. Conclusions Proffering this diagnosis is particularly difficult for tumors of viscera because of the incognizance of the entity in these locations. Moreover, DSRCT is a great mimicker and may get easily confused with more common kidney malignancies of childhood such as Wilms tumor, PNET/EWS, rhabdoid tumor, clear cell sarcoma, and other small round cell tumors as well as renal cell carcinomas. The distinction is critical as the accurate therapeutic approach will require correct diagnosis.

Abstract Background Several phase 3 studies reported positive results for combinations of Immune-Oncology (IO) and Vascular Endothelial Growth Factor (VEGF) targeted therapies in patients with metastatic clear cell Renal Cell Carcinoma (ccRCC). However, there are limited data on outcomes to systemic therapy after IO-VEGF combinations. Methods A retrospective analysis was performed on patients with metastatic ccRCC treated at the Memorial Sloan Kettering Cancer Center and Cleveland Clinic who initiated systemic therapy post IO-VEGF including combinations with VEGF receptor (VEGFR) tyrosine kinase inhibitors (IO-TKI) and combinations with the anti-VEGF monoclonal antibody bevacizumab (IO-Bev). The study objectives were to evaluate the objective response rate (ORR), progression-free survival (PFS) and overall survival (OS) on systemic therapy post IO-VEGF. RECIST v1.1 criteria were used to determine radiological responses and progression. Survival estimates were evaluated with the Kaplan-Meier methods and the log-rank test from the start of systemic therapy post IO-VEGF to the event of interest. Results A total of fifty-nine patients were treated post discontinuation of IO-VEGF regimens which included IO-Bev (n = 35; 59%) and IO-TKI (n = 24; 41%). Fifty-eight patients (98%) received IO-VEGF regimens as part of a clinical trial. Subsequent therapies included cabozantinib (n = 22; 37%), axitinib (n = 18; 31%), pazopanib (n = 4; 7%), lenvatinib and everolimus (n = 4; 7%), mTOR inhibitor monotherapy (n = 3; 5%), axitinib and dalantercept (n = 2; 3%), sunitinib (n = 1; 2%), sorafenib (n = 1; 2%), and treatment with agents on unreported clinical trials (n = 4; 7%). Patients treated on unreported clinical trials were excluded from the efficacy analysis. Post IO-VEGF, the ORR was 25% and median PFS was 12.0 months (95% CI, 8.2–24.5). Median OS was 24.5 months (95% CI, 12–NE) and 12 months OS rate was 63.3% (95% CI, 48.6–74.9). We observed no differences post IO-VEGF OS when comparing IO- TKI vs IO-Bev (Log-rank p = 0.73). Conclusions Post IO-VEGF, most patients received VEGFR-TKIs. In this setting, VEGFR-TKIs demonstrated clinical activity and remain a viable option for salvage therapy after progression on IO-VEGF.

Abstract Purpose Knowing the precise flow of cerebrospinal fluid (CSF) is important in the management of multiple neurological diseases. Technology for non-invasively quantifying CSF flow would allow for precise localization of injury and assist in evaluating the viability of certain devices placed in the central nervous system (CNS). Methods We describe a near-infrared fluorescent dye for accurately monitoring CSF flow by positron emission tomography (PET) and fluorescence. IR-783, a commercially available near-infrared dye, was chemically modified and radiolabeled with fluorine-18 to give [18F]-IR783-AMBF3. [18F]-IR783-AMBF3 was intrathecally injected into the rat models with normal and aberrant CSF flow and evaluated by the fluorescence and PET/MRI or PET/CT imaging modes. Results IR783-AMBF3 was clearly distributed in CSF-containing volumes by PET and fluorescence. We compared IR783-AMBF3 (fluorescent at 778/793 nm, ex/em) to a shorter-wavelength, fluorescein equivalent (fluorescent at 495/511 nm, ex/em). IR783-AMBF3 was superior for its ability to image through blood (hemorrhage) and for imaging CSF-flow, through-skin, in subdural-run lumboperitoneal shunts. IR783-AMBF3 was safe under the tested dosage both in vitro and in vivo. Conclusion The superior imaging properties of IR783-AMBF3 could lead to enhanced accuracy in the treatment of patients and would assist surgeons in non-invasively diagnosing diseases of the CNS.

Background: Barth syndrome (BTHS) is a rare X-linked genetic disease that affects multiple systems and leads to complex clinical manifestations. Although a considerable amount of research has focused on the physical aspects of the disease, less has focused on the psychosocial impact and quality of life (QoL) in BTHS. Methods: The current study investigated caregiver- (n = 10) and self-reported (n = 16) psychological well-being and QoL in a cohort of BTHS-affected patients and families. Participants completed the depression and anxiety components of the Patient-Reported Outcomes Information System (PROMIS) Short Form 8A and Health-related quality of life (HRQoL) surveys at enrollment and again during a follow-up period ranging from 6 to 36 months after baseline. Results: Quality of life changed significantly over time and the various domains with some improvement and some decline. Among the available caregiver-patient dyad data, there was a trend toward discordance between caregiver and self-reported outcomes. Most notably, patients reported improvement in HRQoL, while caregivers reported declines. This suggests that there may be differences in perceived quality of life between the patients and parents, though our study is limited by small sample size. Conclusion: Our study provides valuable insights into the impacts of psychosocial and mental health aspects of BTHS. Implications of these findings include incorporating longitudinal assessment of QoL and screening for psychological symptoms in BTHS care to identify interventions that may drastically impact health status and the course of the disease.

Background and Objectives: Recently, many studies have focused on the early diagnosis of coronary artery disease (CAD), which is one of the leading causes of cardiac-associated death worldwide. The effectiveness of the most important features influencing disease diagnosis determines the performance of machine learning systems that can allow for timely and accurate treatment. We performed a Hybrid ML framework based on hard ensemble voting optimization (HEVO) to classify patients with CAD using the Z-Alizadeh Sani dataset. All categorical features were converted to numerical forms, the synthetic minority oversampling technique (SMOTE) was employed to overcome imbalanced distribution between two classes in the dataset, and then, recursive feature elimination (RFE) with random forest (RF) was used to obtain the best subset of features. Materials and Methods: After solving the biased distribution in the CAD data set using the SMOTE method and finding the high correlation features that affected the classification of CAD patients. The performance of the proposed model was evaluated using grid search optimization, and the best hyperparameters were identified for developing four applications, namely, RF, AdaBoost, gradient-boosting, and extra trees based on an HEV classifier. Results: Five fold cross-validation experiments with the HEV classifier showed excellent prediction performance results with the 10 best balanced features obtained using SMOTE and feature selection. All evaluation metrics results reached > 98% with the HEV classifier, and the gradient-boosting model was the second best classification model with accuracy = 97% and F1-score = 98%. Conclusions: When compared to modern methods, the proposed method perform well in diagnosing coronary artery disease, and therefore, the proposed method can be used by medical personnel for supplementary therapy for timely, accurate, and efficient identification of CAD cases in suspected patients.

This study is based on the limited number of studies on recognizing and evaluating preschool children; It aims to be a guide for families and teachers regarding the developmental problems and social and emotional maturity levels of children who continue their preschool education online during the pandemic process and to evaluate the results in terms of children and turn them into opportunities. This qualitative study is carried out in a case study pattern using the "Koppitz Draw a Person Test" for 5-year-old preschool children. The Working group consists of children a total of 100 students, 50 girls and 50 boys, attend official and independent kindergartens through distance education in Akdeniz, Mezitli, Toroslar and Yenisehir districts in the center of Mersin, affiliated to the Ministry of National Education (MEB) in the 2020-2021 academic year. Pictures obtained by the "Koppitz Draw a Human Test" were used as data tools. The data were coded and analyzed by the "Koppitz Draw a Human Test." The data obtained in the study were interpreted in tables according to Koppitz (1968). In the study, the participant children drew the indicators for academic failures as 8%; and 98% of the indicators expressing social and emotional inadequacy and representing at least one or more anxiety states in the child were drawn. In addition, this study determined that children with self-confidence problems, showing emotional weakness, recessive, and lack of motivation develop negative attitudes when faced with obstacles and rules in response to their expectations during the pandemic process.

In this study, the thermal performance of vacuumed copper pipe has been experimentally investigated by using different volume values of 5 ml, 10 ml, 15 ml, and 20 ml with inclination angles of 45°, 60°, and 90° for variable vacuum pressures. For this purpose, the inner, outer diameter, and length of the vacuumed pipes filled with the distilled water were 26 mm, 28 mm, and 1500 mm, respectively. This copper pipe was vacuumed until the absolute pressure of 6.32 kPa and immersed in a heat source at a constant temperature of 82°C at the beginning of the experiments. The surface temperatures of the vacuumed pipes were measured at twenty different points by using K-type thermocouples in two different data loggers. All calculations were performed as variable saturation temperatures at variable vacuum pressures with changing the time. According to the results, the experimental boiling heat transfer coefficients showed a good agreement with the correlations in the literature. Also, the best thermal resistance was obtained in the vacuumed copper pipe with a water volume of 10 ml at the inclination angle of 90° until the end of the experiment.

Radiomics, one of the potential methods for developing clinical biomarker, is one of the exponentially growing research fields. In addition to its potential, several limitations have been identified in this field, and most importantly the effects of variations in imaging parameters on radiomic features (RFs). In this study, we investigate the potential of RFs to predict overall survival in patients with clear cell renal cell carcinoma, as well as the impact of ComBat harmonization on the performance of RF models. We assessed the robustness of the results by performing the analyses a thousand times. Publicly available CT scans of 179 patients were retrospectively collected and analyzed. The scans were acquired using different imaging vendors and parameters in different medical centers. The performance was calculated by averaging the metrics over all runs. On average, the clinical model significantly outperformed the radiomic models. The use of ComBat harmonization, on average, did not significantly improve the performance of radiomic models. Hence, the variability in image acquisition and reconstruction parameters significantly affect the performance of radiomic models. The development of radiomic specific harmonization techniques remain a necessity for the advancement of the field.

PurposeWe aimed to explore potential confounders of prognostic radiomics signature predicting survival outcomes in clear cell renal cell carcinoma (ccRCC) patients and demonstrate how to control for them.Materials and MethodsPreoperative contrast enhanced abdominal CT scan of ccRCC patients along with pathological grade/stage, gene mutation status, and survival outcomes were retrieved from The Cancer Imaging Archive (TCIA)/The Cancer Genome Atlas—Kidney Renal Clear Cell Carcinoma (TCGA-KIRC) database, a publicly available dataset. A semi-automatic segmentation method was applied to segment ccRCC tumors, and 1,160 radiomics features were extracted from each segmented tumor on the CT images. Non-parametric principal component decomposition (PCD) and unsupervised hierarchical clustering were applied to build the radiomics signature models. The factors confounding the radiomics signature were investigated and controlled sequentially. Kaplan–Meier curves and Cox regression analyses were performed to test the association between radiomics signatures and survival outcomes.Results183 patients of TCGA-KIRC cohort with available imaging, pathological, and clinical outcomes were included in this study. All 1,160 radiomics features were included in the first radiomics signature. Three additional radiomics signatures were then modelled in successive steps removing redundant radiomics features first, removing radiomics features biased by CT slice thickness second, and removing radiomics features dependent on tumor size third. The final radiomics signature model was the most parsimonious, unbiased by CT slice thickness, and independent of tumor size. This final radiomics signature stratified the cohort into radiomics phenotypes that are different by cancer-specific and recurrence-free survival; HR (95% CI) = 3.0 (1.5–5.7), p

Introduction: Given the high mortality rate within the first year of dialysis initiation, an accurate estimation of postdialysis mortality could help patients and clinicians in decision making about initiation of dialysis. We aimed to use machine learning (ML) by incorporating complex information from electronic health records to predict patients at risk for postdialysis short-term mortality. Methods: This study was carried out on a contemporary cohort of 27,615 US veterans with incident end-stage renal disease (ESRD). We implemented a random forest method on 49 variables obtained before dialysis transition to predict outcomes of 30-, 90-, 180-, and 365-day all-cause mortality after dialysis initiation. Results: The mean (±SD) age of our cohort was 68.7 ± 11.2 years, 98.1% of patients were men, 29.4% were African American, and 71.4% were diabetic. The final random forest model provided C-statistics (95% confidence intervals) of 0.7185 (0.6994–0.7377), 0.7446 (0.7346–0.7546), 0.7504 (0.7425–0.7583), and 0.7488 (0.7421–0.7554) for predicting risk of death within the 4 different time windows. The models showed good internal validity and replicated well in patients with various demographic and clinical characteristics and provided similar or better performance compared with other ML algorithms. Results may not be generalizable to non-veterans. Use of predictors available in electronic medical records has limited the assessment of number of predictors. Conclusion: We implemented and ML-based method to accurately predict short-term postdialysis mortality in patients with incident ESRD. Our models could aid patients and clinicians in better decision making about the best course of action in patients approaching ESRD. Keywords: chronic kidney disease, dialysis, end-stage renal disease, mortality, random forest

Abstract Background There is a change in the position of the remaining anatomical structures of the lower urinary tract system following radical prostatectomy. The aims of this investigation were to describe three novel methods used to measure the displacement of i) the vesico-urethral junction (VUJ), proximal membranous urethra (PMU) and anorectal junction (ARJ) and ii) the VUJ angle of displacement in men following radical prostatectomy and determine their intra- and interrater reliability. Methods Retrospective comparative measurement of twenty pre- and postoperative MRI scans was undertaken by one observer on two separate occasions and on one occasion by another observer. Three standardized midsagittal pelvimetry reference lines were used to describe three X, Y axis measurement systems. The displacement (mm) of the VUJ, PMU and ARJ, and the angle of displacement (degrees) of the VUJ was measured for each of the three methods. Interrater reliability of VUJ, PMU and ARJ displacement and the VUJ angle of displacement measurements was assessed using a two-way mixed-effects agreement intra-class correlation coefficient (ICC) with 95% confidence intervals (CI). Test-retest (intrarater) reliability was calculated using a two-way random effects consistency ICC with 95% CI for all displacement measures of the VUJ, PMU and ARJ for one observer between two days. Results The pubococcygeal line (PCL) axis measurement system demonstrated good to excellent intrarater and interrater reliability (ICC 95% interval lower bound > 0.75) for the VUJ and PMU displacement and the VUJ angle of displacement measurements. Other measurement systems were less reliable and more variable. Conclusions In this sample of 20 Korean patients with median prostate volume 27.5 mL and maximum volume 70 mL, the measurement methodology using the PCL consistently demonstrated good to excellent reliability and the lowest variability for the measurement of the displacement of the VUJ and PMU and the VUJ angle of displacement. The PCL methodology is recommended as the method of choice. Further studies should validate these results in patients with large prostate volumes.

A reliable and practical renal-lipid quantification and imaging method is needed. Here, the feasibility of an accelerated MRSI method to map renal fat fractions (FF) at 3T and its repeatability were investigated. A 2D density-weighted concentric-ring-trajectory MRSI was used for accelerating the acquisition of 48 × 48 voxels (each of 0.25 mL spatial resolution) without respiratory navigation implementations. The data were collected over 512 complex-FID timepoints with a 1250 Hz spectral bandwidth. The MRSI sequence was designed with a metabolite-cycling technique for lipid–water separation. The in vivo repeatability performance of the sequence was assessed by conducting a test–reposition–retest study within healthy subjects. The coefficient of variation (CV) in the estimated FF from the test–retest measurements showed a high degree of repeatability of MRSI-FF (CV = 4.3 ± 2.5%). Additionally, the matching level of the spectral signature within the same anatomical region was also investigated, and their intrasubject repeatability was also high, with a small standard deviation (8.1 ± 6.4%). The MRSI acquisition duration was ~3 min only. The proposed MRSI technique can be a reliable technique to quantify and map renal metabolites within a clinically acceptable scan time at 3T that supports the future application of this technique for the non-invasive characterization of heterogeneous renal diseases and tumors.

Background: Scientific evidence confirm that significant racial disparities exist in healthcare, including surgery outcomes. However, the causal pathway underlying disparities at preoperative physical condition of children is not well-understood.Objectives: This research aims to uncover the role of socioeconomic and environmental factors in racial disparities at the preoperative physical condition of children through multidimensional integration of several data sources at the patient and population level.Methods: After the data integration process an unsupervised k-means algorithm on neighborhood quality metrics was developed to split 29 zip-codes from Memphis, TN into good and poor-quality neighborhoods.Results: An unadjusted comparison of African Americans and white children showed that the prevalence of poor preoperative condition is significantly higher among African Americans compared to whites. No statistically significant difference in surgery outcome was present when adjusted by surgical severity and neighborhood quality.Conclusions: The socioenvironmental factors affect the preoperative clinical condition of children and their surgical outcomes.

Background: Total joint replacement (TJR) is a costly and prevalent surgery among older persons. Readmission is the most commonly experienced postoperative adverse event and is an important component to consider before surgery. A preoperative evaluation of postsurgical complications can assist with appropriate postoperative discharge decisions and can improve informed consent. Purpose: Our goal was to compare and use American Society of Anesthesiologists’ Physical Status Classification (ASA) versus modified frailty index (mFI-5) in readmission risk following TJR. Material & methods: We implemented light gradient boosting machine (LightGBM) as the main machine learning technique on data from American College of Surgeons National Surgical Quality Improvement Program (NSQIP) between 2011 and 2017 to preoperatively predict risk of postoperative readmission. We also compared results to extreme gradient boosting (XGB), logistic regression and random forests machine learning techniques. We included 22 presurgical diagnoses and symptoms as well as ASA and mFI-5 scores. However, we randomly split our cohort into 80% training and 20% hold out test data, performing 5-fold cross validation on the 80% training dataset. Model comparisons were based on area under the receiver operating characteristics curve (AUC) obtained on the hold out data. A variable importance analysis was performed on the best modelling technique. Results: Our analytic sample included 477, 616 surgeries. The readmission rate was 3.6% (n = 17 430). ASA class as a standalone predictor yielded an AUC of 0.607 and mFI-5 yielded an AUC of 0.585. LightGBM models using all the defined predictors resulted in an AUC of 0.67. Variable importance analysis on the final LightGBM model showed that ASA, age and mFI-5 are amongst the most important predictors. However, the frailty index can provide more detailed information on the requirement of care post-surgery. Conclusions: There is value in measuring not only the diagnoses and symptoms, but also patient frailty. As standalone variables, both ASA and mFI-5 are moderately associated with post TJR readmission. The LightGBM model predicted post-TJR readmission with moderate accuracy. Future studies should also include socioeconomic and behavioral factors as potential predictors of readmission.