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Wilson's disease (WD) is caused by copper accumulation in the brain and liver, and if not treated early, can lead to severe disability and death. WD has shown white matter hyperintensity (WMH) in the brain magnetic resonance scans (MRI) scans, but the diagnosis is challenging due to (i) subtle intensity changes and (ii) weak training MRI when using artificial intelligence (AI). Design and validate seven types of high-performing AI-based computer-aided design (CADx) systems consisting of 3D optimized classification, and characterization of WD against controls. We propose a "conventional deep convolution neural network" (cDCNN) and an "improved DCNN" (iDCNN) where rectified linear unit (ReLU) activation function was modified ensuring "differentiable at zero." Three-dimensional optimization was achieved by recording accuracy while changing the CNN layers and augmentation by several folds. WD was characterized using (i) CNN-based feature map strength and (ii) Bispectrum strengths of pixels having higher probabilities of WD. We further computed the (a) area under the curve (AUC), (b) diagnostic odds ratio (DOR), (c) reliability, and (d) stability and (e) benchmarking. Optimal results were achieved using 9 layers of CNN, with 4-fold augmentation. iDCNN yields superior performance compared to cDCNN with accuracy and AUC of 98.28 ± 1.55, 0.99 (p < 0.0001), and 97.19 ± 2.53%, 0.984 (p < 0.0001), respectively. DOR of iDCNN outperformed cDCNN fourfold. iDCNN also outperformed (a) transfer learning-based "Inception V3" paradigm by 11.92% and (b) four types of "conventional machine learning-based systems": k-NN, decision tree, support vector machine, and random forest by 55.13%, 28.36%, 15.35%, and 14.11%, respectively. The AI-based systems can potentially be useful in the early WD diagnosis. Graphical Abstract.

Morbidity and mortality from prostate cancer (PCa) are known to vary heavily based on socioeconomic and demographic risk factors. We sought to describe prescreening PSA (prostate-specific antigen) counseling (PPC) rates amongst male-to-female transgender (MtF-TG) patients and non-TG patients using the behavioral risk factor surveillance system (BRFSS).We used the survey data from 2014, 2016, and 2018 BRFSS and included respondents aged 40-79 years who completed the "PCa screening" and "sexual orientation and gender identity" modules. We analyzed differences in age, education level, income level, marital status, and race/ethnicity using Pearson's χA total of 175,383 respondents were included, of which 0.3% identified as MtF-TG. Overall, 62.4% of respondents reported undergoing PPC. On univariate analysis, PPC rates were lower among MtF-TG respondents when compared to the non-TG group (58.3% vs. 62.4%, p = 0.03). MtF-TG respondents were also more likely to report lower education level (p 0.01), lower-income level (p 0.01), and were less likely to be white (p 0.01) than non-TG respondents. However, multivariate analysis adjusting for these respondent features demonstrated an association between higher income and higher education levels with increased odds of PPC, but no association was demonstrated between MtF-TG status and PPC rates. PPC rates for the MtF-TG and non-TG populations did not change significantly over time.Although PPC was less frequently reported among MtF-TG respondents than in the non-TG group on univariate analysis, this association was not demonstrated when controlling for confounders, including education and income levels. Instead, on multivariate analysis, low education and income levels were more predictive of PPC rates. Further research is needed to ensure equivalent access to prescreening counseling for patients across the socioeconomic and gender identity spectrum.

Radiogenomics, a combination of “Radiomics” and “Genomics,” using Artificial Intelligence (AI) has recently emerged as the state-of-the-art science in precision medicine, especially in oncology care. Radiogenomics syndicates large-scale quantifiable data extracted from radiological medical images enveloped with personalized genomic phenotypes. It fabricates a prediction model through various AI methods to stratify the risk of patients, monitor therapeutic approaches, and assess clinical outcomes. It has recently shown tremendous achievements in prognosis, treatment planning, survival prediction, heterogeneity analysis, reoccurrence, and progression-free survival for human cancer study. Although AI has shown immense performance in oncology care in various clinical aspects, it has several challenges and limitations. The proposed review provides an overview of radiogenomics with the viewpoints on the role of AI in terms of its promises for computational as well as oncological aspects and offers achievements and opportunities in the era of precision medicine. The review also presents various recommendations to diminish these obstacles.

COVID-19 is a disease with multiple variants, and is quickly spreading throughout the world. It is crucial to identify patients who are suspected of having COVID-19 early, because the vaccine is not readily available in certain parts of the world.Lung computed tomography (CT) imaging can be used to diagnose COVID-19 as an alternative to the RT-PCR test in some cases. The occurrence of ground-glass opacities in the lung region is a characteristic of COVID-19 in chest CT scans, and these are daunting to locate and segment manually. The proposed study consists of a combination of solo deep learning (DL) and hybrid DL (HDL) models to tackle the lesion location and segmentation more quickly. One DL and four HDL models-namely, PSPNet, VGG-SegNet, ResNet-SegNet, VGG-UNet, and ResNet-UNet-were trained by an expert radiologist. The training scheme adopted a fivefold cross-validation strategy on a cohort of 3000 images selected from a set of 40 COVID-19-positive individuals.The proposed variability study uses tracings from two trained radiologists as part of the validation. Five artificial intelligence (AI) models were benchmarked against MedSeg. The best AI model, ResNet-UNet, was superior to MedSeg by 9% and 15% for Dice and Jaccard, respectively, when compared against MD 1, and by 4% and 8%, respectively, when compared against MD 2. Statistical tests-namely, the Mann-Whitney test, pairedThe AI models reliably located and segmented COVID-19 lesions in CT scans. The COVLIAS 1.0

Wilson's disease (WD) is caused by the excessive accumulation of copper in the brain and liver, leading to death if not diagnosed early. WD shows its prevalence as white matter hyperintensity (WMH) in MRI scans. It is challenging and tedious to classify WD against controls when comparing visually, primarily due to subtle differences in WMH. This Letter presents a computer-aided design-based automated classification strategy that uses optimised transfer learning (TL) utilising two novel paradigms known as (i) MobileNet and (ii) the Visual Geometric Group-19 (VGG-19). Further, the authors benchmark TL systems against a machine learning (ML) paradigm. Using four-fold augmentation, VGG-19 is superior to MobileNet demonstrating accuracy and area under the curve (AUC) pairs as 95.46 ± 7.70%, 0.932 (p < 0.0001) and 86.87 ± 2.23%, 0.871 (p < 0.0001), respectively. Further, MobileNet and VGG-19 showed an improvement of 3.4 and 13.5%, respectively, when benchmarked against the ML-based soft classifier – Random Forest.

Rheumatoid arthritis (RA) is a systemic chronic inflammatory disease that affects synovial joints and has various extra-articular manifestations, including atherosclerotic cardiovascular disease (CVD). Patients with RA experience a higher risk of CVD, leading to increased morbidity and mortality. Inflammation is a common phenomenon in RA and CVD. The pathophysiological association between these diseases is still not clear, and, thus, the risk assessment and detection of CVD in such patients is of clinical importance. Recently, artificial intelligence (AI) has gained prominence in advancing healthcare and, therefore, may further help to investigate the RA-CVD association. There are three aims of this review: (1) to summarize the three pathophysiological pathways that link RA to CVD; (2) to identify several traditional and carotid ultrasound image-based CVD risk calculators useful for RA patients, and (3) to understand the role of artificial intelligence in CVD risk assessment in RA patients. Our search strategy involves extensively searches in PubMed and Web of Science databases using search terms associated with CVD risk assessment in RA patients. A total of 120 peer-reviewed articles were screened for this review. We conclude that (a) two of the three pathways directly affect the atherosclerotic process, leading to heart injury, (b) carotid ultrasound image-based calculators have shown superior performance compared with conventional calculators, and (c) AI-based technologies in CVD risk assessment in RA patients are aggressively being adapted for routine practice of RA patients.

Background Vascular age (VA) has recently emerged for CVD risk assessment and can either be computed using conventional risk factors (CRF) or by using carotid intima-media thickness (cIMT) derived from carotid ultrasound (CUS). This study investigates a novel method of integrating both CRF and cIMT for estimating VA [so-called integrated VA (IVA)]. Further, the study analyzes and compares CVD/stroke risk using the Framingham Risk Score (FRS)-based risk calculator when adapting IVA against VA. Methods The system follows a four-step process: (I) VA using cIMT based using linear- regression (LR) model and its coefficients ; (II) VA prediction using ten CRF using a multivariate linear regression (MLR)-based model with gender adjustment ; (III) coefficients from the LR-based model and MLR-based model are combined using a linear model to predict the final IVA ; (IV) the final step consists of FRS-based risk stratification with IVA as inputs and benchmarked against FRS using conventional method of CA. Area- under-the-curve (AUC) is computed using IVA and benchmarked against CA while taking the response variable as a standardized combination of cIMT and glycated hemoglobin. Results The study recruited 648 patients, 202 were Japanese, 314 were Asian Indian, and 132 were Caucasians. Both left and right common carotid arteries (CCA) of all the population were scanned, thus a total of 1, 287 ultrasound scans. The 10- year FRS using IVA reported higher AUC (AUC =0.78) compared with 10- year FRS using CA (AUC =0.66) by ~18%. Conclusions IVA is an efficient biomarker for risk stratifications for patients in routine practice.

The objectives of this study are to (1) examine the “10-year cardiovascular risk” in the common carotid artery (CCA) versus carotid bulb using an integrated calculator called “AtheroEdge Composite Risk Score 2.0” (AECRS2.0) and (2) evaluate the performance of AECRS2.0 against “conventional cardiovascular risk calculators.” These objectives are met by measuring (1) image-based phenotypes and AECRS2.0 score computation and (2) performance evaluation of AECRS2.0 against 12 conventional cardiovascular risk calculators. The Asian–Indian cohort (n = 379) with type 2 diabetes mellitus (T2DM), chronic kidney disease (CKD), or hypertension were retrospectively analyzed by acquiring the 1516 carotid ultrasound scans (mean age: 55 ± 10.1 years, 67% males, ∼92% with T2DM, ∼83% with CKD [stage 1-5], and 87.5% with hypertension [stage 1-2]). The carotid bulb showed a higher 10-year cardiovascular risk compared to the CCA by 18% ( P < .0001). Patients with T2DM and/or CKD also followed a similar trend. The carotid bulb demonstrated a superior risk assessment compared to CCA in patients with T2DM and/or CKD by showing: (1) ∼13% better than CCA (0.93 vs 0.82, P = .0001) and (2) ∼29% better compared with 12 types of risk conventional calculators (0.93 vs 0.72, P = .06).

Nephrolithiasis patients undergo repeated imaging increasing their radiation risk. Guidelines recommend imaging following ureteroscopic lithotripsy (URSL), but not the modality or frequency of imaging. As such, we sought to elucidate common imaging practices following URSL and current knowledge of radiation exposure among endourologists. A web-based survey of Endourological Society members was used to evaluate knowledge and clinical practices regarding radiation exposure in post-URSL imaging. Respondents were grouped by years of experience ( = 10 years) and geographic location (USA or non-USA). An interaction term was included in the models to allow response rate to vary across experience level with geographic location and P values were derived from model main effects. A total of 309 respondents answered the survey out of the 1800 active members of the Endourological Society that received the survey (17.2% total response rate). 204 of the respondents were included in our analysis as fellowship trained endourologists that perform URSL with a mean of 13.3 years in practice (IQR 6.75, 20 years). Overall, routine postoperative imaging is performed by 92% of respondents: 97% in the USA and 88% outside the US (P = 0.143). 39% of respondents correctly estimated standard dose CT scan (SCT) dose, while 36% correctly estimated that of a low-dose CT scan (LCT). American urologists correctly identified the SCT radiation dose more often than non-US respondents (54% vs 32%, respectively) (P value = 0.004). Respondents with 30 kg/m2. We demonstrate a knowledge gap regarding radiation exposure during CT scans and the limitations of low-dose CT imaging. This gap is wider in non-US-based urologists and in those with over 10 years of experience. However, all urologists would benefit from improved education and clear guidelines regarding radiation exposure in nephrolithiasis patients.

Diabetes and atherosclerosis are the predominant causes of stroke and cardiovascular disease (CVD) both in low- and high-income countries. This is due to the lack of appropriate medical care or high medical costs. Low-cost 10-year preventive screening can be used for deciding an effective therapy to reduce the effects of atherosclerosis in diabetes patients. American College of Cardiology (ACC)/American Heart Association (AHA) recommended the use of 10-year risk calculators, before advising therapy. Conventional risk calculators are suboptimal in certain groups of patients because their stratification depends on (a) current blood biomarkers and (b) clinical phenotypes, such as age, hypertension, ethnicity, and sex. The focus of this review is on risk assessment using innovative composite risk scores that use conventional blood biomarkers combined with vascular image-based phenotypes. AtheroEdge™ tool is beneficial for low-moderate to high- moderate and low-risk to high-risk patients for the current and 10-year risk assessment that outperforms conventional risk calculators. The preventive screening tool that combines the image- based phenotypes with conventional risk factors can improve the 10-year cardiovascular/stroke risk assessment.

INTRODUCTION AND OBJECTIVE:Percutaneous nephrolithotomy (PCNL) is a combination of two procedures that first requires the establishment of percutaneous renal access followed by endoscopic stone fra...

Background: COVID-19 lung segmentation using Computed Tomography (CT) scans is important for the diagnosis of lung severity. The process of automated lung segmentation is challenging due to (a) CT radiation dosage and (b) ground-glass opacities caused by COVID-19. The lung segmentation methodologies proposed in 2020 were semi- or automated but not reliable, accurate, and user-friendly. The proposed study presents a COVID Lung Image Analysis System (COVLIAS 1.0, AtheroPoint™, Roseville, CA, USA) consisting of hybrid deep learning (HDL) models for lung segmentation. Methodology: The COVLIAS 1.0 consists of three methods based on solo deep learning (SDL) or hybrid deep learning (HDL). SegNet is proposed in the SDL category while VGG-SegNet and ResNet-SegNet are designed under the HDL paradigm. The three proposed AI approaches were benchmarked against the National Institute of Health (NIH)-based conventional segmentation model using fuzzy-connectedness. A cross-validation protocol with a 40:60 ratio between training and testing was designed, with 10% validation data. The ground truth (GT) was manually traced by a radiologist trained personnel. For performance evaluation, nine different criteria were selected to perform the evaluation of SDL or HDL lung segmentation regions and lungs long axis against GT. Results: Using the database of 5000 chest CT images (from 72 patients), COVLIAS 1.0 yielded AUC of ~0.96, ~0.97, ~0.98, and ~0.96 (p-value &lt, 0.001), respectively within 5% range of GT area, for SegNet, VGG-SegNet, ResNet-SegNet, and NIH. The mean Figure of Merit using four models (left and right lung) was above 94%. On benchmarking against the National Institute of Health (NIH) segmentation method, the proposed model demonstrated a 58% and 44% improvement in ResNet-SegNet, 52% and 36% improvement in VGG-SegNet for lung area, and lung long axis, respectively. The PE statistics performance was in the following order: ResNet-SegNet &gt, VGG-SegNet &gt, NIH &gt, SegNet. The HDL runs in &lt, 1 s on test data per image. Conclusions: The COVLIAS 1.0 system can be applied in real-time for radiology-based clinical settings.

Background: SpaceOAR (organ at risk) hydrogel is a hydrogel matrix injected into the perirectal space posterior to the prostate for the purpose of mitigating radiation dose (and hence side effects of radiation) on the rectum. Manufacturer descriptions state that this material is reabsorbed 3 to 6 months after injection. Case Discussion: We encountered a 75-year old male patient who underwent SpaceOAR injection in anticipation for primary external beam radiation treatment for intermediate risk prostate cancer (Gleason score 7 = 4 + 3, prostate specific Antigen [PSA] = 2.32, cT2a). After initiation of androgen deprivation, but before radiotherapy, the patient decided to no longer undergo radiation but rather elected to proceed with surgery. Based on the presence of the SpaceOAR, we delayed his surgery to 6 months after SpaceOAR injection to allow for absorption of the material. A preoperative MRI showed persistent hydrogel matrix in the perirectal space. We performed a robotic radical prostatectomy effectively despite the persistent SpaceOAR hydrogel by modifying our dissection closer to the prostate posteriorly. Conclusion: SpaceOAR hydrogel may alter patient anatomy even 6 months after deployment, however, robotic prostatectomy would be feasible with proper knowledge of anatomy and by following the proper dissection planes above the perirectal space.

Objectives: To report on our experience with the use of an evidence-based algorithm defining specific indications for stent omission (SO) after ureteroscopic lithotripsy (URSL), as stent placement has been associated with increased cost and morbidity and indications for SO in the setting of uncomplicated ureteroscopy have been proposed but remain vague. Patients and methods: Indications for SO were defined as per the attached figure, data from URSL procedures performed from January 2016 to September 2017 were collected. For procedures eligible for SO, preoperative and intraoperative factors were recorded including: stone burden, presence of preoperative stent, procedure time, access sheath use, and whether SO was performed. Morbidity data were reviewed including: postoperative events, patient telephone calls for bothersome symptoms, unplanned return visits, and admissions within 30 days. Results: In all, 250 URSL procedures were performed during the study period, and 106 (42.4%) were eligible for SO. SO was performed in 60 (24.0%) cases reflecting a 56.7% compliance with the algorithm. There were no readmissions or re-operations within 30 days for the SO group. Lower postoperative event rates were noted in the SO group (16.7% vs 34.8%, P = 0.03), unplanned return visits (8.3% vs 17.4%, P = 0.16) and 30-day readmission rates (0.0% vs 6.5%, P = 0.08) were also lower in the SO group, although they did not reach statistical significance. Analysis also demonstrated a protective effect of SO on unplanned return visits (odds ratio 0.43, 95% confidence interval 0.13–1.42, P = 0.17), although this was not statistically significant. No statistically significant associations were noted between postoperative events and stone burden, procedure time, or presence of preoperative stent. Conclusions: We provide an algorithm defining indications for SO. SO is safe in a significant portion of URSL procedures, and SO appears to decrease postoperative events when performed judiciously. Abbreviations: IQR: interquartile range; LUTS: lower urinary tract symptoms; OR, odds ratio; SO: stent omission; URSL: ureteroscopic lithotripsy; YAG: yttrium-aluminium-garnet

Objectives To characterize the perioperative morbidity of transurethral resection of bladder tumor (TURBT) in order to identify important determinants of both quality and cost in the delivery bladder cancer care. Methods We identified 24,100 patients aged 18-89 years who underwent TURBT from 2010 to 2015 in the National Surgical Quality Improvement Program database. Multivariable logistic regression was performed to evaluate the associations of patient features and tumor size ( 5 cm) with 30-day perioperative outcomes. Results Thirty-day postoperative complications occurred in 5.1% of patients, perioperative blood transfusion in 1.5% of patients, hospital readmission in 3.7% of patients, reoperation in 1.5% of patients, and mortality in 0.8% of patients. The most common reasons for readmission were bleeding (29%) and infectious (21%) complications. Although several patient features were associated with increased perioperative morbidity on multivariable analysis, including congestive heart failure, renal failure, higher American Society of Anesthesiology class, and dependent functional status, only larger tumor size was independently associated with increased risks of all perioperative endpoints. Conclusion Perioperative morbidity following TURBT is substantial and represents an important target for quality improvement. Extent of resection, patient functional status, and specific comorbidities are independently associated with increased risks of perioperative morbidity and mortality. These results have implications for patient counseling, perioperative management, and quality improvement programs.

To evaluate the potential of ALLN-177, an orally administered, oxalate-specific enzyme therapy to reduce urine oxalate (UOx) excretion in patients with secondary hyperoxaluria. Sixteen male and female subjects with both hyperoxaluria and a kidney stone history were enrolled in an open-label study. Subjects continued their usual diets and therapies. During a 3-day baseline period, two 24-h (24-h) urines were collected, followed by a 4-day treatment period with ALLN-177 (7,500 units/meal, 3 × day) when three 24-h urines were collected. The primary endpoint was the change in mean 24-h UOx from baseline. Safety assessments and 24-h dietary recalls were performed throughout. The study enrolled 5 subjects with enteric hyperoxaluria and 11 with idiopathic hyperoxaluria. ALLN-177 was well tolerated. Overall mean (SD) UOx decreased from 77.7 (55.9) at baseline to 63.7 (40.1) mg/24 h while on ALLN-177 therapy, with the mean reduction of 14 mg/24 h, (95% CI − 23.71, − 4.13). The calcium oxalate-relative urinary supersaturation ratio in the overall population decreased from a mean of 11.3 (5.7) to 8.8 (3.8) (− 2.8; 95% CI − 4.9, − 0.79). This difference was driven by oxalate reduction alone, but not any other urinary parameters. Mean daily dietary oxalate, calcium, and fluid intake recorded by frequent diet recall did not differ by study periods. ALLN-177 reduced 24-h UOx excretion, and was well tolerated. The results of this pilot study provided justification for further investigation of ALLN-177 in patients with secondary hyperoxaluria. Trial registration: Clinicaltrials.gov NCT02289755.

Chronic kidney disease (CKD) and cardiovascular disease (CVD) together result in an enormous burden on global healthcare. The estimated glomerular filtration rate (eGFR) is a well-established biomarker of CKD and is associated with adverse cardiac events. This review highlights the link between eGFR reduction and that of atherosclerosis progression, which increases the risk of adverse cardiovascular events. In general, CVD risk assessments are performed using conventional risk prediction models. However, since these conventional models were developed for a specific cohort with a unique risk profile and further these models do not consider atherosclerotic plaque-based phenotypes, therefore, such models can either underestimate or overestimate the risk of CVD events. This review examined the approaches used for CVD risk assessments in CKD patients using the concept of integrated risk factors. An integrated risk factor approach is one that combines the effect of conventional risk predictors and non-invasive carotid ultrasound image-based phenotypes. Furthermore, this review provided insights into novel artificial intelligence methods, such as machine learning and deep learning algorithms, to carry out accurate and automated CVD risk assessments and survival analyses in patients with CKD.

COVID-19 has infected 77.4 million people worldwide and has caused 1.7 million fatalities as of December 21, 2020. The primary cause of death due to COVID-19 is Acute Respiratory Distress Syndrome (ARDS). According to the World Health Organization (WHO), people who are at least 60 years old or have comorbidities that have primarily been targeted are at the highest risk from SARS-CoV-2. Medical imaging provides a non-invasive, touch-free, and relatively safer alternative tool for diagnosis during the current ongoing pandemic. Artificial intelligence (AI) scientists are developing several intelligent computer-aided diagnosis (CAD) tools in multiple imaging modalities, i.e., lung computed tomography (CT), chest X-rays, and lung ultrasounds. These AI tools assist the pulmonary and critical care clinicians through (a) faster detection of the presence of a virus, (b) classifying pneumonia types, and (c) measuring the severity of viral damage in COVID-19-infected patients. Thus, it is of the utmost importance to fully understand the requirements of for a fast and successful, and timely lung scans analysis. This narrative review first presents the pathological layout of the lungs in the COVID-19 scenario, followed by understanding and then explains the comorbid statistical distributions in the ARDS framework. The novelty of this review is the approach to classifying the AI models as per the by school of thought (SoTs), exhibiting based on segregation of techniques and their characteristics. The study also discusses the identification of AI models and its extension from non-ARDS lungs (pre-COVID-19) to ARDS lungs (post-COVID-19). Furthermore, it also presents AI workflow considerations of for medical imaging modalities in the COVID-19 framework. Finally, clinical AI design considerations will be discussed. We conclude that the design of the current existing AI models can be improved by considering comorbidity as an independent factor. Furthermore, ARDS post-processing clinical systems must involve include (i) the clinical validation and verification of AI-models, (ii) reliability and stability criteria, and (iii) easily adaptable, and (iv) generalization assessments of AI systems for their use in pulmonary, critical care, and radiological settings., Highlights • The review presents the PRISMA model for ARDS-based studies and eight stages of pathophysiology in ARDS due to COVID-19. • Selection of comorbidity studies, and their statistical distribution in ARDS framework. • Classification of the AI models as per the by school of thought (SoTs), based on segregation of techniques and their characteristics, and their salient features distinguishing pre-and post- COVID-19 lungs. • Critical Recommendations for Artificial Intelligent systems for safe and clinically effective design.

Purpose: Cystinuria is a genetic disorder with both autosomal recessive and incompletely dominant inheritance. The disorder disrupts cystine and other dibasic amino acid transport in proximal tubules of the kidney, resulting in recurrent kidney stone formation. Currently, there are no consensus guidelines on evaluation and management of this disease. This article represents the consensus of the author panel and will provide clinicians with a stepwise framework for evaluation and clinical management of patients with cystinuria based on evidence in the existing literature. Materials and Methods: A search of MEDLINE(®)/PubMed(®) and Cochrane databases was performed using the following key words: “cystine nephrolithiasis,” “cystinuria,” “penicillamine, cystine,” and “tiopronin, cystine.” In total, as of May 2018, these searches yielded 2335 articles, which were then evaluated for their relevance to the topic of evaluation and management of cystinuria. Evidence was evaluated by the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) system. Results: Twenty-five articles on the topic of cystinuria or cystine nephrolithiasis were deemed suitable for inclusion in this study. The literature supports a logical evaluation process and step-wise treatment approach beginning with conservative measures: fluid intake and dietary modification. If stone formation recurs, proceed to pharmacotherapeutic options by first alkalinizing the urine and then using cystine-binding thiol drugs. Conclusions: The proposed clinical pathways provide a framework for efficient evaluation and treatment of patients with cystinuria, which should improve overall outcomes of this rare, but highly recurrent, form of nephrolithiasis.

BACKGROUND: Statistically derived cardiovascular risk calculators (CVRC) that use conventional risk factors, generally underestimate or overestimate the risk of cardiovascular disease (CVD) or stroke events primarily due to lack of integration of plaque burden. This study investigates the role of machine learning (ML)-based CVD/stroke risk calculators (CVRC(ML)) and compares against statistically derived CVRC (CVRC(Stat)) based on (I) conventional factors or (II) combined conventional with plaque burden (integrated factors). METHODS: The proposed study is divided into 3 parts: (I) statistical calculator: initially, the 10-year CVD/stroke risk was computed using 13 types of CVRC(Stat) (without and with plaque burden) and binary risk stratification of the patients was performed using the predefined thresholds and risk classes; (II) ML calculator: using the same risk factors (without and with plaque burden), as adopted in 13 different CVRC(Stat), the patients were again risk-stratified using CVRC(ML) based on support vector machine (SVM) and finally; (III) both types of calculators were evaluated using AUC based on ROC analysis, which was computed using combination of predicted class and endpoint equivalent to CVD/stroke events. RESULTS: An Institutional Review Board approved 202 patients (156 males and 46 females) of Japanese ethnicity were recruited for this study with a mean age of 69±11 years. The AUC for 13 different types of CVRC(Stat) calculators were: AECRS2.0 (AUC 0.83, P

Cardiovascular diseases (CVDs) are the top ten leading causes of death worldwide. Atherosclerosis disease in the arteries is the main cause of the CVD, leading to myocardial infarction and stroke. The two primary image-based phenotypes used for monitoring the atherosclerosis burden is carotid intima-media thickness (cIMT) and plaque area (PA). Earlier segmentation and measurement methods were based on ad hoc conventional and semi- automated digital imaging solutions, which are unreliable, tedious, slow, and not robust. This study reviews the modern and automated methods such as artificial intelligence (AI)-based. Machine learning (ML) and deep learning (DL) can provide automated techniques in the detection and measurement of cIMT and PA from carotid vascular images. Both ML and DL techniques are examples of supervised learning, i.e., learn from “ground truth” images and transformation of test images that are not part of the training. This review summarizes (1) the evolution and impact of the fast-changing AI technology on cIMT/PA measurement, (2) the mathematical representations of ML/DL methods, and (3) segmentation approaches for cIMT/PA regions in carotid scans based for (a) region-of-interest detection and (b) lumen-intima and media-adventitia interface detection using ML/DL frameworks. AI-based methods for cIMT/PA segmentation have emerged for CVD/stroke risk monitoring and may expand to the recommended parameters for atherosclerosis assessment by carotid ultrasound.

OBJECTIVE To evaluate the personal protective equipment (PPE) utilized in common urologic procedures before and during the COVID-19 outbreak in the United States. As elective urologic procedures are being reduced to conserve resources, we sought to quantify the PPE used per case to determine the impact on potentially limited resources needed for protecting healthcare providers treating COVID-19 patients. METHODS An IRB approved retrospective analysis of all urologic procedures in March 2019 and March 2020 was performed. Additionally, all urologic procedures performed by vascular interventional radiology (VIR) in May 2019 and March 2020 were included in the analysis. Case length, surgical and operating room staff present and number of articles of PPE were quantified. Articles of PPE were defined as surgical bonnet/hat and mask, and disposable or reusable gown with 1 pair of surgical gloves. RESULTS Four hundred and thirty-seven urologic and VIR procedures were included in the analysis. The mean PPE per case varied significantly between endoscopic and robotic categories. Robotic assisted laparoscopic cystectomy required the most hats and masks (14.5 per case in March 2019) whereas percutaneous nephrostomy tube placement by VIR required the fewest (3.1 in May 2019 and March 2020). CONCLUSION PPE consumption varied significantly across urologic procedures. Robotic-assisted cases require the most PPE and percutaneous nephrostomy placement by VIR requires the fewest. While PPE shortages are currently being addressed national and internationally, our results provide a baseline benchmark for articles of PPE required should another pandemic or global disaster requiring careful attention to resource allocation occur in the future.