Off-clamp Versus On-clamp Robot-assisted Partial Nephrectomy: A Systematic Review and Quantitative Synthesis by the European Association of Urology Young Academic Urologists Renal Cancer Study Group.

Context: The superiority of off-clamp robot-assisted partial nephrectomy (RAPN) over the on-clamp technique has recently been questioned by randomized controlled trials comparing the two techniques. Objective: To systematically review the recent literature and perform a quantitative synthesis of data on the comparison of off-clamp versus off-clamp hilar control during RAPN. Evidence acquisition: A systematic search was performed in the PubMed, Embase, Web of Science, and Scopus databases for studies comparing off-clamp versus on-clamp RAPN in terms of perioperative and functional outcomes. The study protocol was registered in the PROSPERO database (CRD42023413160). Only prospective randomized controlled trials and retrospective matched observational studies were included. The primary outcome of the study was the percentage decrease in the estimated glomerular filtration rate (eGFR). Evidence synthesis: A total of 11 studies were included involving a total of 2483 patients (944 patients in the off-clamp and 1539 patients in the on-clamp group). There was no difference between the two groups in the percentage decline in eGFR (mean difference [MD] 0.04%, 95% confidence interval [CI] -3.7% to 3.86%; p = 0.98). There were so significant differences between the groups for length of hospital stay (p = 0.56), complications (p = 0.08), conversion to open or radical surgery (p = 0.18), estimated blood loss (p = 0.06), or need for blood transfusion (p = 0.07). The operative time was shorter in the off-clamp group (MD-21.89 min, 95% CI -42.5 to -1.27; p = 0.04) but after sensitivity analysis the difference was no longer statistically significant (p = 0.15). The positive surgical margin rate was significantly lower in the off-clamp group (odds ratio 0.6, 95% CI 0.39-0.91; p = 0.02). Conclusions: Our review revealed no clinically relevant differences in perioperative and functional outcomes between off-clamp and on-clamp RAPN. Patient summary: In this review, we compared the two methods of controlling the kidney blood vessels during robot-assisted surgery to remove part of the kidney. We noted that there was no difference between the two groups for outcomes such as complications and the decrease in kidney function after surgery.

DermAI 1.0: A Robust, Generalized, and Novel Attention-Enabled Ensemble-Based Transfer Learning Paradigm for Multiclass Classification of Skin Lesion Images.

Skin lesion classification plays a crucial role in dermatology, aiding in the early detection, diagnosis, and management of life-threatening malignant lesions. However, standalone transfer learning (TL) models failed to deliver optimal performance. In this study, we present an attention-enabled ensemble-based deep learning technique, a powerful, novel, and generalized method for extracting features for the classification of skin lesions. This technique holds significant promise in enhancing diagnostic accuracy by using seven pre-trained TL models for classification. Six ensemble-based DL (EBDL) models were created using stacking, softmax voting, and weighted average techniques. Furthermore, we investigated the attention mechanism as an effective paradigm and created seven attention-enabled transfer learning (aeTL) models before branching out to construct three attention-enabled ensemble-based DL (aeEBDL) models to create a reliable, adaptive, and generalized paradigm. The mean accuracy of the TL models is 95.30%, and the use of an ensemble-based paradigm increased it by 4.22%, to 99.52%. The aeTL models' performance was superior to the TL models in accuracy by 3.01%, and aeEBDL models outperformed aeTL models by 1.29%. Statistical tests show significant p-value and Kappa coefficient along with a 99.6% reliability index for the aeEBDL models. The approach is highly effective and generalized for the classification of skin lesions.

Off-clamp Versus On-clamp Robot-assisted Partial Nephrectomy: A Propensity-matched Analysis.

BACKGROUND: Partial nephrectomy is the preferred treatment option for the management of small renal masses. On-clamp partial nephrectomy is associated with a risk of ischemia and a greater loss of postoperative renal function, while the off-clamp procedure decreases the duration of renal ischemia, leading to better renal function preservation. However, the efficacy of the off- versus on-clamp partial nephrectomy for renal function preservation remains debatable. OBJECTIVE: To compare perioperative and functional outcomes following off- and on-clamp robot-assisted partial nephrectomy (RAPN). DESIGN, SETTING, AND PARTICIPANTS: This study used the prospective multinational collaborative Vattikuti Collective Quality Initiative (VCQI) database for RAPN. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: The primary objective of this study was the comparison of perioperative and functional outcomes between patients who underwent off- and on-clamp RAPN. Propensity scores were calculated for age, sex, body mass index (BMI), renal nephrometry score (RNS) and preoperative estimated glomerular filtration rate (eGFR). RESULTS AND LIMITATIONS: Of the 2114 patients, 210 had undergone off-clamp RAPN and others on-clamp procedure. Propensity matching was possible for 205 patients in a 1:1 ratio. After matching, the two groups were comparable for age, sex, BMI, tumor size, multifocality, tumor side, face of tumor, RNS, polar location of the tumor, surgical access, and preoperative hemoglobin, creatinine, and eGFR. There was no difference between the two groups for intraoperative (4.8% vs 5.3%, p = 0.823) and postoperative (11.2% vs 8.3%, p = 0.318) complications. Need for blood transfusion (2.9% vs 0, p = 0.030) and conversion to radical nephrectomy (10.2% vs 1%, p < 0.001) were significantly higher in the off-clamp group. At the last follow-up, there was no difference between the two groups for creatinine and eGFR. The mean fall in eGFR at the last follow-up compared with that at baseline was equivalent between the two groups (-16.0 vs -17.3 ml/min, p = 0.985). CONCLUSIONS: Off-clamp RAPN does not result in better renal functional preservation. Alternatively, it may be associated with increased rates of conversion to radical nephrectomy and need for blood transfusion. PATIENT SUMMARY: With this multicentric study, we noted that performing robotic partial nephrectomy without clamping the blood supply to the kidney is not associated with better preservation of renal function. However, off-clamp partial nephrectomy is associated with increased rates of conversion to radical nephrectomy and blood transfusion.

Robot-assisted partial nephrectomy in morbidly obese patients: a VCQI database study.

To compare perioperative outcomes following robot-assisted partial nephrectomy (RAPN) in patients with morbid obesity (body mass index (BMI > 40 kg/m2)) and non-obese patients. Using the Vattikuti Collective quality initiative (VCQI) database for RAPN, data for morbidly obese and non-obese patients was obtained. Propensity scores were calculated for two treatment groups (morbidly obese vs. non-obese) for the following variables i.e. age, sex, tumor size, RNS, surgical access (retroperitoneal/transperitoneal) and estimated glomerular filtration rate (eGFR) to ensure comparability. The primary outcome for the study was comparison of trifecta between the two groups. In this study, 158 morbidly obese patients were matched with 158 non-obese patients undergoing RAPN. Two groups matched well for age, sex, tumor size, eGFR and RNS. There was no difference between two groups for ischemia time, blood loss, blood transfusion, conversion to radical nephrectomy, length of stay, intraoperative and postoperative complications. Operative time was longer in morbidly obese patients (median 210 min vs. 120 min, p = 0.000). On pathological analysis, malignant tumors were more likely in the morbidly obese group (83.1% vs.73.4%, p = 0.018). Trifecta outcomes were comparable between the two groups (60.1% vs. 63.3%, p = 0.563). The Median duration of follow-up was 12 months (1-96 months). The morbidly obese group had significantly higher day one creatinine (1.25 ± 0.7 vs. 1.07 ± 0.37, p = 0.001) and significantly lower day one eGFR (62.1 ± 19 vs. 69.2 ± 21, p = 0.018). However, there was no difference between the two groups for the last follow-up creatinine and eGFR. RAPN in morbidly obese patients is associated with equivalent perioperative outcomes compared to non-obese patients.

Trifecta and pentafecta outcomes following robot-assisted partial nephrectomy in a multi-institutional cohort of Indian patients.

Introduction: The literature on studies reporting trifecta or pentafecta outcomes following robot-assisted partial nephrectomy (RAPN) in Indian patients is limited. The primary aim of this study was to report and evaluate the factors predicting trifecta and pentafecta outcomes following RAPN in Indian patients using the multicentric Vattikuti collective quality initiative (VCQI) database. Methods: From the VCQI database for patients who underwent RAPN, data for Indian patients were extracted and analyzed for factors predicting the achievement of trifecta and pentafecta following RAPN. Trifecta was defined as the absence of complications, negative surgical margins, and warm ischemia period shorter than 25 min or zero ischemia. Pentafecta covers all the trifecta criteria as well as >90% preservation of estimated glomerular filtration rate (eGFR) and no stage upgrade of chronic kidney disease at 12 months. Results: In this study, among 614 patients, the trifecta was achieved in 374 patients (60.9%) and pentafecta was achieved in 24.2% of the patients. Patients who achieved trifecta had significantly higher mean age (54.1 vs. 51.0 years, P = 0.005), body mass index (BMI) (26.7 vs. 26.03 kg/m2, P = 0.022), and smaller tumor size (38.6 vs. 41.4 mm, P = 0.028). The preoperative eGFR (84.2 vs. 91.9 ml/min, P = 0.012) and renal nephrometry score (RNS) (6.96 vs. 7.87, P ≤ 0.0001) were significantly lower in the trifecta group. Comparing patients who achieved pentafecta to those who did not, we noted a statistically significant difference between the two groups for tumor size (36.1 vs. 41.5 mm, P = 0.017) and RNS (6.6 vs. 7.7, P = 0.0001). On multivariate analysis, BMI and RNS were associated with trifecta outcomes. Similarly, only RNS was identified as an independent predictor of pentafecta. Conclusions: RNS and BMI were independent predictors of the trifecta. At the same time, RNS was identified as an independent predictor of pentafecta following RAPN.

Development and Validation of a Nomogram Predicting Intraoperative Adverse Events During Robot-assisted Partial Nephrectomy.

BACKGROUND: Ability to predict the risk of intraoperative adverse events (IOAEs) for patients undergoing partial nephrectomy (PN) can be of great clinical significance. OBJECTIVE: To develop and internally validate a preoperative nomogram predicting IOAEs for robot-assisted PN (RAPN). DESIGN, SETTING, AND PARTICIPANTS: In this observational study, data for demographic, preoperative, and postoperative variables for patients who underwent RAPN were extracted from the Vattikuti Collective Quality Initiative (VCQI) database. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: IOAEs were defined as the occurrence of intraoperative surgical complications, blood transfusion, or conversion to open surgery/radical nephrectomy. Backward stepwise logistic regression analysis was used to identify predictors of IOAEs. The nomogram was validated using bootstrapping, the area under the receiver operating characteristic curve (AUC), and the goodness of fit. Decision curve analysis (DCA) was used to determine the clinical utility of the model. RESULTS AND LIMITATIONS: Among the 2114 patients in the study cohort, IOAEs were noted in 158 (7.5%). Multivariable analysis identified five variables as independent predictors of IOAEs: RENAL nephrometry score (odds ratio [OR] 1.13, 95% confidence interval [CI] 1.02-1.25); clinical tumor size (OR 1.01, 95% CI 1.001-1.024); PN indication as absolute versus elective (OR 3.9, 95% CI 2.6-5.7) and relative versus elective (OR 4.2, 95% CI 2.2-8); Charlson comorbidity index (OR 1.17, 95% CI 1.05-1.30); and multifocal tumors (OR 8.8, 95% CI 5.4-14.1). A nomogram was developed using these five variables. The model was internally valid on bootstrapping and goodness of fit. The AUC estimated was 0.76 (95% CI 0.72-0.80). DCA revealed that the model was clinically useful at threshold probabilities >5%. Limitations include the lack of external validation and selection bias. CONCLUSIONS: We developed and internally validated a nomogram predicting IOAEs during RAPN. PATIENT SUMMARY: We developed a preoperative model than can predict complications that might occur during robotic surgery for partial removal of a kidney. Tests showed that our model is fairly accurate and it could be useful in identifying patients with kidney cancer for whom this type of surgery is suitable.

Economics of Artificial Intelligence in Healthcare: Diagnosis vs. Treatment.

Motivation: The price of medical treatment continues to rise due to (i) an increasing population; (ii) an aging human growth; (iii) disease prevalence; (iv) a rise in the frequency of patients that utilize health care services; and (v) increase in the price. Objective: Artificial Intelligence (AI) is already well-known for its superiority in various healthcare applications, including the segmentation of lesions in images, speech recognition, smartphone personal assistants, navigation, ride-sharing apps, and many more. Our study is based on two hypotheses: (i) AI offers more economic solutions compared to conventional methods; (ii) AI treatment offers stronger economics compared to AI diagnosis. This novel study aims to evaluate AI technology in the context of healthcare costs, namely in the areas of diagnosis and treatment, and then compare it to the traditional or non-AI-based approaches. Methodology: PRISMA was used to select the best 200 studies for AI in healthcare with a primary focus on cost reduction, especially towards diagnosis and treatment. We defined the diagnosis and treatment architectures, investigated their characteristics, and categorized the roles that AI plays in the diagnostic and therapeutic paradigms. We experimented with various combinations of different assumptions by integrating AI and then comparing it against conventional costs. Lastly, we dwell on three powerful future concepts of AI, namely, pruning, bias, explainability, and regulatory approvals of AI systems. Conclusions: The model shows tremendous cost savings using AI tools in diagnosis and treatment. The economics of AI can be improved by incorporating pruning, reduction in AI bias, explainability, and regulatory approvals.

Perioperative outcomes following robot-assisted partial nephrectomy for complex renal masses: A Vattikuti Collective Quality Initiative database study.

Introduction: Outcomes of robot-assisted partial nephrectomy (RAPN) depend on tumor complexity, surgeon experience and patient profile among other variables. We aimed to study the perioperative outcomes of RAPN for patients with complex renal masses using the Vattikuti Collective Quality Initiative (VCQI) database that allowed evaluation of multinational data. Methods: From the VCQI, we extracted data for all the patients who underwent RAPN with preoperative aspects and dimensions used for an anatomical (PADUA) score of ≥10. Multivariate logistic regression was conducted to ascertain predictors of trifecta (absence of complications, negative surgical margins, and warm ischemia times [WIT] <25 min or zero ischemia) outcomes. Results: Of 3,801 patients, 514 with PADUA scores ≥10 were included. The median operative time, WIT, and blood loss were 173 (range 45-546) min, 21 (range 0-55) min, and 150 (range 50-3500) ml, respectively. Intraoperative complications and blood transfusions were reported in 2.1% and 6%, respectively. In 8.8% of the patients, postoperative complications were noted, and surgical margins were positive in 10.3% of the patients. Trifecta could be achieved in 60.7% of patients. Clinical tumor size, duration of surgery, WIT, and complication rates were significantly higher in the group with a high (12 or 13) PADUA score while the trifecta was significantly lower in this group (48.4%). On multivariate analysis, surgical approach (retroperitoneal vs. transperitoneal) and high PADUA score (12/13) were identified as predictors of the trifecta outcomes. Conclusion: RAPN may be a reasonable surgical option for patients with complex renal masses with acceptable perioperative outcomes.

Cardiovascular/Stroke Risk Stratification in Diabetic Foot Infection Patients Using Deep Learning-Based Artificial Intelligence: An Investigative Study.

A diabetic foot infection (DFI) is among the most serious, incurable, and costly to treat conditions. The presence of a DFI renders machine learning (ML) systems extremely nonlinear, posing difficulties in CVD/stroke risk stratification. In addition, there is a limited number of well-explained ML paradigms due to comorbidity, sample size limits, and weak scientific and clinical validation methodologies. Deep neural networks (DNN) are potent machines for learning that generalize nonlinear situations. The objective of this article is to propose a novel investigation of deep learning (DL) solutions for predicting CVD/stroke risk in DFI patients. The Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) search strategy was used for the selection of 207 studies. We hypothesize that a DFI is responsible for increased morbidity and mortality due to the worsening of atherosclerotic disease and affecting coronary artery disease (CAD). Since surrogate biomarkers for CAD, such as carotid artery disease, can be used for monitoring CVD, we can thus use a DL-based model, namely, Long Short-Term Memory (LSTM) and Recurrent Neural Networks (RNN) for CVD/stroke risk prediction in DFI patients, which combines covariates such as office and laboratory-based biomarkers, carotid ultrasound image phenotype (CUSIP) lesions, along with the DFI severity. We confirmed the viability of CVD/stroke risk stratification in the DFI patients. Strong designs were found in the research of the DL architectures for CVD/stroke risk stratification. Finally, we analyzed the AI bias and proposed strategies for the early diagnosis of CVD/stroke in DFI patients. Since DFI patients have an aggressive atherosclerotic disease, leading to prominent CVD/stroke risk, we, therefore, conclude that the DL paradigm is very effective for predicting the risk of CVD/stroke in DFI patients.

Perioperative outcomes following robot-assisted partial nephrectomy in elderly patients.

OBJECTIVE: To compare perioperative outcomes following robot-assisted partial nephrectomy (RAPN) in patients with age ≥ 70 years to age < 70 years. METHODS: Using Vattikuti Collective quality initiative (VCQI) database for RAPN we compared perioperative outcomes following RAPN between the two age groups. Primary outcome of the study was to compare trifecta outcomes between the two groups. Propensity matching using nearest neighbourhood method was performed with trifecta as primary outcome for sex, body mass index (BMI), solitary kidney, tumor size and Renal nephrometery score (RNS). RESULTS: Group A (age ≥ 70 years) included 461 patients whereas group B included 1932 patients. Before matching the two groups were statistically different for RNS and solitary kidney rates. After propensity matching, the two groups were comparable for baselines characteristics such as BMI, tumor size, clinical symptoms, tumor side, face of tumor, solitary kidney and tumor complexity. Among the perioperative outcome parameters there was no difference between two groups for operative time, blood loss, intraoperative transfusion, intraoperative complications, need for radical nephrectomy, positive margins and trifecta rates. Warm ischemia time was significantly longer in the younger age group (18.1 min vs. 16.3 min, p = 0.003). Perioperative complications were significantly higher in the older age group (11.8% vs. 7.7%, p = 0.041). However, there was no difference between the two groups for major complications. CONCLUSION: RAPN in well-selected elderly patients is associated with comparable trifecta outcomes with acceptable perioperative morbidity.

Development and internal validation of preoperative and postoperative nomograms predicting quadrifecta outcomes following robotic radical prostatectomy.

Introduction: Literature on the factors predicting functional and oncological outcomes following robot-assisted radical prostatectomy (RARP) is sparse for the Indian population. Hence, the primary objective of this study was to develop preoperative and postoperative nomograms predicting these outcomes in patients with prostate cancer undergoing RARP. Methods: This retrospective analysis identified the predictors of quadrifecta outcomes, i.e., the patients who did not have complications, were continent, had negative surgical margins, and were biochemical recurrence free with at least 1 year of follow-up following RARP. We excluded the return of sexual potency as the majority of the patients in our series were sexually inactive preoperatively. We used the backward stepwise logistic regression analysis method to identify the predictors of quadrifecta. Preoperative and postoperative nomograms using these predictors were developed and validated with bootstrapping, goodness of fit, calibration plot, decision curve analysis (DCA), and theits receiver operating characteristic (ROC) analysis. Results: Of the 688 patients who underwent RARP, 399 were included in this study, and 123 (30.8%) of these achieved the quadrifecta outcomes. Preoperative nomogram was developed using four variables, i.e., prostate-specific antigen (PSA), Charlson Comorbidity Index (CCI), biopsy Gleason score, and clinical stage. Postoperative nomogram included PSA, CCI, pathological tumor stage, tumor grade, and positive lymph node. Both the models were internally valid on bootstrapping, calibration plots, and goodness of fit. On the ROC analysis, preoperative and postoperative nomograms had an area under the curve of 0.71 and 0.79, respectively. On the DCA, at a threshold probability of 5%, both the models showed a net benefit. Conclusions: We developed and validated accurate nomograms for predicting quadrifecta outcomes following RARP for the Indian population.

Vascular Implications of COVID-19: Role of Radiological Imaging, Artificial Intelligence, and Tissue Characterization: A Special Report.

The SARS-CoV-2 virus has caused a pandemic, infecting nearly 80 million people worldwide, with mortality exceeding six million. The average survival span is just 14 days from the time the symptoms become aggressive. The present study delineates the deep-driven vascular damage in the pulmonary, renal, coronary, and carotid vessels due to SARS-CoV-2. This special report addresses an important gap in the literature in understanding (i) the pathophysiology of vascular damage and the role of medical imaging in the visualization of the damage caused by SARS-CoV-2, and (ii) further understanding the severity of COVID-19 using artificial intelligence (AI)-based tissue characterization (TC). PRISMA was used to select 296 studies for AI-based TC. Radiological imaging techniques such as magnetic resonance imaging (MRI), computed tomography (CT), and ultrasound were selected for imaging of the vasculature infected by COVID-19. Four kinds of hypotheses are presented for showing the vascular damage in radiological images due to COVID-19. Three kinds of AI models, namely, machine learning, deep learning, and transfer learning, are used for TC. Further, the study presents recommendations for improving AI-based architectures for vascular studies. We conclude that the process of vascular damage due to COVID-19 has similarities across vessel types, even though it results in multi-organ dysfunction. Although the mortality rate is ~2% of those infected, the long-term effect of COVID-19 needs monitoring to avoid deaths. AI seems to be penetrating the health care industry at warp speed, and we expect to see an emerging role in patient care, reduce the mortality and morbidity rate.

Deep Learning Paradigm for Cardiovascular Disease/Stroke Risk Stratification in Parkinson's Disease Affected by COVID-19: A Narrative Review.

Background and Motivation: Parkinson's disease (PD) is one of the most serious, non-curable, and expensive to treat. Recently, machine learning (ML) has shown to be able to predict cardiovascular/stroke risk in PD patients. The presence of COVID-19 causes the ML systems to become severely non-linear and poses challenges in cardiovascular/stroke risk stratification. Further, due to comorbidity, sample size constraints, and poor scientific and clinical validation techniques, there have been no well-explained ML paradigms. Deep neural networks are powerful learning machines that generalize non-linear conditions. This study presents a novel investigation of deep learning (DL) solutions for CVD/stroke risk prediction in PD patients affected by the COVID-19 framework. Method: The PRISMA search strategy was used for the selection of 292 studies closely associated with the effect of PD on CVD risk in the COVID-19 framework. We study the hypothesis that PD in the presence of COVID-19 can cause more harm to the heart and brain than in non-COVID-19 conditions. COVID-19 lung damage severity can be used as a covariate during DL training model designs. We, therefore, propose a DL model for the estimation of, (i) COVID-19 lesions in computed tomography (CT) scans and (ii) combining the covariates of PD, COVID-19 lesions, office and laboratory arterial atherosclerotic image-based biomarkers, and medicine usage for the PD patients for the design of DL point-based models for CVD/stroke risk stratification. Results: We validated the feasibility of CVD/stroke risk stratification in PD patients in the presence of a COVID-19 environment and this was also verified. DL architectures like long short-term memory (LSTM), and recurrent neural network (RNN) were studied for CVD/stroke risk stratification showing powerful designs. Lastly, we examined the artificial intelligence bias and provided recommendations for early detection of CVD/stroke in PD patients in the presence of COVID-19. Conclusion: The DL is a very powerful tool for predicting CVD/stroke risk in PD patients affected by COVID-19.

Comparison of perioperative outcomes following transperitoneal versus retroperitoneal robot-assisted partial nephrectomy: a propensity-matched analysis of VCQI database.

OBJECTIVE: To compare perioperative outcomes following retroperitoneal robot-assisted partial nephrectomy (RPRAPN) and transperitoneal robot-assisted partial nephrectomy (TPRAPN). METHODS: With this Vattikuti Collective Quality Initiative (VCQI) database, study propensity scores were calculated according to the surgical access (TPRAPN and RPRAPN) for the following independent variables, i.e., age, sex, side of the surgery, RENAL nephrometry scores (RNS), estimated glomerular filtration rate (eGFR) and serum creatinine. The study's primary outcome was the comparison of trifecta between the two groups. RESULTS: In this study, 309 patients who underwent RPRAPN were matched with 309 patients who underwent TPRAPN. The two groups matched well for age, sex, tumor side, polar location of the tumor, RNS, preoperative creatinine and eGFR. Operative time and warm ischemia time were significantly shorter with RPRAPN. Intraoperative blood loss and need for blood transfusion were lower with RPRAPN. There was a significantly higher number of intraoperative complications with RPRAPN. However, there was no difference in the two groups for postoperative complications. Trifecta outcomes were better with RPRAPN (70.2% vs. 53%, p < 0.0001) compared to TPRAPN. We noted no significant change in overall results when controlled for tumor location (anteriorly or posteriorly). The surgical approach, tumor size and RNS were identified as independent predictors of trifecta on multivariate analysis. CONCLUSION: RPRAPN is associated with superior perioperative outcomes in well-selected patients compared to TPRAPN. However, the data for the retroperitoneal approach were contributed by a few centers with greater experience with this technique, thus limiting the generalizability of the results of this study.

Cardiovascular Risk Stratification in Diabetic Retinopathy via Atherosclerotic Pathway in COVID-19/Non-COVID-19 Frameworks Using Artificial Intelligence Paradigm: A Narrative Review.

Diabetes is one of the main causes of the rising cases of blindness in adults. This microvascular complication of diabetes is termed diabetic retinopathy (DR) and is associated with an expanding risk of cardiovascular events in diabetes patients. DR, in its various forms, is seen to be a powerful indicator of atherosclerosis. Further, the macrovascular complication of diabetes leads to coronary artery disease (CAD). Thus, the timely identification of cardiovascular disease (CVD) complications in DR patients is of utmost importance. Since CAD risk assessment is expensive for low-income countries, it is important to look for surrogate biomarkers for risk stratification of CVD in DR patients. Due to the common genetic makeup between the coronary and carotid arteries, low-cost, high-resolution imaging such as carotid B-mode ultrasound (US) can be used for arterial tissue characterization and risk stratification in DR patients. The advent of artificial intelligence (AI) techniques has facilitated the handling of large cohorts in a big data framework to identify atherosclerotic plaque features in arterial ultrasound. This enables timely CVD risk assessment and risk stratification of patients with DR. Thus, this review focuses on understanding the pathophysiology of DR, retinal and CAD imaging, the role of surrogate markers for CVD, and finally, the CVD risk stratification of DR patients. The review shows a step-by-step cyclic activity of how diabetes and atherosclerotic disease cause DR, leading to the worsening of CVD. We propose a solution to how AI can help in the identification of CVD risk. Lastly, we analyze the role of DR/CVD in the COVID-19 framework.

Cardiovascular/Stroke Risk Assessment in Patients with Erectile Dysfunction-A Role of Carotid Wall Arterial Imaging and Plaque Tissue Characterization Using Artificial Intelligence Paradigm: A Narrative Review.

PURPOSE: The role of erectile dysfunction (ED) has recently shown an association with the risk of stroke and coronary heart disease (CHD) via the atherosclerotic pathway. Cardiovascular disease (CVD)/stroke risk has been widely understood with the help of carotid artery disease (CTAD), a surrogate biomarker for CHD. The proposed study emphasizes artificial intelligence-based frameworks such as machine learning (ML) and deep learning (DL) that can accurately predict the severity of CVD/stroke risk using carotid wall arterial imaging in ED patients. METHODS: Using the PRISMA model, 231 of the best studies were selected. The proposed study mainly consists of two components: (i) the pathophysiology of ED and its link with coronary artery disease (COAD) and CHD in the ED framework and (ii) the ultrasonic-image morphological changes in the carotid arterial walls by quantifying the wall parameters and the characterization of the wall tissue by adapting the ML/DL-based methods, both for the prediction of the severity of CVD risk. The proposed study analyzes the hypothesis that ML/DL can lead to an accurate and early diagnosis of the CVD/stroke risk in ED patients. Our finding suggests that the routine ED patient practice can be amended for ML/DL-based CVD/stroke risk assessment using carotid wall arterial imaging leading to fast, reliable, and accurate CVD/stroke risk stratification. SUMMARY: We conclude that ML and DL methods are very powerful tools for the characterization of CVD/stroke in patients with varying ED conditions. We anticipate a rapid growth of these tools for early and better CVD/stroke risk management in ED patients.

Diagnostic performance of minichromosome maintenance 5 (MCM5) in bladder cancer: A systematic review and meta-analysis.

Primary objective of this study was to determine diagnostic accuracy of minichromosome maintenance 5 (MCM5) protein in patients with bladder cancer (BC). In this review, we searched electronic databases to identify studies on the diagnostic accuracy of MCM5 in patients with BC. We pooled sensitivity and specificities using DerSimonian-Laird random-effect analysis and followed PRISMA guidelines while conducting this review (CRD42021255609). In this meta-analysis, eight prospective studies with 5,114 patients were included. Pooled sensitivity and specificity for MCM5 in predicting BC were 0.66 (0.56-0.75) and 0.72 (0.61-0.81). Subgroup analysis of five studies with 3,000 patients using ADXBLADDER revealed sensitivity and specificity of 0.61 (0.49-0.71) and 0.67 (0.51-0.80). Pooled sensitivity and specificity for primary diagnosis of BC were 0.74 (0.64-0.82) and 0.78 (0.67-0.86), respectively. For BC surveillance, pooled sensitivity and specificity were 0.58 (0.45-0.69) and 0.61 (0.48-0.73), respectively. Pooled sensitivities for low and high-grade tumors were 0.50 (0.36-0.64) and 0.79 (0.68-0.87), respectively. Pooled specificities for low and high-grade tumors were 0.79 (0.63-0.90) and 0.82 (0.65-0.92). MCM5 has an overall moderate diagnostic accuracy for detecting BC. Subgroup analysis revealed good diagnostic performance in patients with high-grade tumors and primary diagnosis of symptomatic patients.