Milap Shah

Background: The aim was to analyze the current state of deep learning (DL)-based prostate cancer (PCa) diagnosis with a focus on magnetic resonance (MR) prostate reconstruction; PCa detection/stratification/reconstruction; positron emission tomography/computed tomography (PET/CT); androgen deprivation therapy (ADT); prostate biopsy; associated challenges and their clinical implications. Methods: A search of the PubMed database was conducted based on the inclusion and exclusion criteria for the use of DL methods within the abovementioned areas. Results: A total of 784 articles were found, of which, 64 were included. Reconstruction of the prostate, the detection and stratification of prostate cancer, the reconstruction of prostate cancer, and diagnosis on PET/CT, ADT, and biopsy were analyzed in 21, 22, 6, 7, 2, and 6 studies, respectively. Among studies describing DL use for MR-based purposes, datasets with magnetic field power of 3 T, 1.5 T, and 3/1.5 T were used in 18/19/5, 0/1/0, and 3/2/1 studies, respectively, of 6/7 studies analyzing DL for PET/CT diagnosis which used data from a single institution. Among the radiotracers, [⁶⁸Ga]Ga-PSMA-11, [¹⁸F]DCFPyl, and [¹⁸F]PSMA-1007 were used in 5, 1, and 1 study, respectively. Only two studies that analyzed DL in the context of DT met the inclusion criteria. Both were performed with a single-institution dataset with only manual labeling of training data. Three studies, each analyzing DL for prostate biopsy, were performed with single- and multi-institutional datasets. TeUS, TRUS, and MRI were used as input modalities in two, three, and one study, respectively. Conclusion: DL models in prostate cancer diagnosis show promise but are not yet ready for clinical use due to variability in methods, labels, and evaluation criteria. Conducting additional research while acknowledging all the limitations outlined is crucial for reinforcing the utility and effectiveness of DL-based models in clinical settings.

Background: The aims were to describe a software-based reconstruction of the patient-specific kidney cavity intraluminal appearance via a head-mounted device and to estimate its feasibility for training novices. Materials and methods: In total, 15 novices were recruited. Each novice was shown a three-dimensional reconstruction of a patient’s computed tomography scan, whose kidney was printed. They then joined the surgeon in the operating room and assisted them in detecting the stone during flexible ureteroscopy on the printed model. Then, each participant did a 7-day virtual reality (VR) study followed by virtual navigation of the printed kidney model and came to the operating room to help the surgeon with ureteroscope navigation. The length of the procedure and the number of attempts to find the targeted calyx were compared. Results: With VR training, the length of the procedure (p = 0.0001) and the number of small calyces that were incorrectly identified as containing stones were significantly reduced (p = 0.0001). All the novices become highly motivated to improve their endourological skills further. Participants noticed minimal values for nausea and for disorientation. However, oculomotor-related side effects were defined as significant. Five specialists noticed a good similarity between the VR kidney cavity representation and the real picture, strengthening the potential for the novice’s education via VR training. Conclusions: Virtual reality simulation allowed for improved spatial orientation within the kidney cavity by the novices and could be a valuable option for future endourological training and curricula.

Artificial intelligence (AI) is an emerging technological system that provides a platform to manage and analyze data by emulating human cognitive functions with greater accuracy, revolutionizing patient care and introducing a paradigm shift to the healthcare industry. The purpose of this study is to identify the underlying factors that affect the adoption of artificial intelligence in healthcare (AIH) by healthcare providers and to understand “What are the factors that influence healthcare providers’ behavioral intentions to adopt AIH in their routine practice?” An integrated survey was conducted among healthcare providers, including consultants, residents/students, and nurses. The survey included items related to performance expectancy, effort expectancy, initial trust, personal innovativeness, task complexity, and technology characteristics. The collected data were analyzed using structural equation modeling. A total of 392 healthcare professionals participated in the survey, with 72.4% being male and 50.7% being 30 years old or younger. The results showed that performance expectancy, effort expectancy, and initial trust have a positive influence on the behavioral intentions of healthcare providers to use AIH. Personal innovativeness was found to have a positive influence on effort expectancy, while task complexity and technology characteristics have a positive influence on effort expectancy for AIH. The study’s empirically validated model sheds light on healthcare providers’ intention to adopt AIH, while the study’s findings can be used to develop strategies to encourage this adoption. However, further investigation is necessary to understand the individual factors affecting the adoption of AIH by healthcare providers.

Briganti and Memorial Sloan-Kettering Cancer Centre (MSKCC) nomograms are the two commonly used models for predicting lymph node invasion (LNI) in patients with prostate cancer. However, they have never been validated in the Indian cohort of patients with prostate cancer. Hence, with this study, we aimed to externally validate Briganti (2012) and MSKCC nomograms in our dataset of patients who underwent robot-assisted radical prostatectomy (RARP). We reviewed our prospectively maintained RARP data to predict the probability of LNI using Briganti (2012) and MSKCC nomograms. The two models were validated by receiver operating characteristic (ROC) curve analysis, calibration plots, and decision curve analysis (DCA). Of the 482 patients included in this study, 127 (26.3%) had lymph nodal metastasis. ROC analysis revealed an area under the curve of 0.75 (0.70–0.80) and 0.76 (0.71–0.80) for the Briganti and MSKCC nomograms, respectively, in predicting LNI. Calibration plots for both Briganti and MSKCC nomograms showed under or overestimation at different predicted probabilities. DCA showed a net clinical benefit of both models at a threshold probability of 10%. Using 5% cut-off for threshold for lymph node dissection, Briganti nomogram would have sensitivity, specificity, PPV, and NPV of (126/127) 99.2%, (14/355) 3.9%, (126/467) 26.9%, and (14/15) 93.3%, respectively. Using the same cut-off, MSKCC nomogram would have sensitivity, specificity, PPV, and NPV of (126/127) 99.2%, (56/355) 15.7%, (126/425) 29%, and (56/57) 98%, respectively. With this study, we independently validated Briganti and MSKCC nomograms for predicting LNI in the Indian cohort of patients with prostate cancer.

Aim: To conduct a comparative, nonrandomized study to assess the feasibility of mini-Endoscopic Combined Intrarenal Surgery (ECIRS) using supine Mini-percutaneous nephrolithotomy (PCNL) access (16F) in Galdakao-Modified Supine Valdivia position for managing proximal large-volume impacted ureteral calculi as ambulatory day-care surgery vis-a-vis standalone ureteroscopy (URS) with push-back PCNL, if needed. The primary aim was to study the outcomes and stone-free rates (SFRs). Secondary aim was to compare the intraoperative and short-term postoperative complications. Materials and Methods: Data of 60 patients undergoing ECIRS (Group 1) from January 2016 to December 2019 were collected prospectively in a nonrandomized fashion from a single center after Ethics Committee approval. A matched-paired analysis was performed with retrospectively collated data of 60 patients undergoing standard URS/pushback PCNL (Group 2) using analysis of variance, Fisher's exact test, and Chi-square test. p < 0.05 was considered statistically significant. Outcomes and Results: Group 1 patients had a significantly shorter procedure time vs Group 2 (42.1 ± 11.2 minutes vs 52.1 ± 13.7 minutes; p < 0.001). Group 1 (59/60) patients had an overall single stage SFR of 98.3%, which was significantly higher than single-stage SFR of 83% in Group 2 (50/60) ( p < 0.002). Group 2 patients had a higher incidence of fever (10 vs 4, p = 0.01). However, there were no cases of sepsis in either group. Rest of the complications were comparable for both, as the need for ancillary procedures was significantly higher in Group 2 (10% vs 1.7%, p < 0.001). Patients were discharged on the same day in both groups. Conclusions: The findings of our study suggest that, in large and impacted proximal ureteric stones, combined minimally invasive endoscopic approach offers the best option to render the patient stone free by a single intervention. With better intra- and postoperative outcomes and safety profile, Mini-ECIRS may be considered as an ambulatory procedure in this setting.

The COVID-19 Pandemic has caused unprecedented global challenges, including significant socioeconomic disruptions and the closure of schools and universities in almost all countries. Understanding the role of culture in shaping individual and societal responses to the Pandemic is crucial. This review article examines the applicability of Hofstede's cultural dimensions framework in devising effective COVID-19 control strategies. The article provides an overview of each dimension and its impact on the implementation of COVID-19 control strategies. It also includes a case analysis of four countries - India, the United Kingdom, the United States of America, and Poland - to illustrate the interplay between culture and COVID-19 control strategies. The insights provided by Hofstede's framework and the case analysis demonstrate that cultural differences can significantly impact the success of COVID-19 control strategies. By taking these differences into account, governments and public health authorities can improve the effectiveness of their COVID-19 control measures.

Purpose of review: With advancements in surgical technology along with procedural techniques, this article throws light on the latest developments and applications of artificial intelligence (AI), extended reality, 3D (three-dimensional) printing and robotics in percutaneous nephrolithotomy (PCNL). Recent findings: This review highlights the applications of AI in PCNL over the past 2 years. Mostly studies have been reported on development of machine learning (ML) based predicting models and identification of stone composition using deep learning convolutional neural network (DL-CNN). But owing to the complexity of the models and lack of generalizability, it is still not incorporated in the routine clinical practice. Extended reality based simulation and training models have enabled trainees to enhance their skills and shorten the learning curve. Similar advantages have been reported with the use of 3D printed models when used to train young and novice endourologists to improve their skills in percutaneous access (PCA). Applications of robotics in PCNL look promising but are still in nascent stages. Summary: Future research on PCNL should focus more on generalizability and adaptability of technological advancements in terms of training and improvement of patient outcomes.

Background: Wearable device technology has recently been involved in the healthcare industry substantially. India is the world’s third largest market for wearable devices and is projected to expand at a compound annual growth rate of ~26.33%. However, there is a paucity of literature analyzing the factors determining the acceptance of wearable healthcare device technology among low-middle-income countries. Methods: This cross-sectional, web-based survey aims to analyze the perceptions affecting the adoption and usage of wearable devices among the Indian population aged 16 years and above. Results: A total of 495 responses were obtained. In all, 50.3% were aged between 25–50 years and 51.3% belonged to the lower-income group. While 62.2% of the participants reported using wearable devices for managing their health, 29.3% were using them daily. technology and task fitness (TTF) showed a significant positive correlation with connectivity (r = 0.716), health care (r = 0.780), communication (r = 0.637), infotainment (r = 0.598), perceived usefulness (PU) (r = 0.792), and perceived ease of use (PEOU) (r = 0.800). Behavioral intention (BI) to use wearable devices positively correlated with PEOU (r = 0.644) and PU (r = 0.711). All factors affecting the use of wearable devices studied had higher mean scores among participants who were already using wearable devices. Male respondents had significantly higher mean scores for BI (p = 0.034) and PEOU (p = 0.009). Respondents older than 25 years of age had higher mean scores for BI (p = 0.027) and Infotainment (p = 0.032). Conclusions: This study found a significant correlation with the adoption and acceptance of wearable devices for healthcare management in the Indian context.

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.