"The Algorithm Will See You Now": The Role of Artificial (and Real) Intelligence in the Future of Urology

The Role of Artificial Intelligence in Medical Education: A Systematic Review

BACKGROUND

To examine the artificial intelligence (AI) tools currently being studied in modern medical education, and critically evaluate the level of validation and the quality of evidence presented in each individual study.

METHODS

This review (PROSPERO ID: CRD42023410752) was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) statement. A database search was conducted using PubMed, Embase, and Cochrane Library. Articles written in the English language between 2000 and March 2023 were reviewed retrospectively using the MeSH Terms "AI" and "medical education" A total of 4642 potentially relevant studies were found.

RESULTS

After a thorough screening process, 36 studies were included in the final analysis. These studies consisted of 26 quantitative studies and 10 studies investigated the development and validation of AI tools. When examining the results of studies in which Support vector machines (SVMs) were employed, it has demonstrated high accuracy in assessing students' experiences, diagnosing acute abdominal pain, classifying skilled and novice participants, and evaluating surgical training levels. Particularly in the comparison of surgical skill levels, it has achieved an accuracy rate of over 92%.

CONCLUSION

AI tools demonstrated effectiveness in improving practical skills, diagnosing diseases, and evaluating student performance. However, further research with rigorous validation is required to identify the most effective AI tools for medical education.

Pilot study of machine learning in the task of distinguishing high and low-grade pediatric hydronephrosis on ultrasound

PURPOSE

Hydronephrosis is a common pediatric urological condition, characterized by dilation of the renal collecting system. Accurate identification of the severity of hydronephrosis is crucial in clinical management, as high-grade hydronephrosis can cause significant damage to the kidney. In this pilot study, we demonstrate the feasibility of machine learning in differentiating between high and low-grade hydronephrosis in pediatric patients.

MATERIALS AND METHODS

We retrospectively reviewed 592 images from 90 unique patients ages 0-8 years diagnosed with hydronephrosis at the University of Chicago's Pediatric Urology Clinic. The study included 74 high-grade hydronephrosis (145 images) and 227 low-grade hydronephrosis (447 images). Patients were excluded if they had less than 2 studies prior to surgical intervention or had structural abnormalities. We developed a radiomic-based artificial intelligence algorithm incorporating computerized texture analysis and machine learning (support-vector machine) to yield a predictor of hydronephrosis grade.

RESULTS

Receiver operating characteristic analysis of the classifier output yielded an area under the curve value of 0.86 (95% CI 0.81-0.92) in the task of distinguishing between low and high-grade hydronephrosis using a five-fold cross-validation by kidney. In addition, a Mann-Kendall trend test between computer output and clinical hydronephrosis grade yielded a statistically significant upward trend (p<0.001).

CONCLUSIONS

Our findings demonstrate the potential of machine learning in the differentiation between low and high-grade hydronephrosis. Further studies are warranted to validate our findings and their generalizability for use in clinical practice as a means to predict clinical outcomes and the resolution of hydronephrosis.

Artificial Intelligence in Surgical Training for Kidney Cancer: A Systematic Review of the Literature

The prevalence of renal cell carcinoma (RCC) is increasing due to advanced imaging techniques. Surgical resection is the standard treatment, involving complex radical and partial nephrectomy procedures that demand extensive training and planning. Furthermore, artificial intelligence (AI) can potentially aid the training process in the field of kidney cancer. This review explores how artificial intelligence (AI) can create a framework for kidney cancer surgery to address training difficulties. Following PRISMA 2020 criteria, an exhaustive search of PubMed and SCOPUS databases was conducted without any filters or restrictions. Inclusion criteria encompassed original English articles focusing on AI's role in kidney cancer surgical training. On the other hand, all non-original articles and articles published in any language other than English were excluded. Two independent reviewers assessed the articles, with a third party settling any disagreement. Study specifics, AI tools, methodologies, endpoints, and outcomes were extracted by the same authors. The Oxford Center for Evidence-Based Medicine's evidence levels were employed to assess the studies. Out of 468 identified records, 14 eligible studies were selected. Potential AI applications in kidney cancer surgical training include analyzing surgical workflow, annotating instruments, identifying tissues, and 3D reconstruction. AI is capable of appraising surgical skills, including the identification of procedural steps and instrument tracking. While AI and augmented reality (AR) enhance training, challenges persist in real-time tracking and registration. The utilization of AI-driven 3D reconstruction proves beneficial for intraoperative guidance and preoperative preparation. Artificial intelligence (AI) shows potential for advancing surgical training by providing unbiased evaluations, personalized feedback, and enhanced learning processes. Yet challenges such as consistent metric measurement, ethical concerns, and data privacy must be addressed. The integration of AI into kidney cancer surgical training offers solutions to training difficulties and a boost to surgical education. However, to fully harness its potential, additional studies are imperative.

CT Rendering and Radiomic Analysis in Post-Chemotherapy Retroperitoneal Lymph Node Dissection for Testicular Cancer to Anticipate Difficulties for Young Surgeons

Post-chemotherapy retroperitoneal lymph node dissection (PC-RPLND) in non-seminomatous germ-cell tumor (NSTGCTs) is a complex procedure. We evaluated whether 3D computed tomography (CT) rendering and their radiomic analysis help predict resectability by junior surgeons. The ambispective analysis was performed between 2016-2021. A prospective group (A) of 30 patients undergoing CT was segmented using the 3D Slicer software while a retrospective group (B) of 30 patients was evaluated with conventional CT (without 3D reconstruction). CatFisher's exact test showed a p-value of 0.13 for group A and 1.0 for Group B. The difference between the proportion test showed a p-value of 0.009149 (IC 0.1-0.63). The proportion of the correct classification showed a p-value of 0.645 (IC 0.55-0.87) for A, and 0.275 (IC 0.11-0.43) for Group B. Furthermore, 13 shape features were extracted: elongation, flatness, volume, sphericity, and surface area, among others. Performing a logistic regression with the entire dataset, n = 60, the results were: Accuracy: 0.7 and Precision: 0.65. Using n = 30 randomly chosen, the best result obtained was Accuracy: 0.73 and Precision: 0.83, with a p-value: 0.025 for Fisher's exact test. In conclusion, the results showed a significant difference in the prediction of resectability with conventional CT versus 3D reconstruction by junior surgeons versus experienced surgeons. Radiomic features used to elaborate an artificial intelligence model improve the prediction of resectability. The proposed model could be of great support in a university hospital, allowing it to plan the surgery and to anticipate complications.

Neural networks outperform expert humans in predicting patient impressions of symptomatic improvement following overactive bladder treatment

INTRODUCTION AND HYPOTHESIS

The objective was to accurately predict patient-centered subjective outcomes following the overactive bladder (OAB) treatments OnabotulinumtoxinA (OBTX-A) injection and sacral neuromodulation (SNM) using a neural network-based machine-learning approach. In the context of treatments designed to improve quality of life, a patient's perception of improvement should be the gold standard outcome measure.

METHODS

Cutting-edge neural network-based algorithms using reproducing kernel techniques were trained to predict patient-reported improvements in urinary leakage and bladder function as assessed by Patient Global Impression of Improvement score using the ROSETTA trial datasets. Blinded expert urologists provided with the same variables also predicted outcomes. Receiver operating characteristic curves and areas under the curve were generated for algorithm and human expert predictions in an out-of-sample holdout dataset.

RESULTS

Algorithms demonstrated excellent accuracy in predicting patient subjective improvement in urinary leakage (OBTX-A: AUC 0.75; SNM: 0.80). Similarly, algorithms accurately predicted patient subjective improvement in bladder function (OBTX-A: AUC 0.86; SNM: 0.96). The top-performing algorithms outcompeted human experts across outcome measures.

CONCLUSIONS

Novel neural network-based machine-learning algorithms accurately predicted OBTX-A and SNM patient subjective outcomes, and generally outcompeted expert humans. Subtle aspects of the physician-patient interaction remain uncomputable, and thus the machine-learning approach may serve as an aid, rather than as an alternative, to human interaction and clinical judgment.

Machine learning provides an accurate prognostication model for refractory overactive bladder treatment response and is noninferior to human experts

OBJECTIVE

The increasing wealth of clinical data may become unmanageable for a physician to assimilate into optimal decision-making without assistance. Utilizing a novel machine learning (ML) approach, we sought to develop algorithms to predict patient outcomes following the overactive bladder treatments OnabotulinumtoxinA (OBTX-A) injection and sacral neuromodulation (SNM).

MATERIALS AND METHODS

ROSETTA datasets for overactive bladder patients randomized to OBTX-A or SNM were obtained. Novel ML algorithms, using reproducing kernel techniques were developed and tasked to predict outcomes including treatment response and decrease in urge urinary incontinence episodes in both the OBTX-A and SNM cohorts, in validation and test sets. Blinded expert urologists also predicted outcomes. Receiver operating characteristic curves were generated and AUCs calculated for comparison to lines of ignorance and the expert urologists' predictions.

RESULTS

Trained algorithms demonstrated outstanding accuracy in predicting treatment response (OBTX-A: AUC 0.95; SNM: 0.88). Algorithms accurately predicted mean decrease in urge urinary incontinence episodes (MSE < 0.15) in OBTX-A and SNM. Algorithms were superior to human experts in response prediction for OBTX-A, and noninferior to human experts in response prediction for SNM.

CONCLUSIONS

Novel ML algorithms were accurate, superior to expert urologists in predicting OBTX-A outcomes, and noninferior to expert urologists in predicting SNM outcomes. Some aspects of the physician-patient interaction are subtle and uncomputable, and thus ML may complement, but not supplant, a physician's judgment.