ReviewWorld journal of urology2020
Current and future applications of machine and deep learning in urology: a review of the literature on urolithiasis, renal cell carcinoma, and bladder and prostate cancer.
Review in World journal of urology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to 2 registered trials, which are not on this map. Cited by 86 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Prospective Multi-center Clinical Study on the Application Value of Artificial Intelligence in MRI Precision Diagnosis and Treatment of Bladder Cancer
Multiomics Approach for Patients Stratification and Novel Target Identification in Metastatic Clear Renal Cell Carcnoma
Who cites it
86 citing papers in PubMed, 1 synthesis or guideline pooled it, 176 citations in OpenAlex.
- Deep Learning in Radiation Oncology Treatment Planning for Prostate Cancer: A Systematic Review.Journal of medical systems · 2020Pooled it
- Predictive model for urosepsis in patients with Upper Urinary Tract Calculi based on ultrasonography and urinalysis using artificial intelligence learning.International braz j urol : official journal of the Brazilian Society of UrologyTrial
- Optimizing Deep Learning for Renal Mass Characterization in Challenging Cases: A Comparative Study of Spatial-Input Strategies and a Validated ROI-Only DLR Nomogram.Annals of surgical oncology · 2026Article
- Artificial intelligence in urology: A review of United States Food and Drug Administration-cleared devices.BJU international · 2026Review
- Shaping the Future of Men's Health: How AI Could Be a Transformative Tool for Better Patient Outcomes and Provider Efficiency.Journal of medical Internet research · 2026Article
- Multiphase CT-based deep learning radiomics nomogram models for preoperative WHO/ISUP grading of clear cell renal cell carcinoma: a two-center validation study.BMC medical imaging · 2026Article
- Exploring advancements in the management of penile cancer in the era of artificial intelligence and machine learning: a narrative review.Annals of medicine and surgery (2012) · 2026Article
- UroFusion-X: a unified multimodal deep learning framework for robust diagnosis, subtyping, and prognosis of urological cancers.NPJ digital medicine · 2026Article
- Machine learning model integrating radiomics and clinical features for predicting postoperative bleeding after percutaneous nephrolithotomy.European journal of medical research · 2026Article
- Non-invasive evaluation of muscle invasion and survival prognosis in bladder cancer using enhanced CT-based deep learning radiomics: a multi-center real-world cohort study.Military Medical Research · 2026Article
- An explainable machine learning model for predicting bladder tumor aecurrence risk.Frontiers in oncology · 2026Article
- Artificial intelligence and machine learning in neurogenic lower urinary tract dysfunction and spinal cord injury: current status, emerging technologies, and ethical perspectives.Frontiers in urology · 2026Article
- Article
- Multimodal deep learning framework integrating multiphase CT and histopathological whole slide imaging for predicting recurrence in ccRCC.Scientific reports · 2025Article
- Evaluation of prostate cancer pathology reports generated by ChatGPT to enhance patient comprehension.Scientific reports · 2025Article
- AI-driven preoperative risk assessment in kidney cancer surgery: A comparative feasibility study of machine learning models.BJUI compass · 2025Article
- Role of DECT-Based Imaging Biomarkers and Machine Learning to Predict Renal Cell Carcinoma Subtypes.The Indian journal of radiology & imaging · 2025Article
- Artificial intelligence model for predicting early biochemical recurrence of prostate cancer after robotic-assisted radical prostatectomy.Scientific reports · 2025Article
- Envisioning the Future of AI in Urodynamics: Exploratory Interviews With Experts.Neurourology and urodynamics · 2025Article
- Impact of tumor size and sex on diagnostic discrepancies in renal mass imaging.Scientific reports · 2025Article
26 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeThe purpose of the study was to provide a comprehensive review of recent machine learning (ML) and deep learning (DL) applications in urological practice. Numerous studies have reported their use in the medical care of various urological disorders; however, no critical analysis has been made to date.
methodsA detailed search of original articles was performed using the PubMed MEDLINE database to identify recent English literature relevant to ML and DL applications in the fields of urolithiasis, renal cell carcinoma (RCC), bladder cancer (BCa), and prostate cancer (PCa).
resultsIn total, 43 articles were included addressing these four subfields. The most common ML and DL application in urolithiasis is in the prediction of endourologic surgical outcomes. The main area of research involving ML and DL in RCC concerns the differentiation between benign and malignant small renal masses, Fuhrman nuclear grade prediction, and gene expression-based molecular signatures. BCa studies employ radiomics and texture feature analysis for the distinction between low- and high-grade tumors, address accurate image-based cytology, and use algorithms to predict treatment response, tumor recurrence, and patient survival. PCa studies aim at developing algorithms for Gleason score prediction, MRI computer-aided diagnosis, and surgical outcomes and biochemical recurrence prediction. Studies consistently found the superiority of these methods over traditional statistical methods.
conclusionsThe continuous incorporation of clinical data, further ML and DL algorithm retraining, and generalizability of models will augment the prediction accuracy and enhance individualized medicine.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.