Artificial intelligence is used in predicting the clinical outcomes before minimally invasive treatments for benign prostatic hyperplasia, to address the insufficient reliability despite multiple assessment parameters, such as flow rates and symptom scores. Various models of artificial intelligence and its contemporary applications in benign prostatic hyperplasia are reviewed and discussed. A search strategy adapted to identify and review the literature on the application of artificial intelligence with a dedicated search string with the following keywords: "Machine Learning," "Artificial Intelligence," AND "Benign Prostate Enlargement" OR "BPH" OR "Benign Prostatic Hyperplasia" was included and categorized. Review articles, editorial comments, and non-urologic studies were excluded. In the present review, 1600 patients were included from 4 studies that used different classifiers such as fuzzy systems, computer-based vision systems, and clinical data mining to study the applications of artificial intelligence in diagnoses and severity prediction and determine clinical factors responsible for treatment response in benign prostatic hyperplasia. The accuracy to correctly diagnose benign prostatic hyperplasia by Fuzzy systems was 90%, while that of computer-based vision system was 96.3%. Data mining achieved sensitivity and specificity of 70% and 50%, respectively, in correctly predicting the clinical response to medical treatment in benign prostatic hyperplasia. Artificial intelligence is gaining attraction in urology, with the potential to improve diagnostics and patient care. The results of artificial intelligence-based applications in benign prostatic hyperplasia are promising but lack generalizability of results. However, in the future, we will see a shift in the clinical paradigm as artificial intelligence applications will find their place in the guidelines and revolutionize the decision-making process.
Turkish journal of urology. 2022 Jul [Epub]
Milap Shah, Nithesh Naik, Bm Zeeshan Hameed, Rahul Paul, Dasharathraj K Shetty, Sufyan Ibrahim, Bhavan Prasad Rai, Piotr Chlosta, Patrick Rice, Bhaskar K Somani
Robotics and Urooncology, Max Hospital and Max Institute of Cancer Care, New Delhi, India ; iTRUE (International Training and Research in Uro-oncology and Endourology) Group, Manipal, Karnataka, India., iTRUE (International Training and Research in Uro-oncology and Endourology) Group, Manipal, Karnataka, India ; Department of Mechanical and Manufacturing Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka, India., Department of Urology, Father Muller Medical College, Karnataka, India., Department of Radiation Oncology, Massachusetts General Hospital, Harvard Medical School, Boston, USA., Department of Humanities and Management, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka, India., Kasturba Medical College, Manipal Academy of Higher Education, Manipal, Karnataka, India., iTRUE (International Training and Research in Uro-oncology and Endourology) Group, Manipal, Karnataka, India ; Department of Urology, Freeman Hospital, Newcastle, UK., Department of Urology, Jagiellonian University in Krakow, Kraków, Poland., Department of Urology, University Hospital Southampton NHS Trust, Southampton, UK., iTRUE (International Training and Research in Uro-oncology and Endourology) Group, Manipal, Karnataka, India ; Department of Urology, University Hospital Southampton NHS Trust, Southampton, UK.