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Artificial intelligence in stone disease
- Source :
- Current Opinion in Urology. 31:391-396
- Publication Year :
- 2021
- Publisher :
- Ovid Technologies (Wolters Kluwer Health), 2021.
-
Abstract
- Purpose of review Artificial intelligence (AI) is the ability of a machine, or computer, to simulate intelligent behavior. In medicine, the use of large datasets enables a computer to learn how to perform cognitive tasks, thereby facilitating medical decision-making. This review aims to describe advancements in AI in stone disease to improve diagnostic accuracy in determining stone composition, to predict outcomes of surgical procedures or watchful waiting and ultimately to optimize treatment choices for patients. Recent findings AI algorithms show high accuracy in different realms including stone detection and in the prediction of surgical outcomes. There are machine learning algorithms for outcomes after percutaneous nephrolithotomy, extracorporeal shockwave lithotripsy, and for ureteral stone passage. Some of these algorithms show better predictive capabilities compared to existing scoring systems and nomograms. Summary The use of AI can facilitate the development of diagnostic and treatment algorithms in patients with stone disease. Although the generalizability and external validity of these algorithms remain uncertain, the development of highly accurate AI-based tools may enable the urologist to provide more customized patient care and superior outcomes.
- Subjects :
- Elementary cognitive task
Ureteral Calculi
business.industry
Urology
medicine.medical_treatment
030232 urology & nephrology
MEDLINE
Nephrolithotomy, Percutaneous
Machine Learning
External validity
03 medical and health sciences
0302 clinical medicine
Artificial Intelligence
Lithotripsy
030220 oncology & carcinogenesis
medicine
Humans
Generalizability theory
In patient
Artificial intelligence
Percutaneous nephrolithotomy
business
Algorithms
Stone disease
Watchful waiting
Subjects
Details
- ISSN :
- 14736586 and 09630643
- Volume :
- 31
- Database :
- OpenAIRE
- Journal :
- Current Opinion in Urology
- Accession number :
- edsair.doi.dedup.....4383f673a4b7a3d7eb65b989131d7449