Artificial Intelligence in Reproductive Urology
Purpose of Review
The promise of artificial intelligence (AI) in medicine has been widely theorized over the past couple of decades. It has only been with technological advances over the past few years that physicians and computer scientists have started discovering its true clinical potential. Reproductive urology is a sub-discipline that AI could be of great contribution, as current predictive models and subjectivity within the field have several limitations. We review the literature to summarize recent AI applications in reproductive urology.
Early AI applications in reproductive urology focused on predicting semen parameters based on questionnaires that identify potential environmental factors and/or lifestyle habits impacting male fertility. AI has shown success in predicting the patient subpopulation most likely to need a genetic workup for azoospermia. With recent advances in image processing, automated sperm detection is a reality. Semen analyses, once a laboratory-only diagnostic test, have moved into health consumer homes with the advent of AI.
AI’s prospects in medicine are considerable and there is strong potential for AI within reproductive urology. Research in identifying the factors that can affect reproductive success either naturally or with assisted reproduction is of paramount importance to move the field forward.
KeywordsArtificial intelligence Machine learning Reproductive urology Male-factor infertility Artificial neural network Urology
Compliance with Ethical Standards
Conflict of Interest
Kevin Y. Chu, Daniel E. Nassau, Himanshu Arora, Soum Lokeshwar, Vinayak Madhusoodanan, and Ranjith Ramasamy each declare no potential conflicts of interest.
Human and Animal Rights and Informed Consent
This article does not contain any studies with human or animal subjects performed by any of the authors.
Papers of particular interest, published recently, have been highlighted as: • Of importance
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