FertilitY Predictor—a machine learning-based web tool for the prediction of assisted reproduction outcomes in men with Y chromosome microdeletions

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Abstract
Purpose Y chromosome microdeletions (YCMD) are a common cause of azoospermia and oligozoospermia in men. Herein, we developed a machine learning-based web tool to predict sperm retrieval rates and success rates of assisted reproduction (ART) in men with YCMD. Methods Data on ART outcomes of men with YCMD who underwent ART were extracted from published studies by performing a systematic review. This data was used to develop a web-based predictive algorithm using machine learning. Results FertilitY Predictor classifies the type of YCMD into AZFa, AZFb, AZFc, their combinations, and gr/gr deletions based on the genetic markers as input. Further, it predicts the probability of sperm retrieval, fertilization rate, clinical pregnancy rate, and live birth rate based on the type of YCMD. Validation studies demonstrated its high accuracy and predictability for sperm retrieval, clinical pregnancy rates, and live birth rates. The tool predicts that men with deletions have a chance of sperm retrieval that varies with type of deletions, the clinical pregnancy rates and live birth rates are lower in men with AZF deletions. A trial version of the tool is available at http://fertilitypredictor.sbdaresearch.in. Conclusions FertilitY Predictor allows users to classify AZFa, AZFb, AZFc, and gr/gr deletions and also predict the outcomes of ART based on the type of deletions. Trial registration PROSPERO (CRD42022311738).
Funding Information
  • Department of Health Research, Government of India (YSS/2019/000118/PRCYSS)
  • Rajiv Gandhi Centre for Biotechnology, Department of Biotechnology, Ministry of Science and Technology, India (BT/PR31614/BID/7/857/2019)
  • Indian Council of Medical Research

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