FertilitY Predictor-a Machine Learning-based Web Tool for the Prediction of Assisted Reproduction Outcomes in Men with Y Chromosome Microdeletions

Overview

Journal J Assist Reprod Genet

Publisher Springer

Specialties Genetics
Reproductive Medicine

Date 2024 Dec 9

PMID 39652237

Authors

Stacy Colaco

Priyanka Narad

Ajit Kumar Singh

Payal Gupta

Alakto Choudhury

Abhishek Sengupta

Deepak Modi

Affiliations

Soon will be listed here.

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).

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