Machine learning-enhanced SERS for accurate azoospermia diagnosis via seminal plasma exosome analysis  

作　　者：Jiarui Wang Shiyan Jiang Jiaxin Shi Jing Wang Shengrong Du Zufang Huang 

机构地区：[1]Key Laboratory of OptoElectronic Science and Technology for Medicine of Ministry of Education Fujian Provincial Key Laboratory of Photonics Technology Fujian Normal University,Fuzhou 350007,P.R.China [2]Center of Reproductive Medicine Fujian Maternity and Child Health Hospital College of Clinical Medicine for Obstetrics&Gynecology and Pediatrics,Fujian Medical University Fuzhou 350001,P.R.China

出　　处：《Journal of Innovative Optical Health Sciences》2025年第1期195-206,共12页创新光学健康科学杂志(英文)

基　　金：support from the National Natural Science Foundation of China(No.62275049);the Natural Science Foundation of Fujian Province,China(No.2022J02024);the Fujian Province Joint Fund Project for Scientific and Technological Innovation(2023Y9383).

摘　　要：Male infertility affects 10-15%of couples globally,with azoospermia-complete absence of sperm-accounting for 15%of cases.Traditional diagnostic methods for azoospermia are subjective and variable.This study presents a novel,noninvasive,and accurate diagnostic method using surface-enhanced Raman spectroscopy(SERS)combined with machine learning to analyze seminal plasma exosomes.Semen samples from healthy controls(n=32)and azoospermic patients(n=22)were collected,and their exosomal SERS spectra were obtained.Machine learning algorithms were employed to distinguish between the SERS pro files of healthy and azoospermic samples,achieving an impressive sensitivity of 99.61%and a speci ficity of 99.58%,thereby highlighting signi ficant spectral differences.This integrated SERS and machine learning approach offers a sensitive,label-free,and objective diagnostic tool for early detection and monitoring of azoospermia,potentially enhancing clinical outcomes and patient management.

关 键 词：AZOOSPERMIA Raman spectroscopy SERS machine learning seminal plasma exosomes 

分 类 号：R318[医药卫生—生物医学工程]