biostudies-literatureUnknown13Jing RThe use of morphology to diagnose invasive mould infections in China still faces substantial challenges, which often leads to delayed diagnosis or misdiagnosis. We developed a model called XMVision Fungus AI to identify mould infections by training, testing, and evaluating a ResNet-50 model. Our research achieved the rapid identification of nine common clinical moulds: Aspergillus fumigatus complex, Aspergillus flavus complex, Aspergillus niger complex, Aspergillus terreus complex, Aspergillus nidulans, Aspergillus sydowii/Aspergillus versicolor, Syncephalastrum racemosum, Fusarium spp., and Penicillium spp. In our study, the adaptive image contrast enhancement enabling XMVision Fungus AI as a promising module by effectively imFrontiers in microbiology1021236https://www.ebi.ac.uk/biostudies/studies/S-EPMC9614265biostudies-literatureMorphologic identification of clinically encountered moulds using a residual neural network.PMC9614265Xie XLLian HQZhang GXu YCLi JSun TSJing RYang WHYin XLfalseMorphologic identification of clinically encountered moulds using a residual neural network.The use of morphology to diagnose invasive mould infections in China still faces substantial challenges, which often leads to delayed diagnosis or misdiagnosis. We developed a model called XMVision Fungus AI to identify mould infections by training, testing, and evaluating a ResNet-50 model. Our research achieved the rapid identification of nine common clinical moulds: Aspergillus fumigatus complex, Aspergillus flavus complex, Aspergillus niger complex, Aspergillus terreus complex, Aspergillus nidulans, Aspergillus sydowii/Aspergillus versicolor, Syncephalastrum racemosum, Fusarium spp., and Penicillium spp. In our study, the adaptive image contrast enhancement enabling XMVision Fungus AI as a promising module by effectively im2022-01-01T00:00:00Z20222026-04-08T13:52:12.379Z2025-02-19T02:59:51.904ZS-EPMC96142653631292810.3389/fmicb.2022.1021236