{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["13"],"submitter":["Jing R"],"pubmed_abstract":["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: <i>Aspergillus fumigatus</i> complex, <i>Aspergillus flavus</i> complex, <i>Aspergillus niger</i> complex, <i>Aspergillus terreus</i> complex, <i>Aspergillus nidulans</i>, <i>Aspergillus sydowii/Aspergillus versicolor</i>, <i>Syncephalastrum racemosum</i>, <i>Fusarium</i> spp., and <i>Penicillium</i> spp. In our study, the adaptive image contrast enhancement enabling XMVision Fungus AI as a promising module by effectively im"],"journal":["Frontiers in microbiology"],"pagination":["1021236"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC9614265"],"repository":["biostudies-literature"],"pubmed_title":["Morphologic identification of clinically encountered moulds using a residual neural network."],"pmcid":["PMC9614265"],"pubmed_authors":["Xie XL","Lian HQ","Zhang G","Xu YC","Li J","Sun TS","Jing R","Yang WH","Yin XL"],"additional_accession":[]},"is_claimable":false,"name":"Morphologic identification of clinically encountered moulds using a residual neural network.","description":"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: <i>Aspergillus fumigatus</i> complex, <i>Aspergillus flavus</i> complex, <i>Aspergillus niger</i> complex, <i>Aspergillus terreus</i> complex, <i>Aspergillus nidulans</i>, <i>Aspergillus sydowii/Aspergillus versicolor</i>, <i>Syncephalastrum racemosum</i>, <i>Fusarium</i> spp., and <i>Penicillium</i> spp. In our study, the adaptive image contrast enhancement enabling XMVision Fungus AI as a promising module by effectively im","dates":{"release":"2022-01-01T00:00:00Z","publication":"2022","modification":"2026-04-08T13:52:12.379Z","creation":"2025-02-19T02:59:51.904Z"},"accession":"S-EPMC9614265","cross_references":{"pubmed":["36312928"],"doi":["10.3389/fmicb.2022.1021236"]}}