AUTHOR=Jing Ran , Yin Xiang-Long , Xie Xiu-Li , Lian He-Qing , Li Jin , Zhang Ge , Yang Wen-Hang , Sun Tian-Shu , Xu Ying-Chun 

TITLE=Morphologic identification of clinically encountered moulds using a residual neural network

JOURNAL=Frontiers in Microbiology

VOLUME=13

YEAR=2022

URL=https://www.frontiersin.org/journals/microbiology/articles/10.3389/fmicb.2022.1021236

DOI=10.3389/fmicb.2022.1021236

ISSN=1664-302X

ABSTRACT=<p>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: <italic>Aspergillus fumigatus</italic> complex, <italic>Aspergillus flavus</italic> complex, <italic>Aspergillus niger</italic> complex, <italic>Aspergillus terreus</italic> complex, <italic>Aspergillus nidulans</italic>, <italic>Aspergillus sydowii/Aspergillus versicolor</italic>, <italic>Syncephalastrum racemosum</italic>, <italic>Fusarium</italic> spp., and <italic>Penicillium</italic> spp. In our study, the adaptive image contrast enhancement enabling XMVision Fungus AI as a promising module by effectively improve the identification performance. The overall identification accuracy of XMVision Fungus AI was up to 93.00% (279/300), which was higher than that of human readers. XMVision Fungus AI shows intrinsic advantages in the identification of clinical moulds and can be applied to improve human identification efficiency through training. Moreover, it has great potential for clinical application because of its convenient operation and lower cost. This system will be suitable for primary hospitals in China and developing countries.</p>