Morphologic identification of clinically encountered moulds using a residual neural network - Jing, Ran; Yin, Xiang-Long; Xie, Xiu-Li; Lian, He-Qing; Li, Jin; Zhang, Ge; Yang, Wen-Hang; Sun, Tian-Shu; Xu, Ying-Chun (Frontiers in Microbiology, 2022)
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 classify mould infections by training, testing, and evaluating a ResNet-50 model. Our research achieved the rapid classification 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 improve the classification performance. The overall classification 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 classification 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.