Human-multimodal deep learning collaboration in 'precise' diagnosis of lupus erythematosus subtypes and similar skin diseases.
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Li Qianwen, Yang Zhi, Chen Kaili, Zhao Ming, Long Hai, Deng Yueming, Hu Haoran, Jia Chen, Wu Meiyu, Zhao Zhidan, Zhu Huan, Zhou Suqing, Zhao Mingming, Cao Pengpeng, Zhou Shengnan, Song Yang, Tang Guishao, Liu Juan, Jiang Jiao, Liao Wei, Zhou Wenhui, Yang Bingyi, Xiong Feng, Zhang Suhan, Gao Xiaofei, Jiang Yiqun, Zhang Wei, Zhang Bo, He Yan-Ling, Ran Liwei, Zhang Chunlei, Wu Wenting, Suolang Quzong, Luo Hanhuan, Kang Xiaojing, Wu Caoying, Jin Hongzhong, Chen Lei, Guo Qing, Gui Guangji, Li Shanshan, Si Henan, Guo Shuping, Liu Hong-Ye, Liu Xiguang, Ma Guo-Zhang, Deng Danqi, Yuan Limei, Lu Jianyun, Zeng Jinrong, Jiang Xian, Lyu Xiaoyan, Chen Liuqing, Hu Bin, Tao Juan, Liu Yuhao, Wang Gang, Zhu Guannan, Yao Zhirong, Xu Qianyue, Yang Bin, Wang Yu, Ding Yan, Yang Xianxu, Kai Hu, Wu Haijing, Lu Qianjin
Аннотация
OBJECTIVES: We aim to develop a multimodal deep learning system (MMDLS) for human-AI collaboration in diagnosis of LE subtypes.
METHODS: This is a multi-centre study based on 25 institutions across China to assist in diagnosis of LE subtypes, other eight similar skin diseases and healthy subjects. In total, 446 cases with 800 clinical skin images, 3786 multicolor-immunohistochemistry (multi-IHC) images and clinical data were collected, and EfficientNet-B3 and ResNet-18 were utilized in this study.
RESULTS: In the multi-classification task, the overall performance of MMDLS on 13 skin conditions is much higher than single or dual modals (Sen = 0.8288, Spe = 0.9852, Pre = 0.8518, AUC = 0.9844). Further, the MMDLS-based diagnostic-support help improves the accuracy of dermatologists from 66.88% ± 6.94% to 81.25% ± 4.23% (p = 0.0004).
CONCLUSIONS: These results highlight the benefit of human-MMDLS collaborated framework in telemedicine by assisting dermatologists and rheumatologists in the differential diagnosis of LE subtypes and similar skin diseases.