Mycoses · 2022
Prototype early diagnostic model for invasive pulmonary aspergillosis based on deep learning and big data training.
СтатьяНазвание и аннотация: Перевод в очереди
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Wang Wei, Li Mujiao, Fan Peimin, Wang Hua, Cai Jing, Wang Kai, Zhang Tao, Xiao Zelin, Yan Jingdong, Chen Chaomin, Lv Qingwen
Аннотация
BACKGROUND: Currently, the diagnosis of invasive pulmonary aspergillosis (IPA) mainly depends on the integration of clinical, radiological and microbiological data. Artificial intelligence (AI) has shown great advantages in dealing with data-rich biological and medical challenges, but the literature on IPA diagnosis is rare.
OBJECTIVE: This study aimed to provide a non-invasive, objective and easy-to-use AI approach for the early diagnosis of IPA.
METHODS: We generated a prototype diagnostic deep learning model (IPA-NET) comprising three interrelated computation modules for the automatic diagnosis of IPA. First, IPA-NET was subjected to transfer learning using 300,000 CT images of non-fungal pneumonia from an online database. Second, training and internal test sets, including clinical features and chest CT images of patients with IPA and non-fungal pneumonia in the early stage of the disease, were independently constructed for model training and internal verification. Third, the model was further validated using an external test set.
RESULTS: IPA-NET showed a marked diagnostic performance for IPA as verified by the internal test set, with an accuracy of 96.8%, a sensitivity of 0.98, a specificity of 0.96 and an area under the curve (AUC) of 0.99. When further validated using the external test set, IPA-NET showed an accuracy of 89.7%, a sensitivity of 0.88, a specificity of 0.91 and an AUC of 0.95.
CONCLUSION: This novel deep learning model provides a non-invasive, objective and reliable method for the early diagnosis of IPA.
OBJECTIVE: This study aimed to provide a non-invasive, objective and easy-to-use AI approach for the early diagnosis of IPA.
METHODS: We generated a prototype diagnostic deep learning model (IPA-NET) comprising three interrelated computation modules for the automatic diagnosis of IPA. First, IPA-NET was subjected to transfer learning using 300,000 CT images of non-fungal pneumonia from an online database. Second, training and internal test sets, including clinical features and chest CT images of patients with IPA and non-fungal pneumonia in the early stage of the disease, were independently constructed for model training and internal verification. Third, the model was further validated using an external test set.
RESULTS: IPA-NET showed a marked diagnostic performance for IPA as verified by the internal test set, with an accuracy of 96.8%, a sensitivity of 0.98, a specificity of 0.96 and an area under the curve (AUC) of 0.99. When further validated using the external test set, IPA-NET showed an accuracy of 89.7%, a sensitivity of 0.88, a specificity of 0.91 and an AUC of 0.95.
CONCLUSION: This novel deep learning model provides a non-invasive, objective and reliable method for the early diagnosis of IPA.