Prototype early diagnostic model for invasive pulmonary aspergillosis based on deep learning and big data training

• Published in: Mycoses Vol. 66; no. 2; pp. 118 - 127
• Main Authors: Wang, Wei, Li, Mujiao, Fan, Peimin, Wang, Hua, Cai, Jing, Wang, Kai, Zhang, Tao, Xiao, Zelin, Yan, Jingdong, Chen, Chaomin, Lv, Qingwen
• Format: Journal Article
• Language: English
• Published: Germany Wiley Subscription Services, Inc 01.02.2023
• Subjects: Artificial Intelligence; Aspergillosis; Big Data; computed tomography; Deep Learning; Diagnosis; Humans; invasive pulmonary aspergillosis; Invasive Pulmonary Aspergillosis - diagnosis; Pneumonia; predictive medicine; Retrospective Studies; retrospective study; Sensitivity and Specificity; Transfer learning; computed tomography; deep learning; predictive medicine; artificial intelligence; invasive pulmonary aspergillosis; retrospective study
• ISSN: 0933-7407; 1439-0507
• DOI: 10.1111/myc.13540

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.