Prototype early diagnostic model for invasive pulmonary aspergillosis based on deep learning and big data training.
Mycoses(2022)
摘要
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.
更多查看译文
关键词
artificial intelligence,computed tomography,deep learning,invasive pulmonary aspergillosis,predictive medicine,retrospective study
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要