Li Yun, Chen Deyan, Zhang Youwen, Liu Shuyi, Liang Lina, Tan Lunfang, Yang Fan, Li Yuyan, Peng Chengbao, Ye Feng, Zhang Xia, Hu Guodong, Chen Huai, Zheng Jinping
National Center for Respiratory Medicine, National Clinical Research Center for Respiratory Disease, State Key Laboratory of Respiratory Disease, Guangzhou Institute of Respiratory Health, the First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China.
Neusoft Research of Intelligent Healthcare Technology, Co. Ltd, Shenyang, China.
Med Mycol. 2025 Jan 6. doi: 10.1093/mmy/myae123.
In clinical practice, differentiating among pulmonary mucormycosis (PM), invasive pulmonary aspergillosis (IPA), and pulmonary tuberculosis (PTB) can be challenging. This study aimed to evaluate the performance of chest CT-based artificial intelligence (AI) models in distinguishing among these three diseases. Patients with confirmed PM, IPA, or PTB were retrospectively recruited from three tertiary hospitals. Two models were developed: unanotated supervised training (UST) model trained with original CT images and annotated supervised training (AST) model trained with manually annotated lesion images. A network questionnaire with 20 cases was designed to assess the performance of clinicians. Sensitivity, specificity, and accuracy were calculated for both models and clinicians. A total of 61 PM cases, 136 IPA cases, and 155 PTB cases were included in the study. In the internal validation set, both models had an accuracy of 66.1%. The UST model had sensitivities of 27.3%, 73.9%, and 76.0% for PM, IPA, and PTB, while AST model had sensitivities of 9.1%, 69.6%, and 88.0% for the same conditions. In the external validation set, both models had an accuracy of 57.6%. The UST model had sensitivities of 0, 85.7%, and 53.3% for PM, IPA, and PTB, respectively, while AST model had sensitivities of 0, 42.9% and 83.3%. 112 clinicians had an accuracy of 42.9%, with sensitivities of 31.5%, 43.4%, and 48.0% for PM, IPA, and PTB. We demonstrated that two AI models showed comparable performance in diagnosing three diseases. Both models achieved acceptable sensitivity in detecting IPA and PTB, but had low sensitivity in identifying PM.
在临床实践中，区分肺毛霉菌病（PM）、侵袭性肺曲霉病（IPA）和肺结核（PTB）具有挑战性。本研究旨在评估基于胸部CT的人工智能（AI）模型在区分这三种疾病方面的性能。从三家三级医院回顾性招募确诊为PM、IPA或PTB的患者。开发了两种模型：用原始CT图像训练的无注释监督训练（UST）模型和用手动注释的病变图像训练的注释监督训练（AST）模型。设计了一份包含20个病例的网络问卷来评估临床医生的表现。计算了两种模型和临床医生的敏感性、特异性和准确性。本研究共纳入61例PM病例、136例IPA病例和155例PTB病例。在内部验证集里，两种模型的准确率均为66.1%。UST模型对PM、IPA和PTB的敏感性分别为27.3%、73.9%和76.0%，而AST模型在相同情况下的敏感性分别为9.1%、69.6%和88.0%。在外部验证集中，两种模型的准确率均为57.6%。UST模型对PM、IPA和PTB的敏感性分别为0、85.7%和53.3%，而AST模型的敏感性分别为0、42.9%和83.3%。112名临床医生的准确率为42.9%，对PM、IPA和PTB的敏感性分别为31.5%、43.4%和48.0%。我们证明，两种AI模型在诊断这三种疾病方面表现相当。两种模型在检测IPA和PTB方面都达到了可接受的敏感性，但在识别PM方面敏感性较低。
