Cell子刊：上海交大孙加源/熊红凯/戴文睿团队开发肺病诊断AI系统，准确率媲美专家

Core Viewpoint - The article discusses the development and potential of the AI-CEMA system, a deep learning-assisted diagnostic tool for intrathoracic lymphadenopathy and lung lesions, which demonstrates diagnostic accuracy comparable to experienced experts [3][5][6]. Group 1: Background on Intrathoracic Lymphadenopathy - Intrathoracic lymphadenopathy is a common challenge faced by pulmonologists, characterized by abnormal enlargement of mediastinal and hilar lymph nodes [2]. - The most common malignant cause of intrathoracic lymphadenopathy is lung cancer, which is the leading cancer globally and the primary cause of cancer-related deaths, with an estimated 2.5 million new cases and 1.8 million deaths in 2022 [2]. Group 2: AI-CEMA System Development - The AI-CEMA system was developed by a team from Shanghai Jiao Tong University and published in Cell Reports Medicine, focusing on the detection and diagnosis of intrathoracic lymphadenopathy using endobronchial ultrasound multimodal videos [3]. - The system utilizes convex probe endobronchial ultrasound (CP-EBUS) multimodal videos to automatically select representative images, identify lymph nodes, and differentiate between benign and malignant nodes [5]. Group 3: Performance and Validation - AI-CEMA was trained on a dataset of 1,006 lymph nodes and validated through a retrospective study, achieving an area under the curve (AUC) of 0.8490, comparable to the expert level AUC of 0.7847 [5]. - The system also successfully applied to lung lesion diagnosis, achieving an AUC of 0.8192, indicating its versatility and effectiveness in clinical settings [5]. Group 4: Clinical Implications - The AI-CEMA system offers a non-invasive diagnostic approach, providing automated and expert-level diagnosis for intrathoracic lymphadenopathy and lung lesions, showcasing significant potential in clinical diagnostics [6][8].