Chang Joseph, Lee Kuan-Jung, Wang Ti-Hao, Chen Chung-Ming
Department of Biomedical Engineering, College of Medicine and College of Engineering, National Taiwan University, No. 1, Sec. 1, Jen-Ai Road, Taipei 100, Taiwan.
EverFortune.AI Co., Ltd., Taichung 403, Taiwan.
J Clin Med. 2024 Jul 10;13(14):4042. doi: 10.3390/jcm13144042.
: Pneumothorax detection is often challenging, particularly when radiographic features are subtle. This study introduces a deep learning model that integrates curriculum learning and ChatGPT to enhance the detection of pneumothorax in chest X-rays. : The model training began with large, easily detectable pneumothoraces, gradually incorporating smaller, more complex cases to prevent performance plateauing. The training dataset comprised 6445 anonymized radiographs, validated across multiple sites, and further tested for generalizability in diverse clinical subgroups. Performance metrics were analyzed using descriptive statistics. : The model achieved a sensitivity of 0.97 and a specificity of 0.97, with an area under the curve (AUC) of 0.98, demonstrating a performance comparable to that of many FDA-approved devices. This study suggests that a structured approach to training deep learning models, through curriculum learning and enhanced data extraction via natural language processing, can facilitate and improve the training of AI models for pneumothorax detection.
气胸检测往往具有挑战性，尤其是当影像学特征不明显时。本研究引入了一种深度学习模型，该模型整合了课程学习和ChatGPT，以增强胸部X光片中气胸的检测。：模型训练从大型、易于检测的气胸开始，逐渐纳入较小、更复杂的病例，以防止性能停滞不前。训练数据集包括6445张匿名X光片，在多个地点进行了验证，并在不同临床亚组中进一步测试了其通用性。使用描述性统计分析性能指标。：该模型的灵敏度为0.97，特异性为0.97，曲线下面积（AUC）为0.98，其性能与许多FDA批准的设备相当。 这项研究表明，通过课程学习和通过自然语言处理增强数据提取的结构化方法来训练深度学习模型，可以促进和改进用于气胸检测的人工智能模型的训练。
