Tsai Chu-Lin, Chu Teresa Cheng-Chieh, Wang Chih-Hung, Chang Wei-Tien, Tsai Min-Shan, Ku Shih-Chi, Lin Yen-Hung, Tai Hao-Chih, Kuo Shuenn-Wen, Wang Kuo-Chuan, Chao Anne, Tang Sung-Chun, Liu Wei-Lun, Tsai Ming-Han, Wang Ting-Ann, Chuang Shu-Lin, Lee Yi-Chia, Kuo Lu-Cheng, Chen Chiuan-Jung, Kao Jia-Horng, Wang Weichung, Huang Chien-Hua
Integrative Medical Data Center, Department of Medical Research, National Taiwan University Hospital, Taipei, Taiwan.
Department of Emergency Medicine, National Taiwan University Hospital, Taipei, Taiwan.
JMIR Res Protoc. 2025 Aug 20;14:e72928. doi: 10.2196/72928.
Advancements in artificial intelligence (AI) have driven substantial breakthroughs in computer-aided detection (CAD) for chest x-ray (CXR) imaging. The National Taiwan University Hospital research team previously developed an AI-based emergency CXR system (Capstone project), which led to the creation of a CXR module. This CXR module has an established model supported by extensive research and is ready for application in clinical trials without requiring additional model training. This study will use 3 submodules of the system: detection of misplaced endotracheal tubes, detection of misplaced nasogastric tubes, and identification of pneumothorax.
This study aims to apply a real-time CXR CAD system in emergency and critical care settings to evaluate its clinical and economic benefits without requiring additional CXR examinations or altering standard care and procedures. The study will evaluate the impact of CAD system on mortality reduction, postintubation complications, hospital stay duration, workload, and interpretation time, as wells as conduct a cost-effectiveness comparison with standard care.
This study adopts a pilot trial and cluster randomized controlled trial design, with random assignment conducted at the ward level. In the intervention group, units are granted access to AI diagnostic results, while the control group continues standard care practices. Consent will be obtained from attending physicians, residents, and advanced practice nurses in each participating ward. Once consent is secured, these health care providers in the intervention group will be authorized to use the CAD system. Intervention units will have access to AI-generated interpretations, whereas control units will maintain routine medical procedures without access to the AI diagnostic outputs.
The study was funded in September 2024. Data collection is expected to last from January 2026 to December 2027.
This study anticipates that the real-time CXR CAD system will automate the identification and detection of misplaced endotracheal and nasogastric tubes on CXRs, as well as assist clinicians in diagnosing pneumothorax. By reducing the workload of physicians, the system is expected to shorten the time required to detect tube misplacement and pneumothorax, decrease patient mortality and hospital stays, and ultimately lower health care costs.
INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/72928.
人工智能（AI）的进步推动了胸部X线（CXR）成像的计算机辅助检测（CAD）取得重大突破。台湾大学医院研究团队此前开发了一种基于AI的急诊CXR系统（顶点项目），并由此创建了一个CXR模块。该CXR模块拥有经广泛研究支持的既定模型，无需额外的模型训练即可应用于临床试验。本研究将使用该系统的3个子模块：气管内导管位置不当检测、鼻胃管位置不当检测和气胸识别。
本研究旨在将实时CXR CAD系统应用于急诊和重症监护环境，以评估其临床和经济效益，且无需额外的CXR检查或改变标准护理及程序。该研究将评估CAD系统对降低死亡率、插管后并发症、住院时间、工作量和解读时间的影响，并与标准护理进行成本效益比较。
本研究采用试点试验和整群随机对照试验设计，在病房层面进行随机分组。在干预组中，各单位可获取AI诊断结果，而对照组继续采用标准护理做法。将获得各参与病房的主治医生、住院医生和高级执业护士的同意。一旦获得同意，干预组的这些医疗服务提供者将被授权使用CAD系统。干预单位将能够获取AI生成的解读结果，而对照单位将维持常规医疗程序，无法获取AI诊断输出结果。
该研究于2024年9月获得资助。数据收集预计从2026年1月持续至2027年12月。
本研究预计，实时CXR CAD系统将自动识别和检测CXR上气管内和鼻胃管位置不当的情况，并协助临床医生诊断气胸。通过减轻医生的工作量，该系统有望缩短检测导管位置不当和气胸所需的时间，降低患者死亡率和住院时间，并最终降低医疗成本。
国际注册报告识别码（IRRID）：PRR1-10.2196/72928。
