Vahabi Arman, Daştan Ali Engin, Günay Hüseyin
Department of Orthopedics and Traumatology, Ege University University School of Medicine, Bornova, İzmir, Türkiye.
Department of Hand Surgery, Izmir City Hospital, İzmir, Türkiye.
J Hand Microsurg. 2025 Apr 9;17(4):100255. doi: 10.1016/j.jham.2025.100255. eCollection 2025 Jul.
Recently introduced image processing capabilities of AI models, which are accessible to a broad audience, may contribute to progress in medical research. Inspection and physical examination are important components of hand injury assessment, but they have inherent limitations in accuracy. The purpose of this study was to compare the structures identified as damaged during physical examination with those predicted by an AI model, utilizing its image processing capability. We hypothesized that the AI tool would demonstrate a level of accuracy comparable to that of physical examination in predicting injured structures.
We retrospectively reviewed the files of patients with hand and forearm injuries related to the volar aspect from January 2024 to July 2024. After exclusions, a total of 30 patients were included in the final analyses. Structures suspected to be damaged based on the initial evaluation and those identified as injured during surgery were documented through chart review. For the same patients, the AI tool (ChatGPT-4.0) was utilized to predict injured structures from clinical photos obtained during the initial examination. We examined the correlation and overlap between the structures identified as injured during the initial clinical examination and those predicted by the AI tool, as well as the correlation and overlap between the structures predicted by the AI tool and those confirmed as injured during surgical procedures.
The sensitivity of the physical examination was found to be 66.0 % (95 % CI: 57.5 %-73.7 %), while the specificity was 98,7 % (95 % CI: 97,6 % to 99,4 %). The sensitivity of the AI tool was found to be 61.7 % (95 % CI: 53.1 %-69.8 %), while the specificity was 82.4 % (95 % CI: 79.4 %-85.2 %).
In its current form, AI demonstrates limited yet promising potential as an adjunctive tool in the clinical evaluation of flexor-side injuries of the hand and forearm.
III, Diagnostic study.
最近人工智能模型引入的图像处理功能已为广大用户所用,这可能有助于医学研究取得进展。检查和体格检查是手部损伤评估的重要组成部分,但在准确性方面存在固有局限性。本研究的目的是利用人工智能模型的图像处理能力,比较体格检查中确定为受损的结构与人工智能模型预测的结构。我们假设人工智能工具在预测受伤结构方面将表现出与体格检查相当的准确性。
我们回顾性分析了2024年1月至2024年7月因手掌侧受伤的手部和前臂患者的病历。排除后,共有30名患者纳入最终分析。通过病历审查记录根据初步评估怀疑受损的结构以及手术中确定为受伤的结构。对于相同的患者,使用人工智能工具(ChatGPT-4.0)从初始检查期间获得的临床照片中预测受伤结构。我们检查了初始临床检查中确定为受伤的结构与人工智能工具预测的结构之间的相关性和重叠性,以及人工智能工具预测的结构与手术中确认受伤的结构之间的相关性和重叠性。
体格检查的敏感性为66.0%(95%CI:57.5%-73.7%),而特异性为98.7%(95%CI:97.6%至99.4%)。人工智能工具的敏感性为61.7%(95%CI:53.1%-69.8%),而特异性为82.4%(95%CI:79.4%-85.2%)。
就目前形式而言,人工智能作为手部和前臂屈侧损伤临床评估的辅助工具,显示出有限但有前景的潜力。
III,诊断性研究。