Klein Céline, Fondu Pierre, Ghazali Daniel Aiham, Rotari Vladimir, Abou-Arab Osama, David Emmanuel
Department of Paediatric Orthopaedics, Jules Verne University of Picardie and Amiens Picardie University Hospital, Amiens, France; MP3CV MP3CV-EA7517, CURS, Amiens University Hospital and Jules Verne University of Picardie, Amiens, France.
Department of Paediatric Orthopaedics, Jules Verne University of Picardie and Amiens Picardie University Hospital, Amiens, France.
Orthop Traumatol Surg Res. 2025 Jul 23:104338. doi: 10.1016/j.otsr.2025.104338.
Hand injuries are a frequent reason for an emergency department visit and require a radiographic analysis. Misdiagnosed or undiagnosed injuries may lead to poor functional outcomes. Artificial intelligence (AI) is providing new tools for the diagnosis of injuries in routine clinical practice. The primary objective of the present study was to assess the diagnostic performance of AI in the diagnosis of hand fractures and dislocations, when compared with reviews by two experienced hand surgeons. The secondary objective was to assess the diagnostic performance of a resident vs. the AI.
On the basis of standard radiographs, the AI system would diagnose metacarpal and phalangeal fractures and dislocations with the same level of diagnostic accuracy (i.e. sensitivity and specificity) as senior hand surgeons.
This single-centre, retrospective study was conducted on hand radiography datasets collected from consecutive patients over the age of 16 consulting in an emergency department. The radiographic data were reviewed by two senior hand surgeons (constituting the gold standard) and a resident. Based on a contingency table, sensitivity, and specificity, the AI's and resident's respective abilities to detect fracture/dislocation were compared with the gold standard. The resident and the AI were also compared.
1915 radiographic datasets (4738 X-rays for 1892 patients) were included in the analysis. The Cohen's kappa of 0.865 indicated almost perfect agreement between the two senior surgeons. The AI's analysis yielded a sensitivity [95% confidence interval] of 97.6% [0.96-0.98] and a specificity of 88.9% [87.2-90.4]. False positives were noted in 162 cases. The AI failed to diagnose 11 injuries (0.6%): two dislocations of the proximal interphalangeal joint, seven fractures of the phalanx (including one third phalanx amputation and two metacarpal fractures). Relative to the AI, the resident's analysis yielded a significantly lower sensitivity (p < 0.0001) and a significantly higher specificity (p = 0.007).
An AI may be a valuable tool in emergency settings - especially for less experienced practitioners - but does not surpass the diagnostic performance of senior surgeons. The AI's ability to detect dislocations and amputations must be improved. An AI can complement (but not replace) a thorough clinical examination.
III.
手部损伤是急诊就诊的常见原因，需要进行影像学分析。误诊或漏诊的损伤可能导致功能预后不良。人工智能（AI）正在为常规临床实践中的损伤诊断提供新工具。本研究的主要目的是评估与两位经验丰富的手外科医生的诊断结果相比，AI在诊断手部骨折和脱位方面的诊断性能。次要目的是评估住院医师与AI的诊断性能。
基于标准X线片，AI系统诊断掌骨和指骨骨折及脱位的诊断准确性（即敏感性和特异性）与资深手外科医生相同。
本单中心回顾性研究对从急诊科就诊的16岁以上连续患者收集的手部X线摄影数据集进行。X线数据由两位资深手外科医生（构成金标准）和一名住院医师进行评估。根据列联表、敏感性和特异性，将AI和住院医师检测骨折/脱位的各自能力与金标准进行比较。同时也对住院医师和AI进行了比较。
1915个X线摄影数据集（1892例患者的4738张X线片）纳入分析。两位资深外科医生之间的Cohen's kappa值为0.865，表明几乎完全一致。AI分析的敏感性[95%置信区间]为97.6%[0.96 - 0.98]，特异性为88.9%[87.2 - 90.4]。发现162例假阳性病例。AI未能诊断出11例损伤（0.6%）：2例近端指间关节脱位、7例指骨骨折（包括1例第三节指骨截肢和2例掌骨骨折）。相对于AI，住院医师的分析敏感性显著较低（p < 0.0001），特异性显著较高（p = 0.007）。
在急诊环境中，AI可能是一种有价值的工具——尤其是对于经验不足的从业者——但并未超过资深外科医生的诊断性能。AI检测脱位和截肢的能力必须提高。AI可以补充（但不能替代）全面的临床检查。
III级。
