Pauling Cato, Laidlow-Singh Harsimran, Evans Emily, Garbera David, Williamson Rosalind, Fernando Ranil, Thomas Kate, Martin Helena, Arthurs Owen J, Shelmerdine Susan C
University College London, Gower Street, London, UK.
Department of Paediatric Radiology, Evelina London Children's Hospital, Guy's and St Thomas NHS Foundation Trust, London, United Kingdom.
Eur Radiol. 2025 Jul 7. doi: 10.1007/s00330-025-11790-z.
To determine the performance of a commercially available AI tool for fracture detection when used in children with osteogenesis imperfecta (OI).
All appendicular and pelvic radiographs from an OI clinic at a single centre from 48 patients were included. Seven radiologists evaluated anonymised images in two rounds, first without, then with AI assistance. Differences in diagnostic accuracy between the rounds were analysed.
48 patients (mean 12 years) provided 336 images, containing 206 fractures established by consensus opinion of two radiologists. AI produced a per-examination accuracy of 74.8% [95% CI: 65.4%, 82.7%], compared to average radiologist performance at 83.4% [95% CI: 75.2%, 89.8%]. Radiologists using AI assistance improved average radiologist accuracy per examination to 90.7% [95% CI: 83.5%, 95.4%]. AI gave more false negatives than radiologists, with 80 missed fractures versus 41, respectively. Radiologists were more likely (74.6%) to alter their original decision to agree with AI at the per-image level, 82.8% of which led to a correct result, 64.0% of which were changing from a false positive to a true negative.
Despite inferior standalone performance, AI assistance can still improve radiologist fracture detection in a rare disease paediatric population. Radiologists using AI typically led to more accurate diagnostic outcomes through reduced false positives. Future studies focusing on the real-world application of AI tools in a larger population of children with bone fragility disorders will help better evaluate whether these improvements in accuracy translate into improved patient outcomes.
Question How well does a commercially available artificial intelligence (AI) tool identify fractures, on appendicular radiographs of children with osteogenesis imperfecta (OI), and can it also improve radiologists' identification of fractures in this population? Findings Specialist human radiologists outperformed the AI fracture detection tool when acting alone; however, their diagnostic performance overall improved with AI assistance. Clinical relevance AI assistance improves specialist radiologist fracture detection in children with osteogenesis imperfecta, even with AI performance alone inferior to the radiologists acting alone. The reason for this was due to the AI moderating the number of false positives generated by the radiologists.
确定一种商用人工智能工具在用于成骨不全（OI）患儿时检测骨折的性能。
纳入来自单一中心OI诊所的48例患者的所有四肢和骨盆X线片。7名放射科医生分两轮评估匿名图像，第一轮无人工智能辅助，第二轮有人工智能辅助。分析两轮诊断准确性的差异。
48例患者（平均12岁）提供了336张图像，其中包含经两名放射科医生一致认定的206处骨折。人工智能每次检查的准确率为74.8%[95%CI：65.4%，82.7%]，而放射科医生的平均表现为83.4%[95%CI：75.2%，89.8%]。使用人工智能辅助的放射科医生每次检查的平均准确率提高到90.7%[95%CI：83.5%，95.4%]。人工智能产生的假阴性比放射科医生更多，分别有80处骨折漏诊和41处骨折漏诊。放射科医生更有可能（74.6%）在每张图像层面改变其原始决定以与人工智能一致，其中82.8%的改变导致了正确结果，64.0%的改变是从假阳性变为真阴性。
尽管独立性能较差，但人工智能辅助仍可提高罕见病儿科人群中放射科医生对骨折的检测能力。使用人工智能的放射科医生通常通过减少假阳性导致更准确的诊断结果。未来针对人工智能工具在更大规模骨脆性疾病患儿群体中的实际应用的研究，将有助于更好地评估这些准确性的提高是否转化为患者预后的改善。
问题一种商用人工智能（AI）工具在成骨不全（OI）患儿的四肢X线片上识别骨折的效果如何，它能否提高放射科医生对该人群骨折的识别能力？研究结果专业的人类放射科医生单独操作时在骨折检测工具方面表现优于人工智能；然而，在人工智能辅助下他们的诊断性能总体上有所提高。临床意义人工智能辅助提高了成骨不全患儿中专业放射科医生对骨折的检测能力，即使人工智能单独的表现不如放射科医生单独操作。原因是人工智能减少了放射科医生产生的假阳性数量。
