MSk Lab, Imperial College London, Sir Michael Uren Hub, 86 Wood Lane, London, W12 0BZ, UK.
Istituto Clinico Citta Studi, Milan, Italy.
Arch Orthop Trauma Surg. 2024 Nov;144(11):4963-4968. doi: 10.1007/s00402-024-05589-8. Epub 2024 Oct 3.
Knee osteoarthritis is a prevalent condition frequently necessitating knee replacement surgery, with demand projected to rise substantially. Partial knee arthroplasty (PKA) offers advantages over total knee arthroplasty (TKA), yet its utilisation remains low despite guidance recommending consideration alongside TKA in shared decision making. Radiographic decision aids exist but are underutilised due to clinician time constraints.
This research develops a novel radiographic artificial intelligence (AI) tool using a dataset of knee radiographs and a panel of expert orthopaedic surgeons' assessments. Six AI models were trained to identify PKA candidacy.
1241 labelled four-view radiograph series were included. Models achieved statistically significant accuracies above random assignment, with EfficientNet-ES demonstrating the highest performance (AUC 95%, F1 score 83% and accuracy 80%).
The AI decision tool shows promise in identifying PKA candidates, potentially addressing underutilisation of this procedure. Its integration into clinical practice could enhance shared decision making and improve patient outcomes. Further validation and implementation studies are warranted to assess real-world utility and impact.
膝关节骨关节炎是一种常见疾病，常需要进行膝关节置换手术，预计需求将大幅增加。与全膝关节置换术（TKA）相比，部分膝关节置换术（PKA）具有优势，但尽管指南建议在共同决策中与 TKA 一起考虑，但 PKA 的使用率仍然较低。存在影像学决策辅助工具，但由于临床医生时间限制而未得到充分利用。
本研究使用膝关节 X 光片数据集和一组专家骨科医生的评估来开发一种新的影像学人工智能（AI）工具。训练了六个 AI 模型来识别 PKA 候选者。
共纳入 1241 个标记的四视图 X 光片系列。模型的表现明显优于随机分配，其中 EfficientNet-ES 表现最佳（AUC 95%，F1 分数 83%，准确率 80%）。
人工智能决策工具在识别 PKA 候选者方面显示出前景，可能解决该手术使用率低的问题。将其整合到临床实践中可以增强共同决策并改善患者结局。需要进一步的验证和实施研究来评估实际效用和影响。
