Department of Orthopaedics, DMMC, Nagpur, India.
Department of Orthopaedics, JNMC, Wardha, India.
Int Orthop. 2024 May;48(5):1303-1311. doi: 10.1007/s00264-024-06125-4. Epub 2024 Mar 19.
AI has shown promise in automating and improving various tasks, including medical image analysis. Distal humerus fractures are a critical clinical concern that requires early diagnosis and treatment to avoid complications. The standard diagnostic method involves X-ray imaging, but subtle fractures can be missed, leading to delayed or incorrect diagnoses. Deep learning, a subset of artificial intelligence, has demonstrated the ability to automate medical image analysis tasks, potentially improving fracture identification accuracy and reducing the need for additional and cost-intensive imaging modalities (Schwarz et al. 2023). This study aims to develop a deep learning-based diagnostic support system for distal humerus fractures using conventional X-ray images. The primary objective of this study is to determine whether deep learning can provide reliable image-based fracture detection recommendations for distal humerus fractures.
Between March 2017 and March 2022, our tertiary hospital's PACS data were evaluated for conventional radiography images of the anteroposterior (AP) and lateral elbow for suspected traumatic distal humerus fractures. The data set consisted of 4931 images of patients seven years and older, after excluding paediatric images below seven years due to the absence of ossification centres. Two senior orthopaedic surgeons with 12 + years of experience reviewed and labelled the images as fractured or normal. The data set was split into training sets (79.88%) and validation tests (20.1%). Image pre-processing was performed by cropping the images to 224 × 224 pixels around the capitellum, and the deep learning algorithm architecture used was ResNet18.
The deep learning model demonstrated an accuracy of 69.14% in the validation test set, with a specificity of 95.89% and a positive predictive value (PPV) of 99.47%. However, the sensitivity was 61.49%, indicating that the model had a relatively high false negative rate. ROC analysis showed an AUC of 0.787 when deep learning AI was the reference and an AUC of 0.580 when the most senior orthopaedic surgeon was the reference. The performance of the model was compared with that of other orthopaedic surgeons of varying experience levels, showing varying levels of diagnostic precision.
The developed deep learning-based diagnostic support system shows potential for accurately diagnosing distal humerus fractures using AP and lateral elbow radiographs. The model's specificity and PPV indicate its ability to mark out occult lesions and has a high false positive rate. Further research and validation are necessary to improve the sensitivity and diagnostic accuracy of the model for practical clinical implementation.
人工智能在自动化和改进各种任务方面显示出了巨大的潜力，包括医学图像分析。肱骨远端骨折是一个临床关键问题，需要早期诊断和治疗，以避免并发症。标准的诊断方法涉及 X 射线成像，但细微的骨折可能会被遗漏，导致诊断延迟或不正确。深度学习是人工智能的一个子集，已经证明了其能够自动化医学图像分析任务，从而有可能提高骨折识别的准确性，并减少对额外且昂贵的成像方式的需求（Schwarz 等人，2023 年）。本研究旨在使用常规 X 射线图像开发一种基于深度学习的肱骨远端骨折诊断支持系统。本研究的主要目的是确定深度学习是否能够为肱骨远端骨折提供可靠的基于图像的骨折检测建议。
在 2017 年 3 月至 2022 年 3 月期间，我们对三级医院的 PACS 数据进行了评估，内容为疑似创伤性肱骨远端骨折的前后位（AP）和侧位肘部常规 X 射线图像。该数据集包含 4931 张 7 岁以上患者的图像，排除了 7 岁以下儿童的图像，因为这些儿童没有骨化中心。两位具有 12 年以上经验的资深骨科医生对这些图像进行了审查和标记，判断是否为骨折或正常。数据集被分为训练集（79.88%）和验证测试（20.1%）。通过裁剪图像来获取图像的关键区域（即肱骨小头周围的 224×224 像素）来进行图像预处理，所使用的深度学习算法架构为 ResNet18。
深度学习模型在验证测试集中的准确率为 69.14%，特异性为 95.89%，阳性预测值（PPV）为 99.47%。然而，敏感性为 61.49%，这表明模型存在相对较高的假阴性率。ROC 分析表明，当深度学习 AI 作为参考时，AUC 为 0.787，而当最资深的骨科医生作为参考时，AUC 为 0.580。该模型的性能与不同经验水平的其他骨科医生进行了比较，结果显示出不同的诊断精度。
所开发的基于深度学习的诊断支持系统具有使用 AP 和侧位肘部 X 射线准确诊断肱骨远端骨折的潜力。该模型的特异性和 PPV 表明其能够标记隐匿性病变，且具有较高的假阳性率。需要进一步的研究和验证，以提高模型的敏感性和诊断准确性，以便在实际临床中实施。
