Abstract：
OBJECTIVE: 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.
METHODS: 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.
RESULTS: 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.
CONCLUSIONS: 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.
METHODS: 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.
RESULTS: 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.
CONCLUSIONS: 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.
摘要：
目标：AI在自动化和改进各种任务方面表现出了希望，包括医学图像分析。肱骨远端骨折是一个关键的临床问题，需要早期诊断和治疗以避免并发症。标准的诊断方法包括X射线成像，但细微的骨折是可以错过的,导致延迟或不正确的诊断。深度学习,人工智能的一个子集，展示了自动化医学图像分析任务的能力，潜在地提高了骨折识别的准确性，并减少了对额外和成本密集型成像模式的需求(Schwarz等人。2023年）。本研究旨在使用常规X射线图像开发基于深度学习的肱骨远端骨折诊断支持系统。这项研究的主要目的是确定深度学习是否可以为肱骨远端骨折提供可靠的基于图像的骨折检测建议。
方法：在2017年3月至2022年3月之间，我们三级医院的PACS数据评估了可疑的创伤性肱骨远端骨折的前后侧（AP）和外侧肘的常规X线图像。数据集包括4931张7岁及以上患者的图像，由于没有骨化中心，因此排除了7年以下的儿科图像。两名具有12年经验的高级骨科医生审查并标记图像为骨折或正常。数据集分为训练集（79.88%）和验证测试（20.1%）。图像预处理是通过将图像裁剪为224×224像素来执行的。使用的深度学习算法架构是ResNet18。
结果：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,灵敏度为61.49%，表明该模型的假阴性率相对较高。ROC分析显示，当深度学习AI作为参考时，AUC为0.787，当最资深的骨科外科医生作为参考时，AUC为0.580。将该模型的性能与其他经验水平不同的整形外科医生进行了比较，显示不同水平的诊断精度。
结论：开发的基于深度学习的诊断支持系统显示出使用AP和肘部外侧X光片准确诊断肱骨远端骨折的潜力。模型的特异性和PPV表明其能够标记出隐匿性病变，并且具有很高的假阳性率。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，以用于实际临床实施。
方法：在2017年3月至2022年3月之间，我们三级医院的PACS数据评估了可疑的创伤性肱骨远端骨折的前后侧（AP）和外侧肘的常规X线图像。数据集包括4931张7岁及以上患者的图像，由于没有骨化中心，因此排除了7年以下的儿科图像。两名具有12年经验的高级骨科医生审查并标记图像为骨折或正常。数据集分为训练集（79.88%）和验证测试（20.1%）。图像预处理是通过将图像裁剪为224×224像素来执行的。使用的深度学习算法架构是ResNet18。
结果：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,灵敏度为61.49%，表明该模型的假阴性率相对较高。ROC分析显示，当深度学习AI作为参考时，AUC为0.787，当最资深的骨科外科医生作为参考时，AUC为0.580。将该模型的性能与其他经验水平不同的整形外科医生进行了比较，显示不同水平的诊断精度。
结论：开发的基于深度学习的诊断支持系统显示出使用AP和肘部外侧X光片准确诊断肱骨远端骨折的潜力。模型的特异性和PPV表明其能够标记出隐匿性病变，并且具有很高的假阳性率。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，以用于实际临床实施。
