■本文旨在探讨AI在提高使用X射线X射线照片识别髋部骨折的诊断准确性和效率方面的潜力。在研究中,我们训练了三种不同的深度学习模型,我们利用多数投票来评估他们的结果,旨在从X射线照片中获得最可靠和最精确的髋部骨折诊断。
■对从巴肯特大学附属的五家医院获得的10,849例AP骨盆X射线进行了初步研究。两名专业的整形外科医生最初将2,291张X射线照片标记为骨折,将8,558张X射线照片标记为非骨折。该算法在6,943(64%)的射线照片上进行了训练,在1,736(16%)射线照片上验证,并在2,170(20%)的射线照片上进行了测试,确保裂缝的均匀分布,年龄,和性别。我们采用了三种先进的深度学习架构,Xception(模型A),EfficientNet(模型B),和NFNet(型号C),通过多数投票技术(模型D)汇总最终决定。
■对于每个型号,我们获得了以下指标:对于模型A:F1评分0.895,准确性0.956,特异性0.973,敏感性0.893。对于模型B:F1评分0.900,准确度0.960,特异性0.991,灵敏度0.845。对于模型C:F1评分0.919,准确度0.966,特异性0.984,灵敏度0.899。对于模型D:F1评分0.929,准确度0.971,特异性0.991,灵敏度0.897。我们得出的结论是,就F1得分而言,模型D(多数投票)取得了最好的结果,准确度,和特异性值。
■我们的研究表明,通过投票汇总多个模型的决策所获得的结果,而不是仅仅依靠单一算法的决策,更加一致。这些算法的实际应用将是困难的,由于道德,legal,和保密问题,尽管取得了理论上的成功。开发成功的算法和方法不应被视为最终目标;重要的是要了解这些算法将如何在现实生活中使用。为了获得更一致的结果,从临床实践的反馈将是有益的。
UNASSIGNED: This article was undertaken to explore the potential of AI in enhancing the diagnostic accuracy and efficiency in identifying hip fractures using X-ray radiographs. In the study, we trained three distinct deep learning models, and we utilized majority voting to evaluate their outcomes, aiming to yield the most reliable and precise diagnoses of hip fractures from X-ray radiographs.
UNASSIGNED: An initial study was conducted of 10,849 AP pelvis X-rays obtained from five hospitals affiliated with Başkent University. Two expert orthopedic surgeons initially labeled 2,291 radiographs as fractures and 8,558 as non-fractures. The algorithm was trained on 6,943 (64%) radiographs, validated on 1,736 (16%) radiographs, and tested on 2,170 (20%) radiographs, ensuring an even distribution of fracture presence, age, and gender. We employed three advanced deep learning architectures, Xception (Model A), EfficientNet (Model B), and NfNet (Model C), with a final decision aggregated through a majority voting technique (Model D).
UNASSIGNED: For each model, we achieved the following metrics:For Model A: F1 Score 0.895, Accuracy 0.956, Specificity 0.973, Sensitivity 0.893.For Model B: F1 Score 0.900, Accuracy 0.960, Specificity 0.991, Sensitivity 0.845.For Model C: F1 Score 0.919, Accuracy 0.966, Specificity 0.984, Sensitivity 0.899.For Model D: F1 Score 0.929, Accuracy 0.971, Specificity 0.991, Sensitivity 0.897.We concluded that Model D (majority voting) achieved the best results in terms of the F1 score, accuracy, and specificity values.
UNASSIGNED: Our study demonstrates that the results obtained by aggregating the decisions of multiple models through voting, rather than relying solely on the decision of a single algorithm, are more consistent. The practical application of these algorithms will be difficult due to ethical, legal, and confidentiality issues, despite the theoretical success achieved. Developing successful algorithms and methodologies should not be viewed as the ultimate goal; it is important to understand how these algorithms will be used in real-life situations. In order to achieve more consistent results, feedback from clinical practice will be helpful.