Alzubaidi Laith, Salhi Asma, A Fadhel Mohammed, Bai Jinshuai, Hollman Freek, Italia Kristine, Pareyon Roberto, Albahri A S, Ouyang Chun, Santamaría Jose, Cutbush Kenneth, Gupta Ashish, Abbosh Amin, Gu Yuantong
School of Mechanical, Medical, and Process Engineering, Queensland University of Technology, Brisbane, QLD, Australia.
Queensland Unit for Advanced Shoulder Research (QUASR)/ARC Industrial Transformation Training Centre-Joint Biomechanics, Queensland University of Technology, Brisbane, QLD, Australia.
PLoS One. 2024 Mar 11;19(3):e0299545. doi: 10.1371/journal.pone.0299545. eCollection 2024.
Musculoskeletal conditions affect an estimated 1.7 billion people worldwide, causing intense pain and disability. These conditions lead to 30 million emergency room visits yearly, and the numbers are only increasing. However, diagnosing musculoskeletal issues can be challenging, especially in emergencies where quick decisions are necessary. Deep learning (DL) has shown promise in various medical applications. However, previous methods had poor performance and a lack of transparency in detecting shoulder abnormalities on X-ray images due to a lack of training data and better representation of features. This often resulted in overfitting, poor generalisation, and potential bias in decision-making. To address these issues, a new trustworthy DL framework has been proposed to detect shoulder abnormalities (such as fractures, deformities, and arthritis) using X-ray images. The framework consists of two parts: same-domain transfer learning (TL) to mitigate imageNet mismatch and feature fusion to reduce error rates and improve trust in the final result. Same-domain TL involves training pre-trained models on a large number of labelled X-ray images from various body parts and fine-tuning them on the target dataset of shoulder X-ray images. Feature fusion combines the extracted features with seven DL models to train several ML classifiers. The proposed framework achieved an excellent accuracy rate of 99.2%, F1Score of 99.2%, and Cohen's kappa of 98.5%. Furthermore, the accuracy of the results was validated using three visualisation tools, including gradient-based class activation heat map (Grad CAM), activation visualisation, and locally interpretable model-independent explanations (LIME). The proposed framework outperformed previous DL methods and three orthopaedic surgeons invited to classify the test set, who obtained an average accuracy of 79.1%. The proposed framework has proven effective and robust, improving generalisation and increasing trust in the final results.
据估计，全球有17亿人受肌肉骨骼疾病影响，这些疾病会引发剧痛和残疾。这些疾病每年导致3000万人次急诊就诊，且人数还在不断增加。然而，诊断肌肉骨骼问题可能具有挑战性，尤其是在需要迅速做出决策的紧急情况下。深度学习（DL）在各种医学应用中已显示出前景。然而，由于缺乏训练数据和特征的更好表示，以前的方法在检测X射线图像上的肩部异常时性能不佳且缺乏透明度。这通常导致过拟合、泛化能力差以及决策中的潜在偏差。为了解决这些问题，已提出一种新的可靠DL框架，用于使用X射线图像检测肩部异常（如骨折、畸形和关节炎）。该框架由两部分组成：同域迁移学习（TL）以减轻与ImageNet的不匹配，以及特征融合以降低错误率并提高对最终结果的信任度。同域TL包括在来自身体各个部位的大量带标签X射线图像上训练预训练模型，并在肩部X射线图像的目标数据集上对其进行微调。特征融合将提取的特征与七个DL模型相结合，以训练多个ML分类器。所提出的框架实现了99.2%的出色准确率、99.2%的F1分数和98.5%的科恩kappa系数。此外，使用三种可视化工具（包括基于梯度的类激活热图（Grad CAM）、激活可视化和局部可解释模型无关解释（LIME））对结果的准确性进行了验证。所提出的框架优于以前的DL方法以及受邀对测试集进行分类的三位骨科医生，他们的平均准确率为79.1%。所提出的框架已证明有效且稳健，提高了泛化能力并增加了对最终结果的信任度。
