Department of Computer Science and Engineering, The Hong Kong University of Science and Technology, Hong Kong, China.
Department of Radiology, The Third Affiliated Hospital of Southern Medical University (Academy of Orthopedics, Guangdong Province), Guangzhou, Guangdong, China.
Nat Commun. 2024 Sep 2;15(1):7637. doi: 10.1038/s41467-024-51888-4.
Multi-sequence magnetic resonance imaging is crucial in accurately identifying knee abnormalities but requires substantial expertise from radiologists to interpret. Here, we introduce a deep learning model incorporating co-plane attention across image sequences to classify knee abnormalities. To assess the effectiveness of our model, we collected the largest multi-sequence knee magnetic resonance imaging dataset involving the most comprehensive range of abnormalities, comprising 1748 subjects and 12 types of abnormalities. Our model achieved an overall area under the receiver operating characteristic curve score of 0.812. It achieved an average accuracy of 0.78, outperforming junior radiologists (accuracy 0.65) and remains competitive with senior radiologists (accuracy 0.80). Notably, with the assistance of model output, the diagnosis accuracy of all radiologists was improved significantly (p < 0.001), elevating from 0.73 to 0.79 on average. The interpretability analysis demonstrated that the model decision-making process is consistent with the clinical knowledge, enhancing its credibility and reliability in clinical practice.
多序列磁共振成像是准确识别膝关节异常的关键，但需要放射科医生具备丰富的专业知识来进行解读。在这里，我们引入了一个深度学习模型，该模型在图像序列中结合了共面注意力，以对膝关节异常进行分类。为了评估我们模型的有效性，我们收集了最大的多序列膝关节磁共振成像数据集，其中包含最全面的异常类型，涉及 1748 名受试者和 12 种异常。我们的模型在接收者操作特征曲线下的整体面积评分为 0.812。它的平均准确率为 0.78，优于初级放射科医生（准确率为 0.65），并与高级放射科医生（准确率为 0.80）保持竞争力。值得注意的是，在模型输出的辅助下，所有放射科医生的诊断准确性都显著提高（p<0.001），平均从 0.73 提高到 0.79。可解释性分析表明，该模型的决策过程与临床知识一致，从而提高了其在临床实践中的可信度和可靠性。
