Learning co-plane attention across MRI sequences for diagnosing twelve types of knee abnormalities

Zelin Qiu, Zhuoyao Xie, Huangjing Lin, Yanwen Li, Qiang Ye, Menghong Wang, Shisi Li, Yinghua Zhao () and Hao Chen ()
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Zelin Qiu: The Hong Kong University of Science and Technology
Zhuoyao Xie: The Third Affiliated Hospital of Southern Medical University (Academy of Orthopedics, Guangdong Province)
Huangjing Lin: Imsight Technology Co., Ltd.
Yanwen Li: Imsight Technology Co., Ltd.
Qiang Ye: The Third Affiliated Hospital of Southern Medical University (Academy of Orthopedics, Guangdong Province)
Menghong Wang: The Third Affiliated Hospital of Southern Medical University (Academy of Orthopedics, Guangdong Province)
Shisi Li: The Third Affiliated Hospital of Southern Medical University (Academy of Orthopedics, Guangdong Province)
Yinghua Zhao: The Third Affiliated Hospital of Southern Medical University (Academy of Orthopedics, Guangdong Province)
Hao Chen: The Hong Kong University of Science and Technology

Nature Communications, 2024, vol. 15, issue 1, 1-11

Abstract: Abstract 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

Date: 2024
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DOI: 10.1038/s41467-024-51888-4

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