ArticleNature communications2024
Learning co-plane attention across MRI sequences for diagnosing twelve types of knee abnormalities.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 2 of them syntheses that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
9 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Artificial intelligence demonstrates comparable diagnostic accuracy to radiologists for anterior cruciate ligament tears on MRI: A systematic review and meta-analysis.Knee surgery, sports traumatology, arthroscopy : official journal of the ESSKA · 2026Pooled it
- AI demonstrates comparable diagnostic performance to radiologists in MRI detection of anterior cruciate ligament tears: a systematic review and meta-analysis.European radiology · 2026Pooled it
- Prompt mechanisms in medical imaging: A comprehensive survey.Innovation (Cambridge (Mass.)) · 2026Review
- Advancing deep learning based knee cartilage segmentation in MRI: Innovations, challenges and applications.Osteoarthritis and cartilage open · 2026Article
- MRI-based morphometric analysis of the patellofemoral joint: diagnostic modeling of knee pathologies in adolescents.Frontiers in artificial intelligence · 2026Article
- Artificial intelligence in osteoarthritis research: summary of the 2025 OARSI pre-congress workshop.Osteoarthritis and cartilage open · 2025Article
- Multi-modal and Multi-view Cervical Spondylosis Imaging Dataset.Scientific data · 2025Article
- Leveraging spatial dependencies and multi-scale features for automated knee injury detection on MRI diagnosis.Frontiers in bioengineering and biotechnology · 2025Article
- Learning co-plane attention across MRI sequences for diagnosing twelve types of knee abnormalities.Nature communications · 2024Article
Corrections and comments
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Authors and funding
9 authors.
Funding
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 < 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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.