ArticleRadiology. Artificial intelligence2020
Deep Learning for Hierarchical Severity Staging of Anterior Cruciate Ligament Injuries from MRI.
Article in Radiology. Artificial intelligence, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers, 4 of them syntheses that pooled it.
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Who cites it
34 citing papers in PubMed, 4 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
- MRI deep learning models for assisted diagnosis of knee pathologies: a systematic review.European radiology · 2025Pooled it
- Systematic review of artificial intelligence development and evaluation for MRI diagnosis of knee ligament or meniscus tears.Skeletal radiology · 2024Pooled it
- Role of Artificial Intelligence and Machine Learning in Diagnosing Knee Lesions: Where Are We Now?Cureus · 2026Review
- Recent Advances in Musculoskeletal Radiology: Bridging Innovation and Clinical Application.Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine · 2026Review
- Self-supervised learning with BYOL for anterior cruciate ligament tear detection from knee MRI.MethodsX · 2025Article
- A deep learning algorithm for automatic 3D segmentation and quantification of hamstrings musculotendon injury from MRI.Scientific reports · 2025Article
- Exploration of 3D Few-Shot Learning Techniques for Classification of Knee Joint Injuries on MR Images.Diagnostics (Basel, Switzerland) · 2025Article
- A comparative analysis of sagittal, coronal, and axial magnetic resonance imaging planes in diagnosing anterior cruciate ligament and meniscal tears via a deep learning model: emphasizing the unexpected importance of the axial plane.Quantitative imaging in medicine and surgery · 2025Article
- Large multimodality model fine-tuned for detecting breast and esophageal carcinomas on CT: a preliminary study.Japanese journal of radiology · 2025Article
- Deep Learning Models to Detect Anterior Cruciate Ligament Injury on MRI: A Comprehensive Review.Diagnostics (Basel, Switzerland) · 2025Review
- Leveraging spatial dependencies and multi-scale features for automated knee injury detection on MRI diagnosis.Frontiers in bioengineering and biotechnology · 2025Article
- Hierarchical classification of anterior cruciate ligament using deep learning for athletes healthcare.BioImpacts : BI · 2025Article
- Learning co-plane attention across MRI sequences for diagnosing twelve types of knee abnormalities.Nature communications · 2024Article
- Artificial intelligence in musculoskeletal imaging: realistic clinical applications in the next decade.Skeletal radiology · 2024Review
- One-stop detection of anterior cruciate ligament injuries on magnetic resonance imaging using deep learning with multicenter validation.Quantitative imaging in medicine and surgery · 2024Article
- Application of machine learning-based multi-sequence MRI radiomics in diagnosing anterior cruciate ligament tears.Journal of orthopaedic surgery and research · 2024Article
- The Role of Artificial Intelligence in Anterior Cruciate Ligament Injuries: Current Concepts and Future Perspectives.Healthcare (Basel, Switzerland) · 2024Review
- Unsupervised domain adaptation for automated knee osteoarthritis phenotype classification.Quantitative imaging in medicine and surgery · 2023Article
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
purposeTo evaluate the diagnostic utility of two convolutional neural networks (CNNs) for severity staging of anterior cruciate ligament (ACL) injuries. MATERIALS AND
methodsIn this retrospective study, 1243 knee MR images (1008 intact, 18 partially torn, 77 fully torn, and 140 reconstructed ACLs) from 224 patients (mean age, 47 years ± 14 [standard deviation]; 54% women) were analyzed. The MRI examinations were performed between 2011 and 2014. A modified scoring metric was used. Classification of ACL injuries using deep learning involved use of two types of CNN, one with three-dimensional (3D) and the other with two-dimensional (2D) convolutional kernels. Performance metrics included sensitivity, specificity, weighted Cohen κ, and overall accuracy, and the McNemar test was used to compare the performance of the CNNs.
resultsThe overall accuracies for ACL injury classification using the 3D CNN and 2D CNN were 89% (225 of 254) and 92% (233 of 254), respectively (
conclusionTwo-dimensional and 3D CNNs applied to ACL lesion classification had high sensitivity and specificity, suggesting that these networks could be used to help nonexperts grade ACL injuries.
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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.