ArticleScientific reports2024
Approaching expert-level accuracy for differentiating ACL tear types on MRI with deep learning.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 2 of them syntheses that pooled it.
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Who cites it
11 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
- Comprehensive deep learning-assisted multi-condition analysis of knee MRI studies improves resident radiologist performance.European radiology · 2026Article
- 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
- Segmentation and diagnosis of anterior cruciate ligament tear using deep learning and radiomics based on knee CT.BMC medical imaging · 2026Article
- Limited Agreement on ACL Tear Location Between Arthroscopy and MRI: A Prospective Evaluation.Orthopaedic journal of sports medicine · 2025Article
- Classification of anterior cruciate ligament tears in knee magnetic resonance images using pre-trained model and custom model.Scientific reports · 2025Article
- AI for Lesion Detection in Musculoskeletal Radiology.Journal of the Korean Society of Radiology · 2025Review
- 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
- Deep Learning Models to Detect Anterior Cruciate Ligament Injury on MRI: A Comprehensive Review.Diagnostics (Basel, Switzerland) · 2025Review
- Hierarchical classification of anterior cruciate ligament using deep learning for athletes healthcare.BioImpacts : BI · 2025Article
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Authors and funding
8 authors.
Funding
Abstract
Treatment for anterior cruciate ligament (ACL) tears depends on the condition of the ligament. We aimed to identify different tear statuses from preoperative MRI using deep learning-based radiomics with sex and age. We reviewed 862 patients with preoperative MRI scans reflecting ACL status from Hunan Provincial People's Hospital. Based on sagittal proton density-weighted images, a fully automated approach was developed that consisted of a deep learning model for segmenting ACL tissue (ACL-DNet) and a deep learning-based recognizer for ligament status classification (ACL-SNet). The efficacy of the proposed approach was evaluated by using the sensitivity, specificity and area under the receiver operating characteristic curve (AUC) and compared with that of a group of three orthopedists in the holdout test set. The ACL-DNet model yielded a Dice coefficient of 98% ± 6% on the MRI datasets. Our proposed classification model yielded a sensitivity of 97% and a specificity of 97%. In comparison, the sensitivity of alternative models ranged from 84 to 90%, while the specificity was between 86 and 92%. The AUC of the ACL-SNet model was 99%, demonstrating high overall diagnostic accuracy. The diagnostic performance of the clinical experts as reflected in the AUC was 96%, 92% and 88%, respectively. The fully automated model shows potential as a highly reliable and reproducible tool that allows orthopedists to noninvasively identify the ACL status and may aid in optimizing different techniques, such as ACL remnant preservation, for ACL reconstruction.
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