ArticleKnee surgery, sports traumatology, arthroscopy : official journal of the ESSKA2025
Achieving high accuracy in meniscus tear detection using advanced deep learning models with a relatively small data set.
Article in Knee surgery, sports traumatology, arthroscopy : official journal of the ESSKA, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
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Who cites it
12 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Diagnosis of knee meniscal injuries using artificial intelligence: A systematic review and meta-analysis of diagnostic performance.PloS one · 2025Pooled it
- A stacking based deep learning framework integrating random search neural architecture search for meniscus tear diagnosis.Journal of applied clinical medical physics · 2026Article
- High diagnostic accuracy of a resnet50-based deep learning model for osteochondral lesions of the talus on magnetic resonance imaging.Joint diseases and related surgery · 2026Article
- Identification of TFCC substructure injury in wrist MRI using computer vision: a diagnostic aid for radiologists.Skeletal radiology · 2026Article
- Deep learning in detecting bucket-handle meniscal tears on knee radiographs: Comparison with surgeon interpretations.Journal of the Chinese Medical Association : JCMA · 2026Article
- Prediction of lymph node metastasis and recurrence risk in early-stage oral tongue squamous cell carcinoma with fully automated MRI deep learning.Cancer imaging : the official publication of the International Cancer Imaging Society · 2026Article
- Standard knee radiographs enable deep learning inference of MRI-defined cartilage and meniscal damage in early knee osteoarthritis: a study using the osteoarthritis initiative database.Frontiers in physiology · 2026Article
- Adapting the Dejour classification of trochlear dysplasia from qualitative radiograph- and CT-based assessments to quantitative MRI-based measurements.Knee surgery, sports traumatology, arthroscopy : official journal of the ESSKA · 2025Article
- Emerging Diagnostic Approaches for Musculoskeletal Disorders: Advances in Imaging, Biomarkers, and Clinical Assessment.Diagnostics (Basel, Switzerland) · 2025Review
- Achieving high accuracy in meniscus tear detection using advanced deep learning models with a relatively small data set.Knee surgery, sports traumatology, arthroscopy : official journal of the ESSKA · 2025Article
- A multimodal deep learning radiomics model for predicting degenerative meniscus tear after arthroscopy.PloS one · 2025Article
- Artificial intelligence in medical and biological research: promise and perils of ChatGPT and DeepSeek in advancing healthcare.Turkish journal of biology = Turk biyoloji dergisi · 2025Review
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4 authors.
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Abstract
purposeThis study aims to evaluate the effectiveness of advanced deep learning models, specifically YOLOv8 and EfficientNetV2, in detecting meniscal tears on magnetic resonance imaging (MRI) using a relatively small data set.
methodOur data set consisted of MRI studies from 642 knees-two orthopaedic surgeons labelled and annotated the MR images. The training pipeline included MRI scans of these knees. It was divided into two stages: initially, a deep learning algorithm called YOLO was employed to identify the meniscus location, and subsequently, the EfficientNetV2 deep learning architecture was utilized to detect meniscal tears. A concise report indicating the location and detection of a torn meniscus is provided at the end.
resultThe YOLOv8 model achieved mean average precision at 50% threshold (mAP@50) scores of 0.98 in the sagittal view and 0.985 in the coronal view. Similarly, the EfficientNetV2 model obtained area under the curve scores of 0.97 and 0.98 in the sagittal and coronal views, respectively. These outstanding results demonstrate exceptional performance in meniscus localization and tear detection.
conclusionDespite a relatively small data set, state-of-the-art models like YOLOv8 and EfficientNetV2 yielded promising results. This artificial intelligence system enhances meniscal injury diagnosis by generating instant structured reports, facilitating faster image interpretation and reducing physician workload. LEVEL OF EVIDENCE: Level III.
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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.