ArticleDiagnostics (Basel, Switzerland)2021
Efficient Detection of Knee Anterior Cruciate Ligament from Magnetic Resonance Imaging Using Deep Learning Approach.
Article in Diagnostics (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 53 papers, 4 of them syntheses that pooled it.
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Who cites it
53 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
- Artificial Intelligence in Anterior Cruciate Ligament Tear Diagnosis: A Bibliometric Analysis of the 50 Most Cited Studies.The Indian journal of radiology & imaging · 2026Review
- Revolutionizing emergency care: an overview of the transformative role of artificial intelligence in diagnosis, triage, and patient management.International journal of emergency medicine · 2025Review
- Classification of anterior cruciate ligament tears in knee magnetic resonance images using pre-trained model and custom model.Scientific reports · 2025Article
- Magnetic resonance imaging of cruciate ligament disorders: current updates.EFORT open reviews · 2025Review
- Anterior cruciate ligament tear detection based on Res2Net modified by improved Lévy flight distribution.Scientific reports · 2025Article
- A metaheuristic optimization-based approach for accurate prediction and classification of knee osteoarthritis.Scientific reports · 2025Article
- Advanced Deep Learning and Machine Learning Techniques for MRI Brain Tumor Analysis: A Review.Sensors (Basel, Switzerland) · 2025Review
- Deep Learning Models to Detect Anterior Cruciate Ligament Injury on MRI: A Comprehensive Review.Diagnostics (Basel, Switzerland) · 2025Review
- Fully and Weakly Supervised Deep Learning for Meniscal Injury Classification, and Location Based on MRI.Journal of imaging informatics in medicine · 2025Article
- A new method for early diagnosis and treatment of meniscus injury of knee joint in student physical fitness tests based on deep learning method.BioImpacts : BI · 2025Article
- Hierarchical classification of anterior cruciate ligament using deep learning for athletes healthcare.BioImpacts : BI · 2025Article
- Federated knee injury diagnosis using few shot learning.Frontiers in artificial intelligence · 2025Article
- Automatic segmentation of extensor carpi ulnaris tendon and detection of tendinosis with convolutional neural networks.Acta radiologica open · 2024Article
- Artificial intelligence in musculoskeletal imaging: realistic clinical applications in the next decade.Skeletal radiology · 2024Review
- Anterior Cruciate Ligament Tear Detection Based on T-Distribution Slice Attention Framework with Penalty Weight Loss Optimisation.Bioengineering (Basel, Switzerland) · 2024Article
- 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
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The most commonly injured ligament in the human body is an anterior cruciate ligament (ACL). ACL injury is standard among the football, basketball and soccer players. The study aims to detect anterior cruciate ligament injury in an early stage via efficient and thorough automatic magnetic resonance imaging without involving radiologists, through a deep learning method. The proposed approach in this paper used a customized 14 layers ResNet-14 architecture of convolutional neural network (CNN) with six different directions by using class balancing and data augmentation. The performance was evaluated using accuracy, sensitivity, specificity, precision and F1 score of our customized ResNet-14 deep learning architecture with hybrid class balancing and real-time data augmentation after 5-fold cross-validation, with results of 0.920%, 0.916%, 0.946%, 0.916% and 0.923%, respectively. For our proposed ResNet-14 CNN the average area under curves (AUCs) for healthy tear, partial tear and fully ruptured tear had results of 0.980%, 0.970%, and 0.999%, respectively. The proposing diagnostic results indicated that our model could be used to detect automatically and evaluate ACL injuries in athletes using the proposed deep-learning approach.
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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.