ArticleSensors (Basel, Switzerland)2022
Automated Knee MR Images Segmentation of Anterior Cruciate Ligament Tears.
Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
19 citing papers in PubMed, 40 citations in OpenAlex.
- Utilization of machine learning to identify lower extremity biomechanical predictors of rupture in a validated cadaveric model of ACL injury.Scientific reports · 2026Article
- Magnetic resonance imaging of cruciate ligament disorders: current updates.EFORT open reviews · 2025Review
- [Segmentation of anterior cruciate ligament images by fusing inflated convolution and residual hybrid attention].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2025Article
- Hierarchical classification of anterior cruciate ligament using deep learning for athletes healthcare.BioImpacts : BI · 2025Article
- DeSPPNet: A Multiscale Deep Learning Model for Cardiac Segmentation.Diagnostics (Basel, Switzerland) · 2024Article
- Clinico-radiological outcome of Arthroscopic Anterior Cruciate Ligament Reconstruction with Augmentation of Dehydrated Human Amnion Chorion Allograft Membrane using Peroneus Longus Autograft.Malaysian orthopaedic journal · 2024Article
- The Role of Artificial Intelligence in Anterior Cruciate Ligament Injuries: Current Concepts and Future Perspectives.Healthcare (Basel, Switzerland) · 2024Review
- Hybrid Techniques of X-ray Analysis to Predict Knee Osteoarthritis Grades Based on Fusion Features of CNN and Handcrafted.Diagnostics (Basel, Switzerland) · 2023Article
- Application of the Model of Spots for Inverse Problems.Sensors (Basel, Switzerland) · 2023Article
- Deep Learning-Based Medical Images Segmentation of Musculoskeletal Anatomical Structures: A Survey of Bottlenecks and Strategies.Bioengineering (Basel, Switzerland) · 2023Review
- MGACA-Net: a novel deep learning based multi-scale guided attention and context aggregation for localization of knee anterior cruciate ligament tears region in MRI images.PeerJ. Computer science · 2023Article
- HaTU-Net: Harmonic Attention Network for Automated Ovarian Ultrasound Quantification in Assisted Pregnancy.Diagnostics (Basel, Switzerland) · 2022Article
- Attachment Type, Thickness, and Volume of the Posterior Meniscofemoral Ligament and Meniscal Pathology.Journal of digital imaging · 2022Article
- Atlas-Based Segmentation in Extraction of Knee Joint Bone Structures from CT and MR.Sensors (Basel, Switzerland) · 2022Article
- Demystifying Supervised Learning in Healthcare 4.0: A New Reality of Transforming Diagnostic Medicine.Diagnostics (Basel, Switzerland) · 2022Review
- Multiresolution Aggregation Transformer UNet Based on Multiscale Input and Coordinate Attention for Medical Image Segmentation.Sensors (Basel, Switzerland) · 2022Article
- Automatic Segmentation of Lumbar Spine MRI Images Based on Improved Attention U-Net.Computational intelligence and neuroscience · 2022Article
- Machine Learning-Based Performance Comparison to Diagnose Anterior Cruciate Ligament Tears.Journal of healthcare engineering · 2022Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 3 institutions in 4 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The anterior cruciate ligament (ACL) is one of the main stabilizer parts of the knee. ACL injury leads to causes of osteoarthritis risk. ACL rupture is common in the young athletic population. Accurate segmentation at an early stage can improve the analysis and classification of anterior cruciate ligaments tears. This study automatically segmented the anterior cruciate ligament (ACL) tears from magnetic resonance imaging through deep learning. The knee mask was generated on the original Magnetic Resonance (MR) images to apply a semantic segmentation technique with convolutional neural network architecture U-Net. The proposed segmentation method was measured by accuracy, intersection over union (IoU), dice similarity coefficient (DSC), precision, recall and F1-score of 98.4%, 99.0%, 99.4%, 99.6%, 99.6% and 99.6% on 11451 training images, whereas on the validation images of 3817 was, respectively, 97.7%, 93.8%,96.8%, 96.5%, 97.3% and 96.9%. We also provide dice loss of training and test datasets that have remained 0.005 and 0.031, respectively. The experimental results show that the ACL segmentation on JPEG MRI images with U-Nets achieves accuracy that outperforms the human segmentation. The strategy has promising potential applications in medical image analytics for the segmentation of knee ACL tears for MR images.
Indexed as
Identifiers
What OpenQuestion holds
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.