SynthesisSkeletal radiology2024
Systematic review of artificial intelligence development and evaluation for MRI diagnosis of knee ligament or meniscus tears.
Synthesis in Skeletal radiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
What it found
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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
14 citing papers in PubMed, 1 synthesis or guideline pooled it, 14 citations in OpenAlex.
- 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
- Magnetic Resonance Imaging-Arthroscopy Correlation in Traumatic Knee Injuries: A Retrospective Diagnostic Accuracy Study.Journal of orthopaedic case reports · 2026Article
- Artificial Intelligence in Anterior Cruciate Ligament Tear Diagnosis: A Bibliometric Analysis of the 50 Most Cited Studies.The Indian journal of radiology & imaging · 2026Review
- Comprehensive deep learning-assisted multi-condition analysis of knee MRI studies improves resident radiologist performance.European radiology · 2026Article
- [Current Landscape and Commercialization of AI Models in Musculoskeletal Imaging].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
- MV2SwimNet: A lightweight transformer-based hybrid model for knee meniscus tears detection.PloS one · 2025Article
- Accuracy and Reliability of Multimodal Imaging in Diagnosing Knee Sports InjuriesCurrent medical imaging · 2025Article
- Artificial intelligence in musculoskeletal radiology: practical aspects and latest perspectives.BJR open · 2025Review
- Artificial intelligence in musculoskeletal imaging: realistic clinical applications in the next decade.Skeletal radiology · 2024Review
- A Comprehensive Evaluation of Deep Learning Models on Knee MRIs for the Diagnosis and Classification of Meniscal Tears: A Systematic Review and Meta-Analysis.Diagnostics (Basel, Switzerland) · 2024Review
- The Role of Artificial Intelligence in Anterior Cruciate Ligament Injuries: Current Concepts and Future Perspectives.Healthcare (Basel, Switzerland) · 2024Review
- Use of artificial intelligence in the field of pain medicine.World journal of clinical cases · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveThe purpose of this systematic review was to summarize the results of original research studies evaluating the characteristics and performance of deep learning models for detection of knee ligament and meniscus tears on MRI. MATERIALS AND
methodsWe searched PubMed for studies published as of February 2, 2022 for original studies evaluating development and evaluation of deep learning models for MRI diagnosis of knee ligament or meniscus tears. We summarized study details according to multiple criteria including baseline article details, model creation, deep learning details, and model evaluation.
results19 studies were included with radiology departments leading the publications in deep learning development and implementation for detecting knee injuries via MRI. Among the studies, there was a lack of standard reporting and inconsistently described development details. However, all included studies reported consistently high model performance that significantly supplemented human reader performance.
conclusionFrom our review, we found radiology departments have been leading deep learning development for injury detection on knee MRIs. Although studies inconsistently described DL model development details, all reported high model performance, indicating great promise for DL in knee MRI analysis.
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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.