Identification and diagnosis of meniscus tear by magnetic resonance imaging using a deep learning model

Li, Jie; Qian, Kun; Liu, Jinyong; Huang, Zhijun; Zhang, Yuchen; Zhao, Guoqing; Wang, Huifen; Li, Meng; Liang, Xiaohan; Zhou, Fang; Yu, Xiaomin; Li, Lan; Wang, Xingsong; Yang, Xianfeng; Jiang, Qing

doi:10.1016/j.jot.2022.05.006

Cited by 8 publications

(24 citation statements)

References 24 publications

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“…They were thoroughly screened, and 24 out of them were selected, and 13 were excluded since they did not have sufficient data to qualify for the systematic review. Finally, 24 studies were included in the systematic review, the characteristics of which are shown in The countries where the included studies were conducted were France [22,32,34], Switzerland [25,28], Ireland [29], USA [13,20,24,26,30,31,33,35], Turkey [36] and Asia [18,19,21,23,27,[37][38][39][40]. The study period in those studies varied from five years to 18 years.…”

Section: Search Resultsmentioning

confidence: 99%

“…Anterior cruciate ligament (ACL) injuries were present in 16 studies [13,18,20,21,23,24,[26][27][28][29][30][31]36,37,40], meniscus injuries in 12 studies [13,19,[22][23][24][25]29,[32][33][34][38][39][40], and cartilage lesion in two studies [24,35].…”

Section: Study Outcomesmentioning

confidence: 99%

“…The countries where the included studies were conducted were France [ 22 , 32 , 34 ], Switzerland [ 25 , 28 ], Ireland [ 29 ], USA [ 13 , 20 , 24 , 26 , 30 , 31 , 33 , 35 ], Turkey [ 36 ] and Asia [ 18 , 19 , 21 , 23 , 27 , 37 - 40 ]. The study period in those studies varied from five years to 18 years.…”

Section: Reviewmentioning

confidence: 99%

“…In seven studies, the risk of bias was low [ 19 , 22 , 26 , 29 , 33 , 38 , 39 ] and in the remaining, i.e. 17 studies, the moderate risk was present [ 13 , 18 , 20 , 21 , 23 - 25 , 27 , 28 , 30 - 32 , 34 - 37 , 40 ].…”

Section: Reviewmentioning

confidence: 99%

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A Comprehensive Review on the Diagnosis of Knee Injury by Deep Learning-Based Magnetic Resonance Imaging

Shetty,

Dhande,

Unadkat

et al. 2023

Cureus

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The continual improvement in the field of medical diagnosis has led to the monopoly of using deep learning (DL)-based magnetic resonance imaging (MRI) for the diagnosis of knee injury related to meniscal injury, ligament injury including the cruciate ligaments, collateral ligaments and medial patella-femoral ligament, and cartilage injury. The present systematic review was done by PubMed and Directory of Open Access Journals (DOAJ), wherein we finalised 24 studies conducted on the accuracy of DL MRI studies for knee injury identification. The studies showed an accuracy of 72.5% to 100% indicating that DL MRI holds an equivalent performance as humans in decision-making and management of knee injuries. This further opens up future exploration for improving MRI-based diagnosis keeping in mind the limitations of verification bias and data imbalance in ground truth subjectivity.

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Section: Search Resultsmentioning

confidence: 99%

Section: Study Outcomesmentioning

confidence: 99%

Section: Reviewmentioning

confidence: 99%

Section: Reviewmentioning

confidence: 99%

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A Comprehensive Review on the Diagnosis of Knee Injury by Deep Learning-Based Magnetic Resonance Imaging

Shetty,

Dhande,

Unadkat

et al. 2023

Cureus

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“…In the examination of various studies, it was observed that five out of the total studies (42%) employed a 1.5 T MRI field to acquire knee scans [12,43,45,48,49]. Additionally, nine studies (75%) utilized a 3 T MRI field for knee scans [12,13,[43][44][45][46][47]49,50], while four studies (33%) opted for a combination of both 1.5 T and 3 T MRI for knee examinations [12,43,45,49]. Two studies did not provide information regarding the MRI field used [41,42].…”

Section: Knee Mri Sequence Acquisitionmentioning

confidence: 99%

A Comprehensive Evaluation of Deep Learning Models on Knee MRIs for the Diagnosis and Classification of Meniscal Tears: A Systematic Review and Meta-Analysis

Botnari,

Kadar,

Patrascu

2024

Diagnostics

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Objectives: This study delves into the cutting-edge field of deep learning techniques, particularly deep convolutional neural networks (DCNNs), which have demonstrated unprecedented potential in assisting radiologists and orthopedic surgeons in precisely identifying meniscal tears. This research aims to evaluate the effectiveness of deep learning models in recognizing, localizing, describing, and categorizing meniscal tears in magnetic resonance images (MRIs). Materials and methods: This systematic review was rigorously conducted, strictly following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Extensive searches were conducted on MEDLINE (PubMed), Web of Science, Cochrane Library, and Google Scholar. All identified articles underwent a comprehensive risk of bias analysis. Predictive performance values were either extracted or calculated for quantitative analysis, including sensitivity and specificity. The meta-analysis was performed for all prediction models that identified the presence and location of meniscus tears. Results: This study’s findings underscore that a range of deep learning models exhibit robust performance in detecting and classifying meniscal tears, in one case surpassing the expertise of musculoskeletal radiologists. Most studies in this review concentrated on identifying tears in the medial or lateral meniscus and even precisely locating tears—whether in the anterior or posterior horn—with exceptional accuracy, as demonstrated by AUC values ranging from 0.83 to 0.94. Conclusions: Based on these findings, deep learning models have showcased significant potential in analyzing knee MR images by learning intricate details within images. They offer precise outcomes across diverse tasks, including segmenting specific anatomical structures and identifying pathological regions. Contributions: This study focused exclusively on DL models for identifying and localizing meniscus tears. It presents a meta-analysis that includes eight studies for detecting the presence of a torn meniscus and a meta-analysis of three studies with low heterogeneity that localize and classify the menisci. Another novelty is the analysis of arthroscopic surgery as ground truth. The quality of the studies was assessed against the CLAIM checklist, and the risk of bias was determined using the QUADAS-2 tool.

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Artificial intelligence applied to magnetic resonance imaging reliably detects the presence, but not the location, of meniscus tears: a systematic review and meta-analysis

Zhao,

Coppola,

Karamchandani

et al. 2024

Eur Radiol

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Objectives To review and compare the accuracy of convolutional neural networks (CNN) for the diagnosis of meniscal tears in the current literature and analyze the decision-making processes utilized by these CNN algorithms. Materials and methods PubMed, MEDLINE, EMBASE, and Cochrane databases up to December 2022 were searched in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) statement. Risk of analysis was used for all identified articles. Predictive performance values, including sensitivity and specificity, were extracted for quantitative analysis. The meta-analysis was divided between AI prediction models identifying the presence of meniscus tears and the location of meniscus tears. Results Eleven articles were included in the final review, with a total of 13,467 patients and 57,551 images. Heterogeneity was statistically significantly large for the sensitivity of the tear identification analysis (I2 = 79%). A higher level of accuracy was observed in identifying the presence of a meniscal tear over locating tears in specific regions of the meniscus (AUC, 0.939 vs 0.905). Pooled sensitivity and specificity were 0.87 (95% confidence interval (CI) 0.80–0.91) and 0.89 (95% CI 0.83–0.93) for meniscus tear identification and 0.88 (95% CI 0.82–0.91) and 0.84 (95% CI 0.81–0.85) for locating the tears. Conclusions AI prediction models achieved favorable performance in the diagnosis, but not location, of meniscus tears. Further studies on the clinical utilities of deep learning should include standardized reporting, external validation, and full reports of the predictive performances of these models, with a view to localizing tears more accurately. Clinical relevance statement Meniscus tears are hard to diagnose in the knee magnetic resonance images. AI prediction models may play an important role in improving the diagnostic accuracy of clinicians and radiologists. Key Points •Artificial intelligence (AI) provides great potential in improving the diagnosis of meniscus tears. •The pooled diagnostic performance for artificial intelligence (AI) in identifying meniscus tears was better (sensitivity 87%, specificity 89%) than locating the tears (sensitivity 88%, specificity 84%). •AI is good at confirming the diagnosis of meniscus tears, but future work is required to guide the management of the disease.

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Identification and diagnosis of meniscus tear by magnetic resonance imaging using a deep learning model

Cited by 8 publications

References 24 publications

A Comprehensive Review on the Diagnosis of Knee Injury by Deep Learning-Based Magnetic Resonance Imaging

A Comprehensive Review on the Diagnosis of Knee Injury by Deep Learning-Based Magnetic Resonance Imaging

A Comprehensive Evaluation of Deep Learning Models on Knee MRIs for the Diagnosis and Classification of Meniscal Tears: A Systematic Review and Meta-Analysis

Artificial intelligence applied to magnetic resonance imaging reliably detects the presence, but not the location, of meniscus tears: a systematic review and meta-analysis

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