Automatic knee meniscus tear detection and orientation classification with Mask-RCNN

Couteaux, Vincent; Si‐Mohamed, Salim; Nempont, Olivier; Lefèvre, Thierry; Popoff, Alexandre; Pizaine, Guillaume; Villain, Nicolas; Bloch, Isabelle; Cotten, Anne; Boussel, Loîc

doi:10.1016/j.diii.2019.03.002

Cited by 102 publications

(81 citation statements)

References 5 publications

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“…Thanks to the availability of big datasets and computational power, a number of artificial intelligent (AI)-based solutions have been developed during the last decade and successfully implemented for medical imaging applications [17,18]. Machine learning (ML) is a subset of AI, providing a promising tool for the development of diagnostic, prognostic and predictive modelling tools from multimodality medical image data [19,20].…”

Section: Introductionmentioning

confidence: 99%

Radiomics for classification of bone mineral loss: A machine learning study

Rastegar

Vaziri

Qasempour

et al. 2020

Diagnostic and Interventional Imaging

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Section: Introductionmentioning

confidence: 99%

Radiomics for classification of bone mineral loss: A machine learning study

Rastegar

Vaziri

Qasempour

et al. 2020

Diagnostic and Interventional Imaging

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“…This approach yielded a weighted AUC of 0.906 for the three tasks (tear detection, orientation, and anatomical location). 44 Roblot et al also performed these three tasks but on a larger data set, also using a CNN-based approach, yielding a weighted AUC of 0.90. 45…”

Section: Meniscus Meniscus Evaluation: Mrimentioning

confidence: 99%

The Use of Artificial Intelligence in the Evaluation of Knee Pathology

Garwood

Tai

Joshi

et al. 2020

Semin Musculoskelet Radiol

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Artificial intelligence (AI) holds the potential to revolutionize the field of radiology by increasing the efficiency and accuracy of both interpretive and noninterpretive tasks. We have only just begun to explore AI applications in the diagnostic evaluation of knee pathology. Experimental algorithms have already been developed that can assess the severity of knee osteoarthritis from radiographs, detect and classify cartilage lesions, meniscal tears, and ligament tears on magnetic resonance imaging, provide automatic quantitative assessment of tendon healing, detect fractures on radiographs, and predict those at highest risk for recurrent bone tumors. This article reviews and summarizes the most current literature.

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“…The machine had an AUC of 0.91 for detecting and classifying meniscal tears. 20 Pedoia et al 18 used a U-Net CNN to segment meniscus on sagittal fat-suppressed proton density-weighted 3D FSE images in 1478 subjects. The segmented meniscus was then analyzed by a custommade classification CNN to determine the presence or absence of meniscal tears using the interpretation of an experienced radiologist as the reference standard.…”

Section: Clinical Applications Of Deep Learning In Musculoskeletal Immentioning

confidence: 99%

Deep Learning for Lesion Detection, Progression, and Prediction of Musculoskeletal Disease

Kijowski

Liu

Calivá

et al. 2019

Magnetic Resonance Imaging

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Deep learning is one of the most exciting new areas in medical imaging. This review article provides a summary of the current clinical applications of deep learning for lesion detection, progression, and prediction of musculoskeletal disease on radiographs, computed tomography (CT), magnetic resonance imaging (MRI), and nuclear medicine. Deep-learning methods have shown success for estimating pediatric bone age, detecting fractures, and assessing the severity of osteoarthritis on radiographs. In particular, the high diagnostic performance of deep-learning approaches for estimating pediatric bone age and detecting fractures suggests that the new technology may soon become available for use in clinical practice. Recent studies have also documented the feasibility of using deep-learning methods for identifying a wide variety of pathologic abnormalities on CT and MRI including internal derangement, metastatic disease, infection, fractures, and joint degeneration. However, the detection of musculoskeletal disease on CT and especially MRI is challenging, as it often requires analyzing complex abnormalities on multiple slices of image datasets with different tissue contrasts. Thus, additional technical development is needed to create deep-learning methods for reliable and repeatable interpretation of musculoskeletal CT and MRI examinations. Furthermore, the diagnostic performance of all deep-learning methods for detecting and characterizing musculoskeletal disease must be evaluated in prospective studies using large image datasets acquired at different institutions with different imaging parameters and different imaging hardware before they can be implemented in clinical practice. Level of Evidence: 5 Technical Efficacy Stage: 2

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Automatic knee meniscus tear detection and orientation classification with Mask-RCNN

Cited by 102 publications

References 5 publications

Radiomics for classification of bone mineral loss: A machine learning study

Radiomics for classification of bone mineral loss: A machine learning study

The Use of Artificial Intelligence in the Evaluation of Knee Pathology

Deep Learning for Lesion Detection, Progression, and Prediction of Musculoskeletal Disease

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