Automatic Deep Learning–assisted Detection and Grading of                     Abnormalities in Knee MRI Studies

Astuto, Bruno; Flament, Io; Namiri, Nikan K.; Shah, Rutwik; Bharadwaj, Upasana; Link, Thomas M.; Bucknor, Matthew; Pedoia, Valentina; Majumdar, Sharmila

doi:10.1148/ryai.2021200165

Cited by 60 publications

(63 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Another work explored this area using the popular 2D U-Net architecture for the segmentation of cartilage and meniscus in the knee, which were fed into a 3D CNN for classifying the severity of the cartilage and meniscus lesions [22]. Given the large amount of volumetric data, another recent work for classifying knee lesions used cropping of 3 ROIs from knee MRI to reduce the dimensionality before processing by multiple 3D CNN [23]. Aside from these applications, 3D CNNs have also been applied to segmentation problems including knee cartilage segmentation [24] and segmentation of brain lesions [25].…”

Section: Introductionmentioning

confidence: 99%

Knee Osteoarthritis Classification Using 3D CNN and MRI

2021

View full text Add to dashboard Cite

Osteoarthritis (OA) is the most common form of arthritis and can often occur in the knee. While convolutional neural networks (CNNs) have been widely used to study medical images, the application of a 3-dimensional (3D) CNN in knee OA diagnosis is limited. This study utilizes a 3D CNN model to analyze sequences of knee magnetic resonance (MR) images to perform knee OA classification. An advantage of using 3D CNNs is the ability to analyze the whole sequence of 3D MR images as a single unit as opposed to a traditional 2D CNN, which examines one image at a time. Therefore, 3D features could be extracted from adjacent slices, which may not be detectable from a single 2D image. The input data for each knee were a sequence of double-echo steady-state (DESS) MR images, and each knee was labeled by the Kellgren and Lawrence (KL) grade of severity at levels 0–4. In addition to the 5-category KL grade classification, we further examined a 2-category classification that distinguishes non-OA (KL ≤ 1) from OA (KL ≥ 2) knees. Clinically, diagnosing a patient with knee OA is the ultimate goal of assigning a KL grade. On a dataset with 1100 knees, the 3D CNN model that classifies knees with and without OA achieved an accuracy of 86.5% on the validation set and 83.0% on the testing set. We further conducted a comparative study between MRI and X-ray. Compared with a CNN model using X-ray images trained from the same group of patients, the proposed 3D model with MR images achieved higher accuracy in both the 5-category classification (54.0% vs. 50.0%) and the 2-category classification (83.0% vs. 77.0%). The result indicates that MRI, with the application of a 3D CNN model, has greater potential to improve diagnosis accuracy for knee OA clinically than the currently used X-ray methods.

show abstract

Section: Introductionmentioning

confidence: 99%

Knee Osteoarthritis Classification Using 3D CNN and MRI

2021

View full text Add to dashboard Cite

show abstract

“…These results were of higher accuracy than the MRNet technique. Astuto et al [ 58 ] made use of 3D CNNs, which were designed to identify and grade ACL injuries in MRI investigations. The reported binary lesion sensitivity for ACL tissue is 88%.…”

Section: Resultsmentioning

confidence: 99%

“…Moreover, Dai et al utilized TransMed [ 57 ], achieving accuracy and AUC values of 94.9% and 0.98, respectively, for detecting meniscus tears, thus improving over the MRNet technique. 3D CNNs were built by Astuto et al [ 58 ] to identify and grade meniscus tear in MRI examinations. The reported binary lesion sensitivity and specificity values were 85% for both., whereas the AUC was 0.93.…”

Section: Resultsmentioning

confidence: 99%

Knee Injury Detection Using Deep Learning on MRI Studies: A Systematic Review

Siouras

Moustakidis²,

Giannakidis

et al. 2022

Diagnostics

View full text Add to dashboard Cite

The improved treatment of knee injuries critically relies on having an accurate and cost-effective detection. In recent years, deep-learning-based approaches have monopolized knee injury detection in MRI studies. The aim of this paper is to present the findings of a systematic literature review of knee (anterior cruciate ligament, meniscus, and cartilage) injury detection papers using deep learning. The systematic review was carried out following the PRISMA guidelines on several databases, including PubMed, Cochrane Library, EMBASE, and Google Scholar. Appropriate metrics were chosen to interpret the results. The prediction accuracy of the deep-learning models for the identification of knee injuries ranged from 72.5–100%. Deep learning has the potential to act at par with human-level performance in decision-making tasks related to the MRI-based diagnosis of knee injuries. The limitations of the present deep-learning approaches include data imbalance, model generalizability across different centers, verification bias, lack of related classification studies with more than two classes, and ground-truth subjectivity. There are several possible avenues of further exploration of deep learning for improving MRI-based knee injury diagnosis. Explainability and lightweightness of the deployed deep-learning systems are expected to become crucial enablers for their widespread use in clinical practice.

show abstract

“…In a study by Liu F et al, the DL model approached the output of a radiologist when detecting only the anterior cruciate ligament of the knee. 53 Other researchers like Astuto B et al, 54 Bien N et al 48 took a broader approach. They tried building a model which could perform multiple functions like identify ACL tears, bone marrow edema, meniscus tear, and cartilage abnormality.…”

Section: Mri Based DL Algorithmsmentioning

confidence: 99%

Real-world analysis of artificial intelligence in musculoskeletal trauma

Ajmera

Kharat

Botchu

et al. 2021

Journal of Clinical Orthopaedics and Trauma

View full text Add to dashboard Cite

Musculoskeletal trauma accounts for a large percentage of emergency room visits and is amongst the top causes of unscheduled patient visits to the emergency room. Musculoskeletal trauma results in expenditure of billions of dollars and protracted losses of quality-adjusted life years. New and innovative methods are needed to minimise the impact by ensuring quick and accurate assessment. However, each of the currently utilised radiological procedures, such as radiography, ultrasonography, computed tomography, and magnetic resonance imaging, has resulted in implosion of medical imaging data. Deep learning, a recent advancement in artificial intelligence, has demonstrated the potential to analyse medical images with sensitivity and specificity at par with experts. In this review article, we intend to summarise and showcase the various developments which have occurred in the dynamic field of artificial intelligence and machine learning and how their applicability to different aspects of imaging in trauma can be explored to improvise our existing reporting systems and improvise on patient outcomes.

show abstract

Automatic Deep Learning–assisted Detection and Grading of Abnormalities in Knee MRI Studies

Cited by 60 publications

References 29 publications

Knee Osteoarthritis Classification Using 3D CNN and MRI

Knee Osteoarthritis Classification Using 3D CNN and MRI

Knee Injury Detection Using Deep Learning on MRI Studies: A Systematic Review

Real-world analysis of artificial intelligence in musculoskeletal trauma

Contact Info

Product

Resources

About