Deep Learning Approach for Anterior Cruciate Ligament Lesion Detection: Evaluation of Diagnostic Performance Using Arthroscopy as the Reference Standard

Zhang, Lingyan; Li, Mifang; Zhou, Yujia; Lu, Guangming; Zhou, Quan

doi:10.1002/jmri.27266

Cited by 43 publications

(35 citation statements)

References 34 publications

(63 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Not only in the field of OA, 3D CNN has also been applied in detecting anterior cruciate ligament (ACL) injuries from MR images [ 72 ]. Moreover, ACL damage has been known to be a high-risk factor of knee OA.…”

Section: Application Of 3d Deep Learning In Knee Osteoarthritis Assessmentmentioning

confidence: 99%

See 1 more Smart Citation

Emergence of Deep Learning in Knee Osteoarthritis Diagnosis

Yeoh

Lai

Goh

et al. 2021

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

Osteoarthritis (OA), especially knee OA, is the most common form of arthritis, causing significant disability in patients worldwide. Manual diagnosis, segmentation, and annotations of knee joints remain as the popular method to diagnose OA in clinical practices, although they are tedious and greatly subject to user variation. Therefore, to overcome the limitations of the commonly used method as above, numerous deep learning approaches, especially the convolutional neural network (CNN), have been developed to improve the clinical workflow efficiency. Medical imaging processes, especially those that produce 3-dimensional (3D) images such as MRI, possess ability to reveal hidden structures in a volumetric view. Acknowledging that changes in a knee joint is a 3D complexity, 3D CNN has been employed to analyse the joint problem for a more accurate diagnosis in the recent years. In this review, we provide a broad overview on the current 2D and 3D CNN approaches in the OA research field. We reviewed 74 studies related to classification and segmentation of knee osteoarthritis from the Web of Science database and discussed the various state-of-the-art deep learning approaches proposed. We highlighted the potential and possibility of 3D CNN in the knee osteoarthritis field. We concluded by discussing the possible challenges faced as well as the potential advancements in adopting 3D CNNs in this field.

show abstract

Section: Application Of 3d Deep Learning In Knee Osteoarthritis Assessmentmentioning

confidence: 99%

“…Moreover, ACL damage has been known to be a high-risk factor of knee OA. Zhang et al [ 72 ] have demonstrated that 3D DenseNet outperformed VGG16 and ResNet due to its low complexity and excellent anti-overfitting performance.…”

Section: Application Of 3d Deep Learning In Knee Osteoarthritis Assessmentmentioning

confidence: 99%

Emergence of Deep Learning in Knee Osteoarthritis Diagnosis

Yeoh

Lai

Goh

et al. 2021

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

show abstract

“…Knee injuries account for the largest percentage of sport-related, severe injuries (i.e., injuries that cause more than 21 days of missed sport participation) [ 1 , 2 , 3 , 4 ]. Anterior cruciate ligament (ACL) ruptures represent more than 50% of the cases, affecting 200,000 individuals in the United States each year [ 1 , 5 , 6 , 7 ]. Knee cartilage lesions affect around 900,000 individuals in the United States every year, resulting in over 200,000 surgical procedures [ 5 , 6 , 7 , 8 ].…”

Section: Introductionmentioning

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 2020, a study described a customized CNN for diagnosing ACL tears using sagittal fat-suppressed proton densityweighted 1.5-T and 3-T MR images [13]. The CNN achieved a sensitivity of 98%, specificity of 94%, and accuracy of 96%.…”

Section: Introductionmentioning

confidence: 99%

Artificial intelligence for MRI diagnosis of joints: a scoping review of the current state-of-the-art of deep learning-based approaches

Fritz

2021

Skeletal Radiol

View full text Add to dashboard Cite

Deep learning-based MRI diagnosis of internal joint derangement is an emerging field of artificial intelligence, which offers many exciting possibilities for musculoskeletal radiology. A variety of investigational deep learning algorithms have been developed to detect anterior cruciate ligament tears, meniscus tears, and rotator cuff disorders. Additional deep learning-based MRI algorithms have been investigated to detect Achilles tendon tears, recurrence prediction of musculoskeletal neoplasms, and complex segmentation of nerves, bones, and muscles. Proof-of-concept studies suggest that deep learning algorithms may achieve similar diagnostic performances when compared to human readers in meta-analyses; however, musculoskeletal radiologists outperformed most deep learning algorithms in studies including a direct comparison. Earlier investigations and developments of deep learning algorithms focused on the binary classification of the presence or absence of an abnormality, whereas more advanced deep learning algorithms start to include features for characterization and severity grading. While many studies have focused on comparing deep learning algorithms against human readers, there is a paucity of data on the performance differences of radiologists interpreting musculoskeletal MRI studies without and with artificial intelligence support. Similarly, studies demonstrating the generalizability and clinical applicability of deep learning algorithms using realistic clinical settings with workflow-integrated deep learning algorithms are sparse. Contingent upon future studies showing the clinical utility of deep learning algorithms, artificial intelligence may eventually translate into clinical practice to assist detection and characterization of various conditions on musculoskeletal MRI exams.

show abstract

Deep Learning Approach for Anterior Cruciate Ligament Lesion Detection: Evaluation of Diagnostic Performance Using Arthroscopy as the Reference Standard

Cited by 43 publications

References 34 publications

Emergence of Deep Learning in Knee Osteoarthritis Diagnosis

Emergence of Deep Learning in Knee Osteoarthritis Diagnosis

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

Artificial intelligence for MRI diagnosis of joints: a scoping review of the current state-of-the-art of deep learning-based approaches

Contact Info

Product

Resources

About