The Use of Artificial Intelligence in the Evaluation of Knee Pathology

Garwood, Elisabeth R.; Tai, Ryan; Joshi, Ganesh; Watts, George J.

doi:10.1055/s-0039-3400264

Cited by 16 publications

(8 citation statements)

References 53 publications

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“…Deep learning (DL) models have been proposed in medical imaging over recent years for an increasing number of tasks and with improving performances, fueled by strong collaborative efforts between radiologists and data scientists. detection models (usually focused on anterior cruciate ligament (ACL), meniscal or cartilage lesions) from MRI imaging have been proposed in the literature [5]. Bien et al [6] used aggregated 2D convolutional neural networks (CNN) to detect both general abnormalities and specific diagnoses (ACL and meniscal tears) from knee MRI examinations and published their dataset, MRNet.…”

Section: Introductionmentioning

confidence: 99%

Meniscal lesion detection and characterization in adult knee MRI: A deep learning model approach with external validation

Rizk¹,

Brat²,

Zille

et al. 2021

Physica Medica

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Evaluation of a deep learning approach for the detection of meniscal tears and their characterization (presence/absence of migrated meniscal fragment). Methods: A large annotated adult knee MRI database was built combining medical expertise of radiologists and data scientists' tools. Coronal and sagittal proton density fat suppressed-weighted images of 11,353 knee MRI examinations (10,401 individual patients) paired with their standardized structured reports were retrospectively collected. After database curation, deep learning models were trained and validated on a subset of 8058 examinations. Algorithm performance was evaluated on a test set of 299 examinations reviewed by 5 musculoskeletal specialists and compared to general radiologists' reports. External validation was performed using the publicly available MRNet database. Receiver Operating Characteristic (ROC) curves results and Area Under the Curve (AUC) values were obtained on internal and external databases. Results: A combined architecture of meniscal localization and lesion classification 3D convolutional neural networks reached AUC values of 0.93 (95% CI 0.82, 0.95) for medial and 0.84 (95% CI 0.78, 0.89) for lateral meniscal tear detection, and 0.91 (95% CI 0.87, 0.94) for medial and 0.95 (95% CI 0.92, 0.97) for lateral meniscal tear migration detection. External validation of the combined medial and lateral meniscal tear detection models resulted in an AUC of 0.83 (95% CI 0.75, 0.90) without further training and 0.89 (95% CI 0.82, 0.95) with fine tuning. Conclusion:Our deep learning algorithm demonstrated high performance in knee menisci lesion detection and characterization, validated on an external database.

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Section: Introductionmentioning

confidence: 99%

Meniscal lesion detection and characterization in adult knee MRI: A deep learning model approach with external validation

Rizk¹,

Brat²,

Zille

et al. 2021

Physica Medica

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“…The final result proposed an effective algorithm for the detection of PHF, and Chung further noted that the AI was able to improve the Neer classification, which brought profound significance for clinical PHF diagnosis and treatment [23] . Besides fracture detection, AI technology has also played an inspiring role in the diagnosis of other orthopedic diseases, such as scoliosis, arthritis, bone tumors, and meniscus and ligament injuries [24][25][26] . Taken together, these previous studies have confirmed the validity of AI-aided methods for clinical diagnosis.…”

Section: Discussionmentioning

confidence: 99%

Artificial Intelligence to Diagnose Tibial Plateau Fractures: An Intelligent Assistant for Orthopedic Physicians

et al. 2021

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Objective To explore a new artificial intelligence (AI)-aided method to assist the clinical diagnosis of tibial plateau fractures (TPFs) and further measure its validity and feasibility. Methods A total of 542 X-rays of TPFs were collected as a reference database. An AI algorithm (RetinaNet) was trained to analyze and detect TPF on the X-rays. The ability of the AI algorithm was determined by indexes such as detection accuracy and time taken for analysis. The algorithm performance was also compared with orthopedic physicians. Results The AI algorithm showed a detection accuracy of 0.91 for the identification of TPF, which was similar to the performance of orthopedic physicians (0.92±0.03). The average time spent for analysis of the AI was 0.56 s, which was 16 times faster than human performance (8.44±3.26 s). Conclusion The AI algorithm is a valid and efficient method for the clinical diagnosis of TPF. It can be a useful assistant for orthopedic physicians, which largely promotes clinical workflow and further guarantees the health and security of patients.

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“…The gap of the existing reviews [ 8 , 10 , 13 , 24 , 28 , 41 – 45 ] is that none of the reviews highlighted 3D CNN as well as its importance in OA studies. In addition, most of the review papers focused on knee anatomical segmentation approaches instead of classification approaches in OA diagnosis.…”

Section: Gap Of Knowledgementioning

confidence: 99%

“…Recent studies have adapted artificial intelligence (AI) and have increasingly recognized the role of deep learning in the medical field, including computer-aided knee OA diagnosis [ 10 , 11 ] which is aimed to reduce uncertainties in diagnosis due to human error [ 12 ]. The significant motivation in the development of AI in OA research is the availability of huge repositories of clinical and imaging data such as through Osteoarthritis Initiative (OAI) [ 13 ]. There are different types of architecture of deep learning such as convolutional neural network (CNN), recurrent neural network (RNN), recursive neural network, and unsupervised pretrained network (UPN) [ 8 ].…”

Section: Introductionmentioning

confidence: 99%

Emergence of Deep Learning in Knee Osteoarthritis Diagnosis

Yeoh

Lai

Goh

et al. 2021

Computational Intelligence and Neuroscience

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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.

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The Use of Artificial Intelligence in the Evaluation of Knee Pathology

Cited by 16 publications

References 53 publications

Meniscal lesion detection and characterization in adult knee MRI: A deep learning model approach with external validation

Meniscal lesion detection and characterization in adult knee MRI: A deep learning model approach with external validation

Artificial Intelligence to Diagnose Tibial Plateau Fractures: An Intelligent Assistant for Orthopedic Physicians

Emergence of Deep Learning in Knee Osteoarthritis Diagnosis

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