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

Siouras, Athanasios; Moustakidis, Serafeim; Giannakidis, Archontis; Chalatsis, Georgios; Liampas, Ioannis; Vlychou, Marianna; Hantes, Michael; Tasoulis, Sotiris K.; Tsaopoulos, Dimitrios

doi:10.3390/diagnostics12020537

Cited by 23 publications

(14 citation statements)

References 64 publications

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“…Performance and accuracy as seen among the studies ranged from 72.5% to 100% which is effective enough for expanding the medical experts' knowledge and diagnostic skills for knee injuries. Our findings were in line with a previous systematic review conducted by Siouras et al [41], who also quoted diagnostic accuracy of 72.5-100%, but the review was conducted on 22 studies. Another systematic review was conducted by Kunze et al [16] including 11 studies, among which five evaluated ACL tears, five assessed meniscal tears, and one study assessed both where the area under the curve (AUC) for detecting ACL tear was in the range of 0.895 to 0.980 and for meniscus tear was 0.847 to 0.910.…”

Section: Discussionsupporting

confidence: 93%

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: Discussionsupporting

confidence: 93%

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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“…Machine learning in MRI can also be used for the assessment of vertebrae, discs, and muscles in patients with lower back pain [ 66 ]. In a systematic review, Siouras and colleagues stated that AI in MRI has the potential to be on par with human-level performance and has shown a prediction accuracy ranging from 72.5–100% [ 67 ].…”

Section: Methodsmentioning

confidence: 99%

Artificial Intelligence in Orthopedic Radiography Analysis: A Narrative Review

et al. 2022

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Artificial intelligence (AI) in medicine is a rapidly growing field. In orthopedics, the clinical implementations of AI have not yet reached their full potential. Deep learning algorithms have shown promising results in computed radiographs for fracture detection, classification of OA, bone age, as well as automated measurements of the lower extremities. Studies investigating the performance of AI compared to trained human readers often show equal or better results, although human validation is indispensable at the current standards. The objective of this narrative review is to give an overview of AI in medicine and summarize the current applications of AI in orthopedic radiography imaging. Due to the different AI software and study design, it is difficult to find a clear structure in this field. To produce more homogeneous studies, open-source access to AI software codes and a consensus on study design should be aimed for.

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“…Images of internal structures, such as the brain or joints Diagnosing internal injuries or diseases (i.e., cardiovascular diseases [161], cancers [162,163], knee injuries [164])…”

Section: Mri (Image)mentioning

confidence: 99%

Survey of Transfer Learning Approaches in the Machine Learning of Digital Health Sensing Data

Chato,

Regentova

2023

JPM

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Machine learning and digital health sensing data have led to numerous research achievements aimed at improving digital health technology. However, using machine learning in digital health poses challenges related to data availability, such as incomplete, unstructured, and fragmented data, as well as issues related to data privacy, security, and data format standardization. Furthermore, there is a risk of bias and discrimination in machine learning models. Thus, developing an accurate prediction model from scratch can be an expensive and complicated task that often requires extensive experiments and complex computations. Transfer learning methods have emerged as a feasible solution to address these issues by transferring knowledge from a previously trained task to develop high-performance prediction models for a new task. This survey paper provides a comprehensive study of the effectiveness of transfer learning for digital health applications to enhance the accuracy and efficiency of diagnoses and prognoses, as well as to improve healthcare services. The first part of this survey paper presents and discusses the most common digital health sensing technologies as valuable data resources for machine learning applications, including transfer learning. The second part discusses the meaning of transfer learning, clarifying the categories and types of knowledge transfer. It also explains transfer learning methods and strategies, and their role in addressing the challenges in developing accurate machine learning models, specifically on digital health sensing data. These methods include feature extraction, fine-tuning, domain adaptation, multitask learning, federated learning, and few-/single-/zero-shot learning. This survey paper highlights the key features of each transfer learning method and strategy, and discusses the limitations and challenges of using transfer learning for digital health applications. Overall, this paper is a comprehensive survey of transfer learning methods on digital health sensing data which aims to inspire researchers to gain knowledge of transfer learning approaches and their applications in digital health, enhance the current transfer learning approaches in digital health, develop new transfer learning strategies to overcome the current limitations, and apply them to a variety of digital health technologies.

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

Cited by 23 publications

References 64 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

Artificial Intelligence in Orthopedic Radiography Analysis: A Narrative Review

Survey of Transfer Learning Approaches in the Machine Learning of Digital Health Sensing Data

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