ViVGG19: Novel exemplar deep feature extraction-based shoulder rotator cuff tear and biceps tendinosis detection using magnetic resonance images

Key, Sefa; Demir, Sukru; Gürger, Murat; Yılmaz, Erhan; Barua, Prabal Datta; Doğan, Şengül; Tuncer, Türker; Arunkumar, N.; Tan, Ru San; Acharya, U. Rajendra

doi:10.1016/j.medengphy.2022.103864

Cited by 10 publications

(4 citation statements)

References 20 publications

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“…In this model, MRA data from 636 patients were used for training and testing with an accuracy of 0.92 and 0.85, respectively. Other applications of DL have been extrapolated to automated classification of RCT and biceps tendinosis (BT) by Key et al [ 56 ] who created a fixed-size patch-based (PB) model called ViVGG-19 based on the transfer learning model VGG-19. DL has also been applied to RC disorders in obstetrical brachial plexus palsy (OBPP) [ 46 ].…”

Section: Shouldermentioning

confidence: 99%

Artificial intelligence powered advancements in upper extremity joint MRI: A review

Chen,

Lim,

Lim

et al. 2024

Heliyon

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Section: Shouldermentioning

confidence: 99%

Artificial intelligence powered advancements in upper extremity joint MRI: A review

Chen,

Lim,

Lim

et al. 2024

Heliyon

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“…MRI is commonly used as the primary imaging study for RCT diagnosis for its ability to accurately assess shoulder muscle atrophy and fatty infiltration (FI). 14,27,35 Initial studies have applied AI's CNN architectures for detecting and classifying RCTs. Recently, a study utilized an ensemble algorithm with 4 parallel 3-dimensional (3D) ResNet50 CNN architectures.…”

Section: Diagnostic Usementioning

confidence: 99%

Artificial Intelligence and Machine Learning in Rotator Cuff Tears

Rodriguez,

Rust,

Hansen

et al. 2023

Sports Medicine and Arthroscopy Review

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Rotator cuff tears (RCTs) negatively impacts patient well-being. Artificial intelligence (AI) is emerging as a promising tool in medical decision-making. Within AI, deep learning allows to autonomously solve complex tasks. This review assesses the current and potential applications of AI in the management of RCT, focusing on diagnostic utility, challenges, and future perspectives. AI demonstrates promise in RCT diagnosis, aiding clinicians in interpreting complex imaging data. Deep learning frameworks, particularly convoluted neural networks architectures, exhibit remarkable diagnostic accuracy in detecting RCTs on magnetic resonance imaging. Advanced segmentation algorithms improve anatomic visualization and surgical planning. AI-assisted radiograph interpretation proves effective in ruling out full-thickness tears. Machine learning models predict RCT diagnosis and postoperative outcomes, enhancing personalized patient care. Challenges include small data sets and classification complexities, especially for partial thickness tears. Current applications of AI in RCT management are promising yet experimental. The potential of AI to revolutionize personalized, efficient, and accurate care for RCT patients is evident. The integration of AI with clinical expertise holds potential to redefine treatment strategies and optimize patient outcomes. Further research, larger data sets, and collaborative efforts are essential to unlock the transformative impact of AI in orthopedic surgery and RCT management.

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“…These techniques have demonstrated exceptional performance in medical image analysis. [7][8][9][10][11][12][13][14][15][16] Generally, deep learning-based approaches for BAA involve initial extraction of TW3-ROIs from radiographs, followed by bone age evaluation through training classification models. However, the clinical adoption of relevant algorithms is hindered by significant morphological differences in TW3-ROIs during various stages of skeletal development and the overlapping interbone occlusions.…”

Section: Introductionmentioning

confidence: 99%

“…In recent years, there has been a remarkable advancement and application of deep learning techniques based on artificial neural networks. These techniques have demonstrated exceptional performance in medical image analysis 7–16 . Generally, deep learning‐based approaches for BAA involve initial extraction of TW3‐ROIs from radiographs, followed by bone age evaluation through training classification models.…”

Section: Introductionmentioning

confidence: 99%

SVTNet: Automatic bone age assessment network based onTW3method and vision transformer

Wu,

Mi,

Zhang

et al. 2023

Int J Imaging Syst Tech

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This study aims to develop a proficient and clinically applicable algorithm that can accurately assess bone age. This algorithm is based on the principles of the Tanner‐Whitehouse 3 (TW3) integral approach, and aims to achieve efficiency, scalability, and interpretability. We developed a model for bone age prediction in children. The model was tested on a pediatric dataset from a tertiary care hospital consisting of left‐hand radiographs of children between the age of 0 and 18. Our model consists of removing the arm portion using a pre‐trained YOLO network, localizing 37 key points in the hand bone portion using a spatial configuration network, and segmenting the original image through 20 of these points to obtain 20 fixed‐size patches. Finally, each of the 20 bone images is classified by training a visual transformer (ViT) model. In this study, a hybrid network, SVTNet, was developed that incorporates visual transformers to obtain estimates of bone age in the carpal (C series) and metacarpal (RUS series) bones. The sum of the clinical TW3 scoring region scores and bone maturity scores were utilized to determine the bone age for each corresponding region. The performance of the algorithm was evaluated in terms of both training and testing by evaluating 3871 left hand X‐ray micrographs obtained from a tertiary hospital in China. The results showed that the average absolute error of bone age estimation was 0.50 years for the RUS series of bones and 0.47 years for the C series of bones. The main contribution of this study is to propose, for the first time, a ViT‐based bone age assessment method that automates the entire process of the TW3 algorithm and is clinically interpretable, with predictive accuracy comparable to that of an experienced orthopedic surgeon.

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ViVGG19: Novel exemplar deep feature extraction-based shoulder rotator cuff tear and biceps tendinosis detection using magnetic resonance images

Cited by 10 publications

References 20 publications

Artificial intelligence powered advancements in upper extremity joint MRI: A review

Artificial intelligence powered advancements in upper extremity joint MRI: A review

Artificial Intelligence and Machine Learning in Rotator Cuff Tears

SVTNet: Automatic bone age assessment network based onTW3method and vision transformer

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