Musculoskeletal radiographs classification using deep learning

Harini, N.; Ramji, B.; Sriram, Sriniwas; Sowmya, V.; Soman, K. P.

doi:10.1016/b978-0-12-819764-6.00006-5

Cited by 16 publications

(5 citation statements)

References 23 publications

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“…While a direct comparison with those results is not possible, based on the performance of the trained DenseNet 169 model on the validation set we can make an educated guess that the three models with the highest performance will slightly surpass those results, perhaps scoring a Cohen's kappa of 0.72 on the test set. While the CNNs trained here have a better performance compared to those trained in similar studies [27][28][29][30][31][32][33], they are still slightly behind expert human radiologists.…”

Section: Resultsmentioning

confidence: 85%

Automating Abnormality Detection in Musculoskeletal Radiographs through Deep Learning

Mehr¹

2020

Preprint

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This paper introduces MuRAD (Musculoskeletal Radiograph Abnormality Detection tool), a tool that can help radiologists automate the detection of abnormalities in musculoskeletal radiographs (bone X-rays). MuRAD utilizes a Convolutional Neural Network (CNN) that can accurately predict whether a bone X-ray is abnormal, and leverages Class Activation Map (CAM) to localize the abnormality in the image. MuRAD achieves an F1 score of 0.822 and a Cohen's kappa of 0.699, which is comparable to the performance of expert radiologists.

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Section: Resultsmentioning

confidence: 85%

Automating Abnormality Detection in Musculoskeletal Radiographs through Deep Learning

Mehr¹

2020

Preprint

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“…The MURA dataset is comprised of 40,000 multi-view musculoskeletal radiographs that were collected from 12,000 patient. The figure 1 includes upper limb parts(elbow, finger, forearm, hand, humerus, shoulder, and wrist) [8]. The dataset was collected from Stanford Hospital's Picture Archive and Communication System(PACS) between the years 2001 and 2012.…”

Section: Datasetmentioning

confidence: 99%

Enhanced Diagnostic Accuracy in Musculoskeletal Radiography: A Comprehensive Ensemble Approach of Deep Learning Models

BS,

Buchupalli,

Gottimukkula

et al. 2024

Preprint

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The musculoskeletal radiograph (MURA) dataset, which is the largest bone x-ray dataset is an important resource for training and testing the machine learning model, as it has been specifically analyzed and well-classified by specially trained radiologists. Four CNN architectures have separately trained and evaluated the dataset ( DenseNet201 GoogLeNet, ResNet50, and Inception-ResNet-V2). The results of the algorithms showed variation in recall and accuracy. In addition, multiple-architecture ensemble models performed better than single models, highlighting the benefits of ensembling techniques to improve prediction accuracy. Combining multiple deep learning models reduces the risk of overfitting, increases predictive stability, improves accuracy, and optimizes the dataset. The integration of several models considers dataset robustness and uncertainty reduction. This ensembling process enables the development of accurate diagnostic tools for upper limb bone-related problems using the MURA dataset and other radiographic datasets. This work shows the use of ensemble methodologies, optimization of architecture, and various techniques, such as using data augmentation and demonstrating the ability of CNN to generalize musculoskeletal diseases. The results of this work can contribute to the creation of more effective diagnostic tools for the healthcare industry.

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“…The application of artificial intelligence (AI) and machine learning (ML) models in rehabilitation has become increasingly important for different purposes, such as classification, prediction, and the development of personalized treatment plans, as well as the enhancement of diagnostic accuracy [44][45][46][47][48]. These not only enhance treatment effectiveness but also facilitate more efficient and cost-effective care.…”

Section: Artificial Intelligencementioning

confidence: 99%

Advanced Home-Based Shoulder Rehabilitation: A Systematic Review of Remote Monitoring Devices and Their Therapeutic Efficacy

Sassi,

Villa Corta,

Pisani

et al. 2024

Sensors

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Shoulder pain represents the most frequently reported musculoskeletal disorder, often leading to significant functional impairment and pain, impacting quality of life. Home-based rehabilitation programs offer a more accessible and convenient solution for an effective shoulder disorder treatment, addressing logistical and financial constraints associated with traditional physiotherapy. The aim of this systematic review is to report the monitoring devices currently proposed and tested for shoulder rehabilitation in home settings. The research question was formulated using the PICO approach, and the PRISMA guidelines were applied to ensure a transparent methodology for the systematic review process. A comprehensive search of PubMed and Scopus was conducted, and the results were included from 2014 up to 2023. Three different tools (i.e., the Rob 2 version of the Cochrane risk-of-bias tool, the Joanna Briggs Institute (JBI) Critical Appraisal tool, and the ROBINS-I tool) were used to assess the risk of bias. Fifteen studies were included as they fulfilled the inclusion criteria. The results showed that wearable systems represent a promising solution as remote monitoring technologies, offering quantitative and clinically meaningful insights into the progress of individuals within a rehabilitation pathway. Recent trends indicate a growing use of low-cost, non-intrusive visual tracking devices, such as camera-based monitoring systems, within the domain of tele-rehabilitation. The integration of home-based monitoring devices alongside traditional rehabilitation methods is acquiring significant attention, offering broader access to high-quality care, and potentially reducing healthcare costs associated with in-person therapy.

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Musculoskeletal radiographs classification using deep learning

Cited by 16 publications

References 23 publications

Automating Abnormality Detection in Musculoskeletal Radiographs through Deep Learning

Automating Abnormality Detection in Musculoskeletal Radiographs through Deep Learning

Enhanced Diagnostic Accuracy in Musculoskeletal Radiography: A Comprehensive Ensemble Approach of Deep Learning Models

Advanced Home-Based Shoulder Rehabilitation: A Systematic Review of Remote Monitoring Devices and Their Therapeutic Efficacy

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