Radiographic Bone Texture Analysis Using Deep Learning Models for Early Rheumatoid Arthritis Diagnosis

Huang, Yun-Ju; Miao, Shun; Zheng, Kang; Lü, Le; Lu, Yuhang; Lin, Chi‐Hung; Kuo, Chang‐Fu

doi:10.21203/rs.3.rs-76193/v1

Cited by 6 publications

(4 citation statements)

References 25 publications

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“…The authors of [22] try to classify RA by using deep learning models to analyze texture changes in different stages of the disease. They use the Deep Texture Encoding Network (Deep-TEN) and residual network-50 (ResNet-50) in order to predict the probability of RA.…”

Section: Deep Learning Methodsmentioning

confidence: 99%

Rheumatoid Arthritis Diagnosis: Deep Learning vs. Humane

Avramidis

Avramidou²,

Papakostas

2021

Applied Sciences

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Rheumatoid arthritis (RA) is a systemic autoimmune disease that preferably affects small joints. As the well-timed diagnosis of the disease is essential for the treatment of the patient, several works have been conducted in the field of deep learning to develop fast and accurate automatic methods for RA diagnosis. These works mainly focus on medical images as they use X-ray and ultrasound images as input for their models. In this study, we review the conducted works and compare the methods that use deep learning with the procedure that is commonly followed by a medical doctor for the RA diagnosis. The results show that 93% of the works use only image modalities as input for the models as distinct from the medical procedure where more patient medical data are taken into account. Moreover, only 15% of the works use direct explainability methods, meaning that the efforts for solving the trustworthiness issue of deep learning models were limited. In this context, this work reveals the gap between the deep learning approaches and the medical doctors’ practices traditionally applied and brings to light the weaknesses of the current deep learning technology to be integrated into a trustworthy context inside the existed medical infrastructures.

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Section: Deep Learning Methodsmentioning

confidence: 99%

Rheumatoid Arthritis Diagnosis: Deep Learning vs. Humane

Avramidis

Avramidou²,

Papakostas

2021

Applied Sciences

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“…These methods were applied to various organs and inflammatory diseases, particularly rheumatoid arthritis (RA), which is the most common inflammatory and systemic connective tissue disease [183,184]. Some of the RA-related studies focus on hand images captured with different imaging modalities, such as infrared thermography sensor [185], thermal image [172], and digital anterior-posterior radiographs of hand images [186,187]. Some of these studies (e.g., [172,185]) use traditional image processing and machine learning algorithms such as thresholding, dilation, erosion, depth-first search (DFS), gray-level co-occurrence matrix (GLCM), and k-means.…”

Section: Image Analysis Of Inflammatory Diseasementioning

confidence: 99%

“…Some of these studies (e.g., [172,185]) use traditional image processing and machine learning algorithms such as thresholding, dilation, erosion, depth-first search (DFS), gray-level co-occurrence matrix (GLCM), and k-means. Other studies (e.g., [186,187]) use a CNNbased approach to segment and detect the RA regions. A review paper summarizes machine learning studies in rheumatic diseases [188].…”

Section: Image Analysis Of Inflammatory Diseasementioning

confidence: 99%

Molecular Imaging of Inflammatory Disease

et al. 2021

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Inflammatory diseases include a wide variety of highly prevalent conditions with high mortality rates in severe cases ranging from cardiovascular disease, to rheumatoid arthritis, to chronic obstructive pulmonary disease, to graft vs. host disease, to a number of gastrointestinal disorders. Many diseases that are not considered inflammatory per se are associated with varying levels of inflammation. Imaging of the immune system and inflammatory response is of interest as it can give insight into disease progression and severity. Clinical imaging technologies such as computed tomography (CT) and magnetic resonance imaging (MRI) are traditionally limited to the visualization of anatomical information; then, the presence or absence of an inflammatory state must be inferred from the structural abnormalities. Improvement in available contrast agents has made it possible to obtain functional information as well as anatomical. In vivo imaging of inflammation ultimately facilitates an improved accuracy of diagnostics and monitoring of patients to allow for better patient care. Highly specific molecular imaging of inflammatory biomarkers allows for earlier diagnosis to prevent irreversible damage. Advancements in imaging instruments, targeted tracers, and contrast agents represent a rapidly growing area of preclinical research with the hopes of quick translation to the clinic.

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“…Medical imaging studies on bone X-rays have traditionally been focused on fracture detection [53][54][55], disease diagnosis [56,57], or segmentation [57,58]. Little attention has been given to bone mineral density (BMD) estimation, which is important for diagnosing osteoporosis, a condition characterized by decreased levels of bone density.…”

Section: B Bone Mineral Density Estimation From Plain X-ray Filmsmentioning

confidence: 99%

Adaptive Contrast for Image Regression in Computer-Aided Disease Assessment

Dai¹,

Li²,

Chiu³

et al. 2021

Preprint

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Image regression tasks for medical applications, such as bone mineral density (BMD) estimation and leftventricular ejection fraction (LVEF) prediction, play an important role in computer-aided disease assessment. Most deep regression methods train the neural network with a single regression loss function like MSE or L1 loss. In this paper, we propose the first contrastive learning framework for deep image regression, namely AdaCon, which consists of a feature learning branch via a novel adaptive-margin contrastive loss and a regression prediction branch. Our method incorporates label distance relationships as part of the learned feature representations, which allows for better performance in downstream regression tasks. Moreover, it can be used as a plug-and-play module to improve performance of existing regression methods. We demonstrate the effectiveness of AdaCon on two medical image regression tasks, i.e., bone mineral density estimation from X-ray images and left-ventricular ejection fraction prediction from echocardiogram videos. AdaCon leads to relative improvements of 3.3% and 5.9% in MAE over state-of-the-art BMD estimation and LVEF prediction methods, respectively.

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Radiographic Bone Texture Analysis Using Deep Learning Models for Early Rheumatoid Arthritis Diagnosis

Cited by 6 publications

References 25 publications

Rheumatoid Arthritis Diagnosis: Deep Learning vs. Humane

Rheumatoid Arthritis Diagnosis: Deep Learning vs. Humane

Molecular Imaging of Inflammatory Disease

Adaptive Contrast for Image Regression in Computer-Aided Disease Assessment

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