ArticleJournal of biomedical optics2013
Computer-aided diagnosis of rheumatoid arthritis with optical tomography, Part 1: feature extraction.
Article in Journal of biomedical optics, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- Machine-learning-enhanced image reconstruction in optical tomography using the Monte Carlo method for light transport.Journal of biomedical optics · 2026Article
- A Dual-Mode Near-Infrared Optical Probe and Monte Carlo Framework for Functional Monitoring of Rheumatoid Arthritis: Addressing Diagnostic Ambiguity and Skin Tone Robustness.Sensors (Basel, Switzerland) · 2026Article
- Evaluating hemodynamic response to treatment in patients with peripheral arterial disease using dynamic vascular optical spectroscopy.Journal of biomedical optics · 2024Article
- Stochastic Gauss-Newton method for estimating absorption and scattering in optical tomography with the Monte Carlo method for light transport.Biomedical optics express · 2024Article
- Flexible optical imaging band system for the assessment of arthritis in patients with systemic lupus erythematosus.Biomedical optics express · 2021Article
- Practical Implementation of Artificial Intelligence-Based Deep Learning and Cloud Computing on the Application of Traditional Medicine and Western Medicine in the Diagnosis and Treatment of Rheumatoid Arthritis.Frontiers in pharmacology · 2021Review
- Computer-aided diagnosis in rheumatic diseases using ultrasound: an overview.Clinical rheumatology · 2020Review
- Assessing spectral imaging of the human finger for detection of arthritis.Biomedical optics express · 2019Article
- Detecting inflammation in rheumatoid arthritis using Fourier transform analysis of dorsal optical transmission images from a pilot study.Journal of biomedical optics · 2019Observational
- Multispectral, non-contact diffuse optical tomography of healthy human finger joints.Biomedical optics express · 2018Article
- Frequency-domain optical tomographic image reconstruction algorithm with the simplified spherical harmonics (SPInternational journal of thermal sciences = Revue generale de thermique · 2017Article
- Joint blood flow is more sensitive to inflammatory arthritis than oxyhemoglobin, deoxyhemoglobin, and oxygen saturation.Biomedical optics express · 2016Article
- In Vivo, Non-Invasive Characterization of Human Bone by Hybrid Broadband (600-1200 nm) Diffuse Optical and Correlation Spectroscopies.PloS one · 2016Article
- Fast linear solver for radiative transport equation with multiple right hand sides in diffuse optical tomography.Journal of quantitative spectroscopy & radiative transfer · 2015Article
- Computer-aided diagnosis of rheumatoid arthritis with optical tomography, Part 2: image classification.Journal of biomedical optics · 2013Article
- Scoping review: Machine learning interventions in the management of healthcare systems.Digital healthArticle
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Authors and funding
7 authors.
Funding
Abstract
This is the first part of a two-part paper on the application of computer-aided diagnosis to diffuse optical tomography (DOT). An approach for extracting heuristic features from DOT images and a method for using these features to diagnose rheumatoid arthritis (RA) are presented. Feature extraction is the focus of Part 1, while the utility of five classification algorithms is evaluated in Part 2. The framework is validated on a set of 219 DOT images of proximal interphalangeal (PIP) joints. Overall, 594 features are extracted from the absorption and scattering images of each joint. Three major findings are deduced. First, DOT images of subjects with RA are statistically different (p<0.05) from images of subjects without RA for over 90% of the features investigated. Second, DOT images of subjects with RA that do not have detectable effusion, erosion, or synovitis (as determined by MRI and ultrasound) are statistically indistinguishable from DOT images of subjects with RA that do exhibit effusion, erosion, or synovitis. Thus, this subset of subjects may be diagnosed with RA from DOT images while they would go undetected by reviews of MRI or ultrasound images. Third, scattering coefficient images yield better one-dimensional classifiers. A total of three features yield a Youden index greater than 0.8. These findings suggest that DOT may be capable of distinguishing between PIP joints that are healthy and those affected by RA with or without effusion, erosion, or synovitis.
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