ArticleJournal of biomedical optics2013
Computer-aided diagnosis of rheumatoid arthritis with optical tomography, Part 2: image classification.
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 14 papers.
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Who cites it
14 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
- 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
- The basics of data, big data, and machine learning in clinical practice.Clinical rheumatology · 2021Review
- Quantification of joint blood flow by dynamic contrast-enhanced near-infrared spectroscopy: application to monitoring disease activity in a rat model of rheumatoid arthritis.Journal of biomedical optics · 2020Article
- Article
- 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
- Development of a multi-wavelength diffuse optical tomography system for early diagnosis of rheumatoid arthritis: simulation, phantoms and healthy human studies.Biomedical optics express · 2016Article
- Joint blood flow is more sensitive to inflammatory arthritis than oxyhemoglobin, deoxyhemoglobin, and oxygen saturation.Biomedical optics express · 2016Article
- Computer-aided diagnosis of rheumatoid arthritis with optical tomography, Part 1: feature extraction.Journal of biomedical optics · 2013Article
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Authors and funding
7 authors.
Funding
Abstract
This is the second part of a two-part paper on the application of computer-aided diagnosis to diffuse optical tomography (DOT) for diagnosing rheumatoid arthritis (RA). A comprehensive analysis of techniques for the classification of DOT images of proximal interphalangeal joints of subjects with and without RA is presented. A method for extracting heuristic features from DOT images was presented in Part 1. The ability of five classification algorithms to accurately label each DOT image as belonging to a subject with or without RA is analyzed here. The algorithms of interest are the k-nearest-neighbors, linear and quadratic discriminant analysis, self-organizing maps, and support vector machines (SVM). With a polynomial SVM classifier, we achieve 100.0% sensitivity and 97.8% specificity. Lower bounds for these results (at 95.0% confidence level) are 96.4% and 93.8%, respectively. Image features most predictive of RA are from the spatial variation of optical properties and the absolute range in feature values. The optimal classifiers are low-dimensional combinations (<7 features). These results underscore the high potential for DOT to become a clinically useful diagnostic tool and warrant larger prospective clinical trials to conclusively demonstrate the ultimate clinical utility of this approach.
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