Detecting inflammation in rheumatoid arthritis using Fourier transform analysis of dorsal optical transmission images from a pilot study

Lighter, Daniel; Filer, Andrew; Dehghani, Hamid

doi:10.1117/1.jbo.24.6.066008

Cited by 14 publications

(25 citation statements)

References 43 publications

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“…The three models simulated with the expected changes due to RA produced observable differences in spectral and spatial information. Most notable was the suppression of the spatial peak in transmission near the joint, consistent with the changes in spatial profiles observed in [15]. However, considering the experimentally observed inter-subject variability, a more sensitive indicator might be needed.…”

Section: Discussionsupporting

confidence: 67%

“…Optical techniques providing both spatial and spectral information are hyperspectral (HSI) [12] and multispectral imaging (MSI) [13,14]. In a pilot study involving 144 joints from 21 rheumatology patients, transmittance images were obtained at five wavelengths (650, 710, 730, 830, and 930 nm) [15]. Accurate detection of inflamed joints was demonstrated, based on Fourier transform analysis of optical transmission images.…”

Section: Introductionmentioning

confidence: 99%

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Assessing spectral imaging of the human finger for detection of arthritis

Dolenec¹,

Laistler²,

Milanič³

2019

Biomed. Opt. Express

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Rheumatoid arthritis causes changes in the optical properties of tissues in the joints, which could be detected using spectral imaging. This has the potential for development of low cost, non-contact method for early detection of the disease. In this work, hyperspectral imaging system was used to obtain 24 images of proximal interphalangeal joints of 12 healthy volunteers. A large inter-subject variability was observed, but still an increase in transmittance in the spectral range of 600 nm-950 nm could be associated to the joint in all images. The results of experiments were compared to detailed simulations of light propagation trough tissue. For the simulations, voxelized 3D models of unaffected and inflamed human joints with realistic tissue distributions were constructed from an in-vivo MRI scan of a healthy human finger. The simulated model of healthy finger successfully reproduced the experimental data, while the affected models indicated that the inflammation introduces detectable differences in the spectral and spatial features. The results were used to guide the design of a dedicated imaging system for detection of rheumatoid arthritis, that will be used in an upcoming clinical study.

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Section: Discussionsupporting

confidence: 67%

Section: Introductionmentioning

confidence: 99%

Assessing spectral imaging of the human finger for detection of arthritis

Dolenec¹,

Laistler²,

Milanič³

2019

Biomed. Opt. Express

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“…In the past, a few studies also reported using finger joint models to simulate light transmission through healthy and inflamed joints. Lighter et al [ 32 ] used a simple 2D model of a joint with only four different tissue types, where they attempted to simulate the inflammation as a non-physiologic, circular scattering in the joint cavity. Although simulations resulted in the transmitted light linear profiles roughly resembling their experimental findings, the profiles were featureless, not showing fine joint details.…”

Section: Discussionmentioning

confidence: 99%

Anatomically Accurate, High-Resolution Modeling of the Human Index Finger Using In Vivo Magnetic Resonance Imaging

et al. 2022

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Anatomically accurate models of a human finger can be useful in simulating various disorders. In order to have potential clinical value, such models need to include a large number of tissue types, identified by an experienced professional, and should be versatile enough to be readily tailored to specific pathologies. Magnetic resonance images were acquired at ultrahigh magnetic field (7 T) with a radio-frequency coil specially designed for finger imaging. Segmentation was carried out under the supervision of an experienced radiologist to accurately capture various tissue types (TTs). The final segmented model of the human index finger had a spatial resolution of 0.2 mm and included 6,809,600 voxels. In total, 15 TTs were identified: subcutis, Pacinian corpuscle, nerve, vein, artery, tendon, collateral ligament, volar plate, pulley A4, bone, cartilage, synovial cavity, joint capsule, epidermis and dermis. The model was applied to the conditions of arthritic joint, ruptured tendon and variations in the geometry of a finger. High-resolution magnetic resonance images along with careful segmentation proved useful in the construction of an anatomically accurate model of the human index finger. An example illustrating the utility of the model in biomedical applications is shown. As the model includes a number of tissue types, it may present a solid foundation for future simulations of various musculoskeletal disease processes in human joints.

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“…Moreover, the author of [36] used the acquisition of optical pictures using a CCD (charge-coupled device) image sensor from the side of the dorsal hand. In order to detect optical modifications, a non-tomographic, single-wavelength, CW transillumer has earlier been provided, in which a light source focused on the finger's side, using a CCD camera sensor to collect the images on the palmar side.…”

Section: Sensors For Detecting Ramentioning

confidence: 99%

Development of Rheumatoid Arthritis Classification from Electronic Image Sensor Using Ensemble Method

Sharon

Elamvazuthi

et al. 2019

Sensors

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Rheumatoid arthritis (RA) is an autoimmune illness that impacts the musculoskeletal system by causing chronic, inflammatory, and systemic effects. The disease often becomes progressive and reduces physical function, causes suffering, fatigue, and articular damage. Over a long period of time, RA causes harm to the bone and cartilage of the joints, weakens the joints’ muscles and tendons, eventually causing joint destruction. Sensors such as accelerometer, wearable sensors, and thermal infrared camera sensor are widely used to gather data for RA. In this paper, the classification of medical disorders based on RA and orthopaedics datasets using Ensemble methods are discussed. The RA dataset was gathered from the analysis of white blood cell classification using features extracted from the image of lymphocytes acquired from a digital microscope with an electronic image sensor. The orthopaedic dataset is a benchmark dataset for this study, as it posed a similar classification problem with several numerical features. Three ensemble algorithms such as bagging, Adaboost, and random subspace were used in the study. These ensemble classifiers use k-NN (K-nearest neighbours) and Random forest (RF) as the base learners of the ensemble classifiers. The data classification is accessed using holdout and 10-fold cross-validation evaluation methods. The assessment was based on set of performance measures such as precision, recall, F-measure, and receiver operating characteristic (ROC) curve. The performance was also measured based on the comparison of the overall classification accuracy rate between different ensembles classifiers and the base learners. Overall, it was found that for Dataset 1, random subspace classifier with k-NN shows the best results in terms of overall accuracy rate of 97.50% and for Dataset 2, bagging-RF shows the highest overall accuracy rate of 94.84% over different ensemble classifiers. The findings indicate that the efficiency of the base classifiers with ensemble classifier have substantially improved.

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Detecting inflammation in rheumatoid arthritis using Fourier transform analysis of dorsal optical transmission images from a pilot study

Cited by 14 publications

References 43 publications

Assessing spectral imaging of the human finger for detection of arthritis

Assessing spectral imaging of the human finger for detection of arthritis

Anatomically Accurate, High-Resolution Modeling of the Human Index Finger Using In Vivo Magnetic Resonance Imaging

Development of Rheumatoid Arthritis Classification from Electronic Image Sensor Using Ensemble Method

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