Digital image analysis protocol for determining the radiocarpal joint space in the rheumatoid arthritic wrist

Dual-energy x-ray approach for object/energy-specific attenuation coefficient correction in single-photon emission computed tomography: effects of contrast agent

Purpose: To investigate the influence of radiographic contrast agent on the accuracy of the photon counts arising from the emission of gamma rays of radionuclides in single-photon emission computed tomography (SPECT), when dual-energy x-ray CT (DXCT) is employed for providing object/energy-specific attenuation coefficient correction in SPECT. Approach: Computer simulation was performed for three transmission CT approaches, namely, the conventional (single kVp, unimodal spectrum) x-ray CT, DXCT (single kVp, bimodal spectrum) with basis material decomposition (BMD), and DXCT with BMD followed by basis material coefficients transformation (BMT), to study the effects of these approaches on the accuracy of the photon counts from the SPECT image of a thorax-like phantom. Results: All three CT approaches revealed that the error in the counts was both photon energy and iodine concentration-dependent. Differences in the trending increase/decrease in the errors with the respective increase in iodine concentration and photon energy were observed among the three CT approaches. Of the three, the BMT/SPECT approach resulted in the smallest error in the concentration of radionuclides measured, especially in the contrast agent-filled region, and the optimal level depended on the iodine concentration and photon energy. Conclusion: With a judicious choice of the basis materials and photon energy, it may be possible to take advantage of the benefits of the BMT method to mitigate the accuracy problem in DXCT for quantitative SPECT imaging.

Computer-aided diagnosis based on hand thermal, RGB images, and grip force using artificial intelligence as screening tool for rheumatoid arthritis in women

Rheumatoid arthritis (RA) is an autoimmune disorder that typically affects people between 23 and 60 years old causing chronic synovial inflammation, symmetrical polyarthritis, destruction of large and small joints, and chronic disability. Clinical diagnosis of RA is stablished by current ACR-EULAR criteria, and it is crucial for starting conventional therapy in order to minimize damage progression. The 2010 ACR-EULAR criteria include the presence of swollen joints, elevated levels of rheumatoid factor or anti-citrullinated protein antibodies (ACPA), elevated acute phase reactant, and duration of symptoms. In this paper, a computer-aided system for helping in the RA diagnosis, based on quantitative and easy-to-acquire variables, is presented. The participants in this study were all female, grouped into two classes: class I, patients diagnosed with RA (n = 100), and class II corresponding to controls without RA (n = 100). The novel approach is constituted by the acquisition of thermal and RGB images, recording their hand grip strength or gripping force. The weight, height, and age were also obtained from all participants. The color layout descriptors (CLD) were obtained from each image for having a compact representation. After, a wrapper forward selection method in a range of classification algorithms included in WEKA was performed. In the feature selection process, variables such as hand images, grip force, and age were found relevant, whereas weight and height did not provide important information to the classification. Our system obtains an AUC ROC curve greater than 0.94 for both thermal and RGB images using the RandomForest classifier. Thirty-eight subjects were considered for an external test in order to evaluate and validate the model implementation. In this test, an accuracy of 94.7% was obtained using RGB images; the confusion matrix revealed our system provides a correct diagnosis for all participants and failed in only two of them (5.3%). Graphical abstract.