Prospectus, February 28, 2007

https://spark.parkland.edu/prospectus_2007/1006/thumbnail.jp

Prospectus, October 28, 2006

https://spark.parkland.edu/prospectus_2006/1025/thumbnail.jp

Prospectus, January 17, 2007

https://spark.parkland.edu/prospectus_2007/1000/thumbnail.jp

Prospectus, November 8, 2006

https://spark.parkland.edu/prospectus_2006/1027/thumbnail.jp

Development of pericardial fat count images using a combination of three different deep-learning models

Rationale and Objectives: Pericardial fat (PF), the thoracic visceral fat surrounding the heart, promotes the development of coronary artery disease by inducing inflammation of the coronary arteries. For evaluating PF, this study aimed to generate pericardial fat count images (PFCIs) from chest radiographs (CXRs) using a dedicated deep-learning model. Materials and Methods: The data of 269 consecutive patients who underwent coronary computed tomography (CT) were reviewed. Patients with metal implants, pleural effusion, history of thoracic surgery, or that of malignancy were excluded. Thus, the data of 191 patients were used. PFCIs were generated from the projection of three-dimensional CT images, where fat accumulation was represented by a high pixel value. Three different deep-learning models, including CycleGAN, were combined in the proposed method to generate PFCIs from CXRs. A single CycleGAN-based model was used to generate PFCIs from CXRs for comparison with the proposed method. To evaluate the image quality of the generated PFCIs, structural similarity index measure (SSIM), mean squared error (MSE), and mean absolute error (MAE) of (i) the PFCI generated using the proposed method and (ii) the PFCI generated using the single model were compared. Results: The mean SSIM, MSE, and MAE were as follows: 0.856, 0.0128, and 0.0357, respectively, for the proposed model; and 0.762, 0.0198, and 0.0504, respectively, for the single CycleGAN-based model. Conclusion: PFCIs generated from CXRs with the proposed model showed better performance than those with the single model. PFCI evaluation without CT may be possible with the proposed method

Prospectus, November 16, 2006

https://spark.parkland.edu/prospectus_2006/1028/thumbnail.jp

D-Penicillamine-Induced Polymyositis Occurring in Patients with Rheumatoid Arthritis: A Report of Two Cases and Demonstration of a Positive Lymphocyte Stimulation Test to D-Penicillamine

Two patients with erosive, seropositive rheumatoid arthritis developed polymyositis during treatment with D-penicillamine. In both patients the HLA tissue typing revealed the presence of DR4. The myopathy improved promptly after withdrawal of D-penicillamine and institution of prednisolone therapy. In one patient, hypersensitivity to D-penicillamine was demonstrated by a lymphocyte stimulation test. This is the first case of D-penicillamine-induced polymyositis in which T-cell proliferative response to D-penicillamine was demonstrated in vitro