Aim To evaluate subclinical left ventricular (LV) regional dysfunction in patients with primary Sjögren's syndrome (pSS) using feature tracking cardiac magnetic resonance (FT‐CMR) imaging and to identify pSS characteristics independently associated with LV regional dysfunction. Method Fifty patients with pSS and 20 controls without cardiovascular disease underwent non‐contrast CMR imaging. Labial gland biopsy was performed in 42 patients (84%). Disease activity was assessed using the European League Against Rheumatism Sjögren's syndrome disease activity index (ESSDAI). LV global longitudinal strain (GLS), global circumferential strain (GCS), and global radial strain (GRS) were measured using FT‐CMR. Results No significant differences in cardiovascular risk factors were found between the pSS group and controls. The pSS group had significantly lower GLS (P = .015) and GCS (P = .008) than the control group. Multiple linear regression analysis indicated that GCS was significantly associated with Raynaud's phenomenon (P = .015), focus score ≥2 (P = .032), and total ESSDAI score ≥8 (P = .029). Conclusion FT‐CMR can reveal subclinical LV regional dysfunction in patients with pSS without cardiovascular disease. Furthermore, patients with pSS and Raynaud's phenomenon, a focus score ≥2, or an ESSDAI score ≥8 were considered to be at high risk for myocardial dysfunction.
Recently, numerous attempts have been devoted to applying deep-learning-based super resolution to medical images. However, discussions on its usefulness have been limited to the use of indices such as peak signalto-noise ratio (PSNR) and structural similarity (SSIM), and the significance of its application has not been widely discussed. This study aimed to compare several deep learning (DL) -based super-resolution methods using publicly available brain magnetic resonance imaging datasets. The impact of training the segmentation models on super-resolved images was also investigated. The results demonstrated the superiority of the DL-based model over traditional image interpolation methods and the limitations of its application in medical imaging. Additionally, the results indicated that PSNR and SSIM might not always be suitable as evaluation indices.
Introduction: There is a difficulty to predict the success rate in endoscopic combined intrarenal surgery (ECIRS). This study aimed to create a prediction model for successful ECIRS using machine learning from medical record information and diagnostic imaging data, and verify its area under the curve (AUC). Methods: Patients who underwent ECIRS for urinary tract stones at Meirikai Tokyo Yamato Hospital were recruited. Patients were excluded if the surgical position was other than the modified Valdivia position, if the patient had been treated multiple times, or if the urinary tract stone had never been evaluated by computed tomography. Collected data included clinical information, blood biochemical findings, urinary findings, and imaging findings of the patients. To assess the performance of our model, we used 10-fold cross-validation where 90% of the data were used for training and 10% for validation. All possible input variables were used to train the model and validate its AUC and accuracy. Results: A total of 441 patients who underwent ECIRS were included. Logistic regression, which had the highest AUC, was used as the machine-learning model. The learning accuracy was adjusted by calculating the importance of the features and selecting 18 items. LDH, stone shape, TP, age, 3rd largest stone exist distal ureter, double renal pelvis or ureter, height, 1st largest stone exists mid-ureter, length diameter of the 1st largest stone and the 2nd largest stone, WBC count of 30-49/HPF and ≥ 100/HPF in urine sediment, MCHC, horseshoe kidney, presence of stones or the number of stones in the renal calyx, and short diameter of the 2nd largest stone were predictors of high success in ECIRS. AUC, accuracy, sensitivity, and specificity were 0.71, 71.9, 77.7, and 54.5, respectively. Conclusion:The machine-learning model obtained using medical record information accurately predicted the success of the ECIRS.
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