Reddy Dalsgaard (pikehandle29)

64; 95% CI, 0.49-0.85; p < 0.001). Women with a weight increase of more than 2 kg in a 4-week gestation period had a higher probability of having a low birth weight or premature baby than those with an increment of <1 kg (OR = 8.43; 95% CI, 2.90-24.54; p < 0.001). An increase in weight gain after 32 weeks was shown to reduce the risk of low birth weight and premature babies. Maternal weight monitoring was suggested to be conducted every 4 weeks to minimize the chance of having a low birth weight and premature baby. An increase in weight gain after 32 weeks was shown to reduce the risk of low birth weight and premature babies. Maternal weight monitoring was suggested to be conducted every 4 weeks to minimize the chance of having a low birth weight and premature baby. To date, plastic surgeons do not have an objective method of measuring facial symmetry for zygomatic bone fracture management. Based on clinical practice, the authors utilized a 3-dimensional (3D) model to propose the symmetry index from the anterior view (SIAV) and the symmetry index from inferior view (SIIV). This study aimed to assess the application of these 2 indices. The SIAV is defined as the distance between the superior and lower orbital rims (DSLOR) of the defective side divided by that of the healthy side in the anterior view. The SIIV is defined as the area within the region of interest (AROI) of the defective side divided by that of the healthy side in the inferior view. We retrospectively reviewed 95 patients who underwent zygomatic fracture surgery at our medical center from January 2017 to September 2020. The Patients who had bilateral zygomatic fractures and did not have both pre- and postoperative computed tomography (CT) images were excluded. Five out of the 95 patients were enrolled in this study. The difference between pre- and postoperative mean AROI and DSLOR on the healthy side was not significant. The insignificant difference indicates the repeatability of the measurement of the 3D skull model and different CT machines would not affect the calculation of AROI and DSLOR. The mean values of postoperative SIAV (1.06 ± 0.07) and SIIV (1.02 ± 0.08) were closer to 1 than the preoperative values (0.97 ± 0.09 and 1.10 ± 0.12). Although the difference was not statistically significant, the SIIV and SIAV would numerically present the changes in malar bone fracture postoperatively. The SIAV and SIIV based on clinical practice could numerically assess the symmetry of the malar mound. The SIAV and SIIV based on clinical practice could numerically assess the symmetry of the malar mound. In clinical applications, mucosal healing is a therapeutic goal in patients with ulcerative colitis (UC). Endoscopic remission is associated with lower rates of colectomy, relapse, hospitalization, and colorectal cancer. Differentiation of mucosal inflammatory status depends on the experience and subjective judgments of clinical physicians. We developed a computer-aided diagnostic system using deep learning and machine learning (DLML-CAD) to accurately diagnose mucosal healing in UC patients. We selected 856 endoscopic colon images from 54 UC patients (643 images with endoscopic score 0-1 and 213 with score 2-3) from the endoscopic image database at Tri-Service General Hospital, Taiwan. Endoscopic grading using the Mayo endoscopic subscore (MES 0-3) was performed by two reviewers. A pretrained neural network extracted image features, which were used to train three different classifiers-deep neural network (DNN), support vector machine (SVM), and k-nearest neighbor (k-NN) network. DNN classified MES 0 to 1, representing mucosal healing, vs MES 2 to 3 images with 93.8% accuracy (sensitivity 84.6%, specificity 96.9%); SVM had 94.1% accuracy (sensitivity 89.2%, specificity 95.8%); and k-NN had 93.4% accuracy (sensitivity 86.2%, specificity 95.8%). Combined, ensemble learni