Objectives: Telemedicine is firmly established in the healthcare landscape of many countries. Acute respiratory infections are the most common reason for telemedicine consultations. A throat examination is important for diagnosing bacterial pharyngitis, but this is challenging for doctors during a telemedicine consultation. A solution could be for patients to upload images of their throat to a web application. This study aimed to develop a deep learning model for the automated diagnosis of exudative pharyngitis. Thereafter, the model will be deployed online. Methods: We used 343 throat images (139 with exudative pharyngitis and 204 without pharyngitis) in the study. ImageDataGenerator was used to augment the training data. The convolutional neural network models of MobileNetV3, ResNet50, and EfficientNetB0 were implemented to train the dataset, with hyperparameter tuning. Results: All three models were trained successfully; with successive epochs, the loss and training loss decreased, and accuracy and training accuracy increased. The EfficientNetB0 model achieved the highest accuracy (95.5%), compared to MobileNetV3 (82.1%) and ResNet50 (88.1%). The EfficientNetB0 model also achieved high precision (1.00), recall (0.89) and F1-score (0.94). Conclusions We trained a deep learning model based on EfficientNetB0 that can diagnose exudative pharyngitis. Our model was able to achieve the highest accuracy, at 95.5%, out of all previous studies that used machine learning for the diagnosis of exudative pharyngitis. We have deployed the model on a web application that can be used to augment the doctor’s diagnosis of exudative pharyngitis.

Objective: Bariatric surgery effectively treats non-alcoholic fatty liver disease (NAFLD). The glutamate-serine-glycine (GSG) index has emerged as a non-invasive diagnostic marker for NAFLD, but its ability to monitor treatment response remains unclear. This study investigates the GSG index's ability to monitor NAFLD's response to bariatric surgery. Methodology: Ten NAFLD participants were studied at baseline and 6 months post-bariatric surgery. Blood samples were collected for serum biomarkers and metabolomic profiling. Hepatic steatosis [proton density fat fraction (PDFF)] and fibroinflammation (cT1) were quantified with multiparametric magnetic resonance imaging (mpMRI), and hepatic stiffness with magnetic resonance elastography (MRE). Amino acids and acylcarnitines were measured with mass spectrometry. Statistical analyses included paired Student’s t-test, Wilcoxon-signed rank test, and Pearson’s correlation. Results: Eight participants provided complete data. At baseline, all had hepatic steatosis (BMI 39.3 ± 5.6 kg/m2, PDFF ≥ 5%). Post-surgery reductions in PDFF (from 12.4 ± 6.7% to 6.2 ± 2.8%, p = 0.013) and cT1 (from 823.3 ± 85.4ms to 757.5 ± 41.6ms, p = 0.039) were significant, along with the GSG index (from 0.272 ± 0.03 to 0.157 ± 0.05, p = 0.001). Conclusion The GSG index can potentially be developed as a marker for monitoring the response of patients with NAFLD to bariatric surgery.
Non-alcoholic Fatty Liver Disease ; Amino Acids ; Metabolomics