RESULTS: We demonstrate a method using a web-based tool to construct a deep learning model and later export the model for deployment. We train the model by using breeding substrate images with different spectra of illumination on known densities of larvae and evaluate the training model in both the test set and field-collected samples. In general, the model was able to predict the larval abundance by the laboratory-prepared breeding substrate with 87.56% to 94.10% accuracy, precision, recall, and F-score on the unseen test set, and white and green illumination performed significantly higher compared to other illuminations. For field samples, the model was able to obtain at least 70% correct predictions by using white and infrared illumination.
CONCLUSION: Larval abundance can be monitored with computer vision and deep learning, and the monitoring can be improved by using more biochemistry parameters as the predictors and examples of field samples included building a more robust model. © 2021 Society of Chemical Industry.
METHODS: We identified children ≤ 12 years old hospitalized for COVID-19 across five hospitals in Negeri Sembilan, Malaysia, from 1 January 2021 to 31 December 2021 from the state's pediatric COVID-19 case registration system. The primary outcome was the development of moderate/severe COVID-19 during hospitalization. Multivariate logistic regression was performed to identify independent risk factors for moderate/severe COVID-19. A nomogram was constructed to predict moderate/severe disease. The model performance was evaluated using the area under the curve (AUC), sensitivity, specificity, and accuracy.
RESULTS: A total of 1,717 patients were included. After excluding the asymptomatic cases, 1,234 patients (1,023 mild cases and 211 moderate/severe cases) were used to develop the prediction model. Nine independent risk factors were identified, including the presence of at least one comorbidity, shortness of breath, vomiting, diarrhea, rash, seizures, temperature on arrival, chest recessions, and abnormal breath sounds. The nomogram's sensitivity, specificity, accuracy, and AUC for predicting moderate/severe COVID-19 were 58·1%, 80·5%, 76·8%, and 0·86 (95% CI, 0·79 - 0·92) respectively.
CONCLUSION: Our nomogram, which incorporated readily available clinical parameters, would be useful to facilitate individualized clinical decisions.
METHODS: A retrospective, multicentre, observational study was performed among children ≤15 years old who were hospitalized for MIS-C between January 18, 2021 and June 30, 2023. The incidence of MIS-C was estimated using reported SARS-CoV-2 cases and census population data. Descriptive analyses were used to summarize the clinical presentation and outcomes.
RESULTS: The study included 53 patients with a median age of 5.7 years (IQR 1.8-8.7 years); 75.5% were males. The overall incidence of MIS-C was approximately 5.9 cases per 1,000,000 person-months. Pediatric intensive care unit (PICU) admission was required for 22 (41.5%) patients. No mortalities were recorded. Children aged 6-12 years were more likely to present with cardiac dysfunction/shock (odds ratio [OR] 5.43, 95% confidence interval [CI] 1.67-17.66), whereas children below 6 years were more likely to present with a Kawasaki disease phenotype (OR 5.50, 95% CI 1.33-22.75). Twenty patients (37.7%) presented with involvement of at least four organ systems, but four patients (7.5%) demonstrated single-organ system involvement.
CONCLUSION: An age-based variation in the clinical presentation of MIS-C was demonstrated. Our findings suggest MIS-C could manifest in a spectrum, including single-organ involvement. Despite the high requirement for PICU admission, the prognosis of MIS-C was favorable, with no recorded mortalities.