METHODS: Two blinded reviewers searched PubMed, Embase, Scopus, Web of Science, and IEEE Xplore databases, then selected and graded the risk of bias of observational studies of adults (≥ 18 years) comparing the diagnostic performance of AI algorithms using craniofacial photographs, versus conventional OSA diagnostic criteria (i.e. apnea-hypopnea index [AHI]). Studies were excluded if they detected apneic events without diagnosing OSA. AI models evaluated with a random split test set or k-fold cross-validation were included in a Bayesian bivariate meta-analysis.

RESULTS: From 5,147 records, 6 studies were included, containing 10 AI models trained/tested on 1,417/983 participants. The risk of bias was low. AI trained on craniofacial photographs achieved a pooled 84.9% sensitivity (95% credible interval [95% CrI]: 77.1-90.7%) and 71.2% specificity (95% CrI: 60.7-81.4%). Bayesian meta-regression identified deep learning (convolutional neural networks) as the most accurate AI algorithm (91.1% sensitivity, 79.2% specificity) comparable to home sleep apnea tests. AHI cutoffs, OSA prevalence, feature engineering, input data, camera type and informativeness of Bayesian prior did not alter diagnostic accuracy. There was no substantial publication bias.

DATA SOURCES: An updated systematic search was performed in three databases until September 4, 2024. The study adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses 2020 guidelines and the protocol was preregistered in PROSPERO (CRD42024546387).

STUDY SELECTION: Randomized controlled trials that studied adult critically ill patients comparing protein doses delivered enterally and/or parenterally with similar energy delivery between groups were included.

DATA EXTRACTION: Data extraction was performed by two authors independently, using a predefined worksheet. The primary outcome was mortality. Posterior probabilities of any benefit (relative risk [RR] < 1.00) or harm (RR > 1.00) and other important beneficial and harmful effect size thresholds were estimated. Risk of bias assessment was performed using the risk of bias 2.0 tool. All analyses were performed using a Bayesian hierarchical random-effects models, under vague priors.

DATA SYNTHESIS: Twenty-two randomized trials ( n = 4164 patients) were included. The mean protein delivery in the higher and lower protein groups was 1.5 ± 0.6 vs. 0.9 ± 0.4 g/kg/d. The median RR for mortality was 1.01 (95% credible interval, 0.84-1.16). The posterior probability of any mortality benefit from higher protein delivery was 43.6%, while the probability of any harm was 56.4%. The probabilities of a 1% (RR < 0.99) and 5% (RR < 0.95) mortality reduction by higher protein delivery were 38.7% and 22.9%, respectively. Conversely, the probabilities of a 1% (RR > 1.01) and 5% (RR > 1.05) mortality increase were 51.5% and 32.4%, respectively.

METHODS: The most important climatic factors that contribute to dengue outbreaks were identified in the current work. Correlation analyses were performed in order to determine these factors and these factors were used as input parameters for machine learning models. Top five machine learning classification models (Bayes network (BN) models, support vector machine (SVM), RBF tree, decision table and naive Bayes) were chosen based on past research. The models were then tested and evaluated on the basis of 4-year data (January 2010 to December 2013) collected in Malaysia.

RESULTS: This research has two major contributions. A new risk factor, called the TempeRain factor (TRF), was identified and used as an input parameter for the model of dengue outbreak prediction. Moreover, TRF was applied to demonstrate its strong impact on dengue outbreaks. Experimental results showed that the Bayes Network model with the new meteorological risk factor identified in this study increased accuracy to 92.35% for predicting dengue outbreaks.