Exploring the effectiveness of artificial intelligence, machine learning and deep learning in trauma triage: A systematic review and meta-analysis

Retinal imaging and Alzheimer's disease: a future powered by Artificial Intelligence

Alzheimer's disease (AD) is a neurodegenerative condition that primarily affects brain tissue. Because the retina and brain share the same embryonic origin, visual deficits have been reported in AD patients. Artificial Intelligence (AI) has recently received a lot of attention due to its immense power to process and detect image hallmarks and make clinical decisions (like diagnosis) based on images. Since retinal changes have been reported in AD patients, AI is being proposed to process images to predict, diagnose, and prognosis AD. As a result, the purpose of this review was to discuss the use of AI trained on retinal images of AD patients. According to previous research, AD patients experience retinal thickness and retinal vessel density changes, which can occasionally occur before the onset of the disease's clinical symptoms. AI and machine vision can detect and use these changes in the domains of disease prediction, diagnosis, and prognosis. As a result, not only have unique algorithms been developed for this condition, but also databases such as the Retinal OCTA Segmentation dataset (ROSE) have been constructed for this purpose. The achievement of high accuracy, sensitivity, and specificity in the classification of retinal images between AD and healthy groups is one of the major breakthroughs in using AI based on retinal images for AD. It is fascinating that researchers could pinpoint individuals with a positive family history of AD based on the properties of their eyes. In conclusion, the growing application of AI in medicine promises its future position in processing different aspects of patients with AD, but we need cohort studies to determine whether it can help to follow up with healthy persons at risk of AD for a quicker diagnosis or assess the prognosis of patients with AD.

Artificial Intelligence in the Early Prediction of Cardiogenic Shock in Acute Heart Failure or Myocardial Infarction Patients: A Systematic Review and Meta-Analysis

Cardiogenic shock (CS) may have a negative impact on mortality in patients with heart failure (HF) or acute myocardial infarction (AMI). Early prediction of CS can result in improved survival. Artificial intelligence (AI) through machine learning (ML) models have shown promise in predictive medicine. Here, we conduct a systematic review and meta-analysis to assess the effectiveness of these models in the early prediction of CS. A thorough search of the PubMed, Web of Science, Cochrane, and Scopus databases was conducted from the time of inception until November 2, 2023, to find relevant studies. Our outcomes were area under the curve (AUC), the sensitivity and specificity of the ML model, the accuracy of the ML model, and the predictor variables that had the most impact in predicting CS. Comprehensive Meta-Analysis (CMA) Version 3.0 was used to conduct the meta-analysis. Six studies were considered in our study. The pooled mean AUC was 0.808 (95% confidence interval: 0.727, 0.890). The AUC in the included studies ranged from 0.77 to 0.91. ML models performed well, with accuracy ranging from 0.88 to 0.93 and sensitivity and specificity of 58%-78% and 88%-93%, respectively. Age, blood pressure, heart rate, oxygen saturation, and blood glucose were the most significant variables required by ML models to acquire their outputs. In conclusion, AI has the potential for early prediction of CS, which may lead to a decrease in the high mortality rate associated with it. Future studies are needed to confirm the results.