Improving sepsis classification performance with artificial intelligence algorithms: A comprehensive overview of healthcare applications.

Purpose: This study investigates the potential of machine learning (ML) algorithms in improving sepsis diagnosis and prediction, focusing on their relevance in healthcare decision-making. The primary objective is to contribute to healthcare decision-making by evaluating the performance of various supervised and unsupervised models.

Materials And Methods: Through an extensive literature review, optimal ML models used in sepsis research were identified.

A Novel Method to Improve Inter-Clinician Variation in the Diagnosis of Retinopathy of Prematurity Using Machine Learning.

Purpose: Inter-clinician variation could cause uncertainty in disease management. This is reported to be high in Retinopathy of Prematurity (ROP), a potentially blinding retinal disease affecting premature infants. Machine learning has the potential to quantify the differences in decision-making between ROP specialists and trainees and may improve the accuracy of diagnosis.

A computer-aided diagnosis system for plus disease in retinopathy of prematurity with structure adaptive segmentation and vessel based features.

Retinopathy of Prematurity (ROP) is a blinding disease affecting the retina of low birth-weight preterm infants. Accurate diagnosis of ROP is essential to identify treatment-requiring ROP, which would help to prevent childhood blindness. Plus disease, which characterizes abnormal twisting, widening and branching of the blood vessels, is a significant symptom of treatment requiring ROP.