Diagnosis of diabetes in pregnant woman using a Chaotic-Jaya hybridized extreme learning machine model

On AI Approaches for Promoting Maternal and Neonatal Health in Low Resource Settings: A Review

A significant challenge for hospitals and medical practitioners in low- and middle-income nations is the lack of sufficient health care facilities for timely medical diagnosis of chronic and deadly diseases. Particularly, maternal and neonatal morbidity due to various non-communicable and nutrition related diseases is a serious public health issue that leads to several deaths every year. These diseases affecting either mother or child can be hospital-acquired, contracted during pregnancy or delivery, postpartum and even during child growth and development. Many of these conditions are challenging to detect at their early stages, which puts the patient at risk of developing severe conditions over time. Therefore, there is a need for early screening, detection and diagnosis, which could reduce maternal and neonatal mortality. With the advent of Artificial Intelligence (AI), digital technologies have emerged as practical assistive tools in different healthcare sectors but are still in their nascent stages when applied to maternal and neonatal health. This review article presents an in-depth examination of digital solutions proposed for maternal and neonatal healthcare in low resource settings and discusses the open problems as well as future research directions.

Hybrid stacked ensemble combined with genetic algorithms for diabetes prediction

Diabetes is currently one of the most common, dangerous, and costly diseases globally caused by increased blood sugar or a decrease in insulin in the body. Diabetes can have detrimental effects on people’s health if diagnosed late. Today, diabetes has become one of the challenges for health and government officials. Prevention is a priority, and taking care of people’s health without compromising their comfort is an essential need. In this study, the ensemble training methodology based on genetic algorithms was used to diagnose and predict the outcomes of diabetes mellitus accurately. This study uses the experimental data, actual data on Indian diabetics on the University of California website. Current developments in ICT, such as the Internet of Things, machine learning, and data mining, allow us to provide health strategies with more intelligent capabilities to accurately predict the outcomes of the disease in daily life and the hospital and prevent the progression of this disease and its many complications. The results show the high performance of the proposed method in diagnosing the disease, which has reached 98.8%, and 99% accuracy in this study.

Microvascular Changes After Conbercept Intravitreal Injection of PDR With or Without Center-Involved Diabetic Macular Edema Analyzed by OCTA

Purpose

To investigate the intravitreal injection of conbercept as a treatment strategy for proliferative diabetic retinopathy (PDR) with or without center-involved diabetic macular edema (CI-DME) and evaluate its effect on the microvascular changes in the eyes.

Methods

In this prospective study, 43 patients including 29 cases (56 eyes) in CI-DME with PDR patients, and 14 cases (26 eyes) in the non-center involving diabetic macular edema (NCI-DME) with PDR patients were involved in this study. The best corrected visual acuity (BCVA), central retinal thickness (CRT), foveolar avascular zone (FAZ), and macular capillary vessel density (VD) of the superficial retinal capillary plexus (SCP) and deep retinal capillary plexus (DCP) were assessed before and after conbercept treatments for 1, 3, or 6 months.

Results

The BCVA was significantly increased after conbercept treatment in the eyes of CI-DME patients. After 6 months of treatment with the conbercept, microvascular density of the inferior area in SCP and the central fovea area in DCP increased significantly, regardless of the central fovea involvement. The effect of the conbercept treatment on the VD of NCI-DME was higher than that of CI-DME. Then, after 6 months of treatment, the CRT of patients with CI-DME and NCI-DME were decreased significantly.

Conclusions

In this study, an intravitreal injection of conbercept significantly improved vision, alleviated macular edema in patients with DME. Conbercept treatment also altered the microvascular density in the retina.