Noninvasive Anemia Detection and Hemoglobin Estimation from Retinal Images Using Deep Learning: A Scalable Solution for Resource-Limited Settings.

Purpose: The purpose of this study was to develop and validate a deep-learning model for noninvasive anemia detection, hemoglobin (Hb) level estimation, and identification of anemia-related retinal features using fundus images.

Methods: The dataset included 2265 participants aged 40 years and above from a population-based study in South India. The dataset included ocular and systemic clinical parameters, dilated retinal fundus images, and hematological data such as complete blood counts and Hb concentration levels.

Clinical criteria for limbic-predominant age-related TDP-43 encephalopathy.

Limbic predominant age-related TDP-43 encephalopathy neuropathologic change (LATE-NC) is highly prevalent in late life and a common co-pathology with Alzheimer's disease neuropathologic change (ADNC). LATE-NC is a slowly progressive, amnestic clinical syndrome. Alternatively, when present with ADNC, LATE-NC is associated with a more rapid course.

Evaluating the role of serum cystatin C as a circulating biomarker of diabetic retinopathy from a rural Indian population.

Purpose: This study aimed to evaluate serum cystatin C as a potential biomarker for diabetic retinopathy (DR) in a rural Indian population, addressing the urgent need for effective screening tools amidst rising diabetes prevalence.

Materials And Methods: A cross-sectional study recruited 112 patients with diabetes mellitus from Sambalpur, Odisha, India, categorized into groups with and without DR. Serum cystatin C levels were measured alongside clinical and demographic parameters, using established diagnostic methods.

Phenotype and genetic spectrum of six Indian patients with bestrophinopathy.

The aim of this study is to describe genotype and phenotype of patients with bestrophinopathy. The case records were reviewed retrospectively, findings of multimodal imaging such as color fundus photograph, optical coherence tomography (OCT), fundus autofluorescence, electrophysiological, and genetic tests were noted. Twelve eyes of six patients from distinct Indian families with molecular diagnosis were enrolled.

Artificial intelligence-enabled safety monitoring in Alzheimer's disease clinical trials.

Background: Investigators conducting clinical trials have an ethical, scientific, and regulatory obligation to protect the safety of trial participants. Traditionally, safety monitoring includes manual review and coding of adverse event data by expert clinicians.

Objectives: Our study explores the use of natural language processing (NLP) and artificial intelligence (AI) methods to streamline and standardize clinician coding of adverse event data in Alzheimer's disease (AD) clinical trials.