: In the United States, high rates of vision impairment and eye disease disproportionately impact those who lack access to eye care, specifically vulnerable populations. The objective of our study was to test instruments, implement protocols, and collect preliminary data for a larger 5-year study, which aims to improve detection of eye diseases and follow-up eye care in vulnerable populations using community health workers (CHW) and patient navigators. In the study, trained CHWs conducted vision screening and patient navigators scheduled on-site eye exams and arranged appointments for those referred to ophthalmology to improve adherence to follow-up eye care. Eligible individuals age 40-and-older were recruited from the Riverstone Senior Center in Upper Manhattan, New York City. Participants underwent on-site vision screening (visual acuity with correction, intraocular pressure measurements, and fundus photography). Individuals who failed the vision screening were scheduled with an on-site optometrist for an eye exam; those with ocular pathologies were referred to an ophthalmologist. Participants were also administered the National Eye Institute Visual Function Questionnaire-8 (NEI-VFQ-8) and Stopping Elderly Accidents, Deaths, and Injuries (STEADI) test by community health workers.Participants (n = 42) were predominantly older adults, with a mean age of 70.0 ± 9.8, female (61.9%), and Hispanic (78.6%). Most individuals (78.6%, n = 33) failed vision screening. Of those who failed, 84.8% (n = 28) attended the on-site eye exam with the optometrist. Ocular diagnoses: refractive error 13/28 (46.4%), glaucoma/glaucoma suspect 9/28 (32.1%), cataract 7/28 (25.0%), retina abnormalities 6/28 (21.4%); 13 people required eyeglasses. This study demonstrates the feasibility of using CHWs and patient navigators for reducing barriers to vision screening and optometrist-based eye exams in vulnerable populations, ultimately improving early detection of eye disease and linking individuals to additional eye care appointments. The full five-year study aims to further examine these outcomes.
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http://dx.doi.org/10.1080/02713683.2021.1905000 | DOI Listing |
Front Public Health
January 2025
Department of Ophthalmology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China.
Introduction: Diabetic retinopathy grading plays a vital role in the diagnosis and treatment of patients. In practice, this task mainly relies on manual inspection using human visual system. However, the human visual system-based screening process is labor-intensive, time-consuming, and error-prone.
View Article and Find Full Text PDFPediatr Rheumatol Online J
January 2025
Hamburger Zentrum für Kinder- und Jugendrheumatologie, am Schön Klinik Hamburg Eilbek, Hamburg, Germany.
Childhood blindness significantly impacts development, education, employment, and mental health, creating burden for families and society. Between 8% and 30% of children with Juvenile Idiopathic Arthritis (JIA) develop a potentially blinding chronic inflammatory eye disease, uveitis (JIAU). Alongside the use of disease-modifying agents and anti-TNF immunomodulators, JIAU surveillance has helped to reduce the risk of JIAU related blindness.
View Article and Find Full Text PDFNPJ Digit Med
January 2025
Harvard Ophthalmology AI Lab, Schepens Eye Research Institute of Massachusetts Eye and Ear, Harvard Medical School, Boston, MA, USA.
Glaucoma is the leading cause of irreversible blindness globally. Research indicates a disproportionate impact of glaucoma on racial and ethnic minorities. Existing deep learning models for glaucoma detection might not achieve equitable performance across diverse identity groups.
View Article and Find Full Text PDFACS Omega
January 2025
Laboratory of Natural Products and Mass Spectrometry (LAPNEM), Faculty of Pharmaceutical Sciences, Food, and Nutrition (FACFAN), Federal University of Mato Grosso do Sul, Campo Grande, Mato Grosso do Sul 79070-900, Brazil.
Leishmaniases present a significant global health challenge with limited and often inadequate treatment options available. Traditional microscopic methods for detecting Leishmania amastigotes are time-consuming and error-prone, highlighting the need for automated approaches. This study aimed to implement and validate the YOLOv8 deep learning model for real-time detection, quantification, and categorization of Leishmania amastigotes to enhance drug screening assays.
View Article and Find Full Text PDFRev Neurosci
January 2025
557765 Network of Neurosurgery and Artificial Intelligence (NONAI), Universal Scientific Education and Research Network (USERN ), Tehran, Iran.
The recognition and classification of facial expressions using artificial intelligence (AI) presents a promising avenue for early detection and monitoring of neurodegenerative disorders. This narrative review critically examines the current state of AI-driven facial expression analysis in the context of neurodegenerative diseases, such as Alzheimer's and Parkinson's. We discuss the potential of AI techniques, including deep learning and computer vision, to accurately interpret and categorize subtle changes in facial expressions associated with these pathological conditions.
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