Current state of artificial intelligence applications in ophthalmology and their potential to influence clinical practice

Shetty, Dasharathraj K; Talasila, Abhiroop; Shanbhag, Swapna S; Patil, Vathsala; Hameed, B. M. Zeeshan; Naik, Nithesh; Raju, Adithya

doi:10.1080/23311916.2021.1920707

Cited by 4 publications

(2 citation statements)

References 39 publications

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“…1. Эффективные диагностические алгоритмы должны быть сформированы с использованием размеченных баз данных биомедицинской информации, при этом размечены соответствующими экспертами с указанием характерных поражений [1,2].…”

Section: Introductionunclassified

Ophthalmic Bioengineering. Review

et al. 2023

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This article published the materials of the round table “Bioengineering in ophthalmology” (OphthalmicBioengineering), held on May 13, 2021 as part of the international conference Ural Symposium on Biomedical Engineering, Radioelectronics and Information Technology (USBEREIT). USBEREIT is held under the auspices of the IEEE Engineering in Medicine and Biology Society. The article presents reports on: metrological aspects of registration of tonometric and electrophysiological signals in ophthalmic diagnostics; approaches to modeling the processes of pulse blood filling of the eye with the determination of hemodynamic parameters; retinotoxicity based on electrophysiological signals; analysis of electrophysiological signals in the frequency-time domain and its application in clinical practice; extraction and analysis of specialized data obtained from the electrophysiological medical device; as well as diagnosing retinal diseases based on optical coherence tomography using machine learning.

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Section: Introductionunclassified

Ophthalmic Bioengineering. Review

et al. 2023

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“…It can also complement RAAB by providing broader coverage of eye health issues. Recently, various medical devices have been developed that allow the capture of fundus images through smartphone cameras, enhanced by artificial intelligence (AI) technologies, to diagnose ophthalmic diseases [3,4]. This method facilitates accurate diagnoses, assuming that clinicians capture high-quality fundus images, even in the absence of specialized ophthalmic knowledge.…”

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Prevalence of Selected Ophthalmic Diseases Using a Smartphone-Based Fundus Imaging System in Quang Tri and Thai Nguyen, Vietnam

Kim,

Yoon,

Kim

2024

Healthc Inform Res

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Objectives: This study investigated the prevalence of ophthalmic diseases in Quang Tri and Thai Nguyen, Vietnam, utilizing a smartphone-based fundus imaging (SBFI) system.Methods: This cross-sectional study included nearly 10,000 patients who visited community health centers between July and August 2019. All participants underwent visual acuity testing and fundus imaging. We collected demographic data and medical histories, and fundus images were captured using the EYELIKE system. Data were compiled on an online platform, allowing clinicians from other regions to make diagnoses.Results: The study revealed significant variations in visual acuity and the prevalence of ophthalmic diseases between the two regions. Quang Tri had a higher proportion of individuals with good eyesight compared to Thai Nguyen. In Quang Tri, nearly 50% of the population had media haze, while in Thai Nguyen, about one-third of the population was affected. The prevalence of glaucomatous optic nerve and age-related macular degeneration was approximately 1% higher in Quang Tri than in Thai Nguyen. These findings provide valuable insights into the eye health status of these regions, indicating that eye health in Quang Tri was poorer than in Thai Nguyen.Conclusions: The prevalence rates of ophthalmic conditions in this study were within the expected ranges compared to those in other Asian countries, though they were somewhat low. The SBFI method, being simpler and more efficient than the Rapid Assessment of Avoidable Blindness, offers a promising approach for measuring and estimating the prevalence of ophthalmic diseases.

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