Exploring the possibilities of an artificial intelligence program in the diagnosis of macular diseases

Мұқаба

Дәйексөз келтіру

Толық мәтін

Аннотация

BACKGROUND: Macular diseases are a large group of pathological conditions that cause vision loss and visual impairment. Early diagnosis of such changes plays an important role in treatment selection and is one of the crucial factors in predicting outcomes.

AIM: To examine the potential of an artificial intelligence program in the diagnosis of macular diseases using structural optical coherence tomography scans.

MATERIALS AND METHODS: The study included patients examined and treated at the Federal Research and Clinical Center of Specialized Medical Care and Medical Technologies and Moscow Regional Research and Clinical Institute. In total, 200 eyes with macular diseases were examined, as well as eyes without macular pathologies. A comparative clinical analysis of structural optical coherence tomography scans obtained using an RTVue XR 110-2 tomograph was conducted. The Retina.AI software was used to analyze optical coherence tomography scans.

RESULTS: In the analysis of optical coherence tomography scans using Retina.AI, various pathological structures of the macula were identified, and a probable pathology was then determined. The results were compared with the diagnoses made by ophthalmologists. The sensitivity, specificity, and accuracy of the method were 95.16%, 97.76%, and 97.38%, respectively.

CONCLUSION: Retina.AI allows ophthalmologists to automatically analyze optical coherence tomography scans and identify various pathological conditions of the fundus.

Толық мәтін

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Авторлар туралы

Margarita Khabazova

Federal Research and Clinical Center of Specialized Medical Care and Medical Technologies

Email: rita.khabazova@mail.ru
ORCID iD: 0000-0002-7770-575X
SPIN-код: 2736-9089
Ресей, Moscow

Elena Ponomareva

Federal Research and Clinical Center of Specialized Medical Care and Medical Technologies

Email: ponomareva.en@fnkc-fmba.ru
ORCID iD: 0009-0001-0828-9844
SPIN-код: 7868-4425
Ресей, Moscow

Igor Loskutov

Moscow Regional Research and Clinical Institute

Email: loskoutigor@mail.ru
ORCID iD: 0000-0003-0057-3338
SPIN-код: 5845-6058

MD, Dr. Sci. (Medicine)

Ресей, Moscow

Evgenia Katalevskaya

Digital Vision Solutions LLC

Email: ekatalevskaya@mail.ru
ORCID iD: 0000-0002-5710-9205
SPIN-код: 7849-8890

MD, Cand. Sci. (Medicine)

Ресей, Moscow

Alexander Sizov

Digital Vision Solutions LLC; Nizhny Novgorod State Technical University n.a. R.E. Alekseev

Email: sizov_ost_vk@mail.ru
ORCID iD: 0000-0003-3338-4015
SPIN-код: 4468-1730
Ресей, Moscow; Nizhny Novgorod

Georgiy Gabaraev

Federal Research and Clinical Center of Specialized Medical Care and Medical Technologies

Хат алмасуға жауапты Автор.
Email: geor_gabaraev1@mail.ru
ORCID iD: 0000-0002-0759-3107
SPIN-код: 1802-3224
Ресей, Moscow

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2. Fig. 1. An example of the optical coherence tomography scan analysis of a patient with diabetic macular edema by the artificial intelligence algorithm: a — structural optical coherence tomography scan; b — optical coherence tomography scan after segmentation of the pathological features (subretinal fluid — green mask, intraretinal cysts — blue masks); c — scan analysis report (the reporting table of the differential diagnostic search, probable pathology is highlighted in red — macular edema).

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3. Fig. 2. An example of the optical coherence tomography scan analysis of a patient with exudative age-related macular degeneration by the artificial intelligence algorithm: a — structural optical coherence tomography scan; b — optical coherence tomography scan after segmentation of the pathological features (subretinal fluid — green mask, intraretinal cysts — blue masks, retinal pigment epithelium detachment — orange mask, subretinal hyperreflective material — yellow mask); c — scan analysis report (the reporting table of the differential diagnostic search, probable pathology is highlighted in red — macular neovascularization).

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4. Fig. 3. An example of the optical coherence tomography scan analysis of a patient with macular hole, epiretinal membrane by the artificial intelligence algorithm: a — structural optical coherence tomography scan; b — optical coherence tomography scan after segmentation of the pathological features (macular hole — violet mask, intraretinal cysts — blue masks, epiretinal membrane — red masks); c — scan analysis report (the reporting table of the differential diagnostic search, probable pathology is highlighted in red — macular hole, epiretinal membrane).

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