Evidence map›Paper›PMID 41444668›Full record

ArticleInternational journal of retina and vitreous2025

Detection of cystoid macular edema in patients with retinitis pigmentosa based on deep learning.

Farhang Hosseini, Farkhondeh Asadi, Hamid Ahmadieh, Reza Rabiei, Arash Roshanpoor, Sahba Fekri, Morteza Naderan, Firouze Hatami, Fatemeh Rostami, Mahdi Yazdanpanah and 2 more

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Article in International journal of retina and vitreous, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

12 authors.

Farhang HosseiniGastroenterology and Liver Diseases Research Center, Research Institute for Gastroenterology and Liver Diseases, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Farkhondeh AsadiDepartment of Health Information Technology and Management, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran. asadifar@sbmu.ac.ir.
Hamid AhmadiehOphthalmic Research Center, Research Institute for Ophthalmology and Vision Science, Shahid Behehsti University of Medical Sciences, Tehran, Iran.
Reza RabieiDepartment of Health Information Technology and Management, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Arash RoshanpoorDepartment of Computer, Yadegar-e-Imam Khomeini (RAH) Shahre Rey Branch, Islamic Azad University, Tehran, Iran.
Sahba FekriOphthalmic Research Center, Research Institute for Ophthalmology and Vision Science, Shahid Behehsti University of Medical Sciences, Tehran, Iran.
Morteza NaderanOphthalmic Research Center, Research Institute for Ophthalmology and Vision Science, Shahid Behehsti University of Medical Sciences, Tehran, Iran.
Firouze HatamiOphthalmic Research Center, Research Institute for Ophthalmology and Vision Science, Shahid Behehsti University of Medical Sciences, Tehran, Iran.
Fatemeh RostamiDepartment of Optometry, School of Rehabilitation, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Mahdi YazdanpanahAmirkabir University of Technology, Tehran, Iran.
Rayan Ebnali HarariHarvard Data Science Initiative, Harvard University, Cambridge, MA, USA.
Hamideh SabbaghiOphthalmic Research Center, Research Institute for Ophthalmology and Vision Science, Shahid Behehsti University of Medical Sciences, Tehran, Iran. Sabbaghi.opt@gmail.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundCystoid macular edema (CME) is a leading cause of vision loss in patients with retinitis pigmentosa (RP). The present study was aimed to CME in patients with RP using deep learning (DL) models based on the analysis of the optical coherence tomography (OCT) images.

methodsIn this cross-sectional study, a total of 1,318 OCT scans of 296 eyes from RP patients were analyzed with scans grouped based on the presence (670 images) or absence (648 images) of CME. We used Spectral-Domain OCT (SD-OCT) to measure central foveal thickness and detect retinal abnormalities, including subclinical CME in the study groups. The dataset was stratified and divided into training and testing sets using a subject-wise split at an 80:20 ratio using the scikit-learn library. Resnet-34 and ResNet-18 model architectures were developed to automatically detect CME in RP patients, and their performance was evaluated and compared with other DL algorithms.

resultsFine-tuning pretrained ResNet-34 and ResNet-18 models achieved an accuracy of 99.25%, 98.75%, F1-score of 99.26%, 98.77% and ROC of 99% and non-pretrained ResNet-34 and ResNet-18 achieved an accuracy of 80.64%, 82.13%, F1-score of 83.88%, 84.87% and ROC of 80%, 82% in detection of CME in RP patients.

conclusionThis study was the first to apply DL algorithms to diagnose and manage CME in RP patients using OCT images. Pretrained ResNet models, particularly ResNet-34 with 99.25% accuracy, outperformed non-pretrained ResNet-34 with 80.64% accuracy. These results underscore the potential of pretrained models to aid in detection CME and supporting remote healthcare services.

Indexed as

Artificial intelligenceCystoid macular edemaDeep learningOptical coherence tomographyRetinitis pigmentosa

Identifiers

PMID41444668
PMCPMC12729141

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