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All Outputs (6)

SLO-Net: Enhancing Multiple Sclerosis Diagnosis Beyond Optical Coherence Tomography Using Infrared Reflectance Scanning Laser Ophthalmoscopy Images. (2024)
Journal Article
Arian, R., Aghababaei, A., Soltanipour, A., Khodabandeh, Z., Rakhshani, S., Iyer, S. B., Ashtari, F., Rabbani, H., & Kafieh, R. (2024). SLO-Net: Enhancing Multiple Sclerosis Diagnosis Beyond Optical Coherence Tomography Using Infrared Reflectance Scanning Laser Ophthalmoscopy Images. Translational Vision Science & Technology, 13(7), Article 13. https://doi.org/10.1167/tvst.13.7.13

Several machine learning studies have used optical coherence tomography (OCT) for multiple sclerosis (MS) classification with promising outcomes. Infrared reflectance scanning laser ophthalmoscopy (IR-SLO) captures high-resolution fundus images, comm... Read More about SLO-Net: Enhancing Multiple Sclerosis Diagnosis Beyond Optical Coherence Tomography Using Infrared Reflectance Scanning Laser Ophthalmoscopy Images..

Discrimination of multiple sclerosis using scanning laser ophthalmoscopy images with autoencoder-based feature extraction (2024)
Journal Article
Aghababaei, A., Arian, R., Soltanipour, A., Ashtari, F., Rabbani, H., & Kafieh, R. (2024). Discrimination of multiple sclerosis using scanning laser ophthalmoscopy images with autoencoder-based feature extraction. Multiple Sclerosis and Related Disorders, 88, Article 105743. https://doi.org/10.1016/j.msard.2024.105743

Optical coherence tomography (OCT) investigations have revealed that the thickness of inner retinal layers becomes decreased in multiple sclerosis (MS) patients, compared to healthy control (HC) individuals. To date, a number of studies have applied... Read More about Discrimination of multiple sclerosis using scanning laser ophthalmoscopy images with autoencoder-based feature extraction.

A new convolutional neural network based on combination of circlets and wavelets for macular OCT classification (2023)
Journal Article
Arian, R., Vard, A., Kafieh, R., Plonka, G., & Rabbani, H. (2023). A new convolutional neural network based on combination of circlets and wavelets for macular OCT classification. Scientific Reports, 13(1), Article 22582. https://doi.org/10.1038/s41598-023-50164-7

Artificial intelligence (AI) algorithms, encompassing machine learning and deep learning, can assist ophthalmologists in early detection of various ocular abnormalities through the analysis of retinal optical coherence tomography (OCT) images. Despit... Read More about A new convolutional neural network based on combination of circlets and wavelets for macular OCT classification.

Automatic Choroid Vascularity Index Calculation in Optical Coherence Tomography Images with Low-Contrast Sclerochoroidal Junction Using Deep Learning (2023)
Journal Article
Arian, R., Mahmoudi, T., Riazi-Esfahani, H., Faghihi, H., Mirshahi, A., Ghassemi, F., Khodabande, A., Kafieh, R., & Khalili Pour, E. (2023). Automatic Choroid Vascularity Index Calculation in Optical Coherence Tomography Images with Low-Contrast Sclerochoroidal Junction Using Deep Learning. Photonics, 10(3), Article 234. https://doi.org/10.3390/photonics10030234

The choroidal vascularity index (CVI) is a new biomarker defined for retinal optical coherence tomography (OCT) images for measuring and evaluating the choroidal vascular structure. The CVI is the ratio of the choroidal luminal area (LA) to the total... Read More about Automatic Choroid Vascularity Index Calculation in Optical Coherence Tomography Images with Low-Contrast Sclerochoroidal Junction Using Deep Learning.