Outputs (7)

Association of peripapillary retinal nerve fiber layer atrophy with cognitive impairment in patients with neuromyelitis optica spectrum disorder. (2024)
Journal Article
Barzegar, M., Ashtari, F., Kafieh, R., Karimi, Z., Dehghani, A., Ghalamkari, A., Afshari-Safavi, A., & Paul, F. (online). Association of peripapillary retinal nerve fiber layer atrophy with cognitive impairment in patients with neuromyelitis optica spectrum disorder. Neurological Sciences, https://doi.org/10.1007/s10072-024-07897-8

We aimed to explore the association between peripapillary retinal nerve fiber layer thickness (pRNFL), macular ganglion cell-inner plexiform layer (mGCIPL), and cognitive impairment (CI) in patients with neuromyelitis optica spectrum disorder (NMOSD)... Read More about Association of peripapillary retinal nerve fiber layer atrophy with cognitive impairment in patients with neuromyelitis optica spectrum disorder..

Application of Artificial Intelligence in Ophthalmology: An Updated Comprehensive Review. (2024)
Journal Article
Hashemian, H., Peto, T., Ambrósio, R., Lengyel, I., Kafieh, R., Muhammed Noori, A., & Khorrami-Nejad, M. (2024). Application of Artificial Intelligence in Ophthalmology: An Updated Comprehensive Review. Journal of Ophthalmic and Vision Research, 19(3), 354-367. https://doi.org/10.18502/jovr.v19i3.15893

Artificial intelligence (AI) holds immense promise for transforming ophthalmic care through automated screening, precision diagnostics, and optimized treatment planning. This paper reviews recent advances and challenges in applying AI techniques such... Read More about Application of Artificial Intelligence in Ophthalmology: An Updated Comprehensive Review..

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.

Diagnosis of multiple sclerosis by detecting asymmetry within the retina using a similarity-based neural network (2024)
Journal Article
Bolton, R. C., Kafieh, R., Ashtari, F., & Atapour-Abarghouei, A. (2024). Diagnosis of multiple sclerosis by detecting asymmetry within the retina using a similarity-based neural network. IEEE Access, 12, 62975-62985. https://doi.org/10.1109/access.2024.3395995

Multiple sclerosis (MS) is a chronic neurological disorder that targets the central nervous system, causing demyelination and neural disruption, which can include retinal nerve damage leading to visual disturbances. The purpose of this study is to de... Read More about Diagnosis of multiple sclerosis by detecting asymmetry within the retina using a similarity-based neural network.

Deep learning for discrimination of active and inactive lesions in multiple sclerosis using non-contrast FLAIR MRI: A multicenter study. (2024)
Journal Article
Amini, A., Shayganfar, A., Amini, Z., Ostovar, L., HajiAhmadi, S., Chitsaz, N., Rabbani, M., & Kafieh, R. (2024). Deep learning for discrimination of active and inactive lesions in multiple sclerosis using non-contrast FLAIR MRI: A multicenter study. Multiple Sclerosis and Related Disorders, 87, Article 105642. https://doi.org/10.1016/j.msard.2024.105642

Within the domain of multiple sclerosis (MS), the precise discrimination between active and inactive lesions bears immense significance. Active lesions are enhanced on T1-weighted MRI images after administration of gadolinium-based contrast agents, w... Read More about Deep learning for discrimination of active and inactive lesions in multiple sclerosis using non-contrast FLAIR MRI: A multicenter study..

Recursive autoencoder network for prediction of CAD model parameters from STEP files (2024)
Presentation / Conference Contribution
Miles, V., Giani, S., Vogt, O., & Kafieh, R. Recursive autoencoder network for prediction of CAD model parameters from STEP files

Databases of 3D CAD (computer aided design) models are often large and lacking in meaningful organisation. Effective tools for automatically searching for, categorising and comparing CAD models, therefore, have many potential applic... Read More about Recursive autoencoder network for prediction of CAD model parameters from STEP files.