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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.

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.

Application of ImageJ in Optical Coherence Tomography Angiography (OCT-A): A Literature Review (2023)
Journal Article
Rahimi, M., Khameneh, E. A., Riazi-Esfahani, H., Mahmoudi, T., Khalili Pour, E., & Kafieh, R. (2023). Application of ImageJ in Optical Coherence Tomography Angiography (OCT-A): A Literature Review. Journal of Ophthalmology, 2023, https://doi.org/10.1155/2023/9479183

Background. This study aimed to review the literature on the application of ImageJ in optical coherence tomography angiography (OCT-A) images. Methods. A general search was performed in PubMed, Google Scholar, and Scopus databases. The authors evalua... Read More about Application of ImageJ in Optical Coherence Tomography Angiography (OCT-A): A Literature Review.

Loss-Modified Transformer-Based U-Net for Accurate Segmentation of Fluids in Optical Coherence Tomography Images of Retinal Diseases. (2023)
Journal Article
Darooei, R., Nazari, M., Kafieh, R., & Rabbani, H. (2023). Loss-Modified Transformer-Based U-Net for Accurate Segmentation of Fluids in Optical Coherence Tomography Images of Retinal Diseases. Journal of Medical Signals and Sensors, 13(4), 253-260. https://doi.org/10.4103/jmss.jmss_52_22

Optical coherence tomography (OCT) imaging significantly contributes to ophthalmology in the diagnosis of retinal disorders such as age-related macular degeneration and diabetic macular edema. Both diseases involve the abnormal accumulation of fluids... Read More about Loss-Modified Transformer-Based U-Net for Accurate Segmentation of Fluids in Optical Coherence Tomography Images of Retinal Diseases..

Biased Deep Learning Methods in Detection of COVID-19 Using CT Images: A Challenge Mounted by Subject-Wise-Split ISFCT Dataset (2023)
Journal Article
Parsarad, S., Saeedizadeh, N., Soufi, G. J., Shafieyoon, S., Hekmatnia, F., Zarei, A. P., Soleimany, S., Yousefi, A., Nazari, H., Torabi, P., S. Milani, A., Madani Tonekaboni, S. A., Rabbani, H., Hekmatnia, A., & Kafieh, R. (2023). Biased Deep Learning Methods in Detection of COVID-19 Using CT Images: A Challenge Mounted by Subject-Wise-Split ISFCT Dataset. Journal of Imaging, 9(8), Article 159. https://doi.org/10.3390/jimaging9080159

Accurate detection of respiratory system damage including COVID-19 is considered one of the crucial applications of deep learning (DL) models using CT images. However, the main shortcoming of the published works has been unreliable reported accuracy... Read More about Biased Deep Learning Methods in Detection of COVID-19 Using CT Images: A Challenge Mounted by Subject-Wise-Split ISFCT Dataset.

Discrimination of multiple sclerosis using OCT images from two different centers (2023)
Journal Article
Khodabandeh, Z., Rabbani, H., Ashtari, F., Zimmermann, H. G., Motamedi, S., Brandt, A. U., Paul, F., & Kafieh, R. (2023). Discrimination of multiple sclerosis using OCT images from two different centers. Multiple Sclerosis and Related Disorders, 77(September), Article 104846. https://doi.org/10.1016/j.msard.2023.104846

Background: Multiple sclerosis (MS) is one of the most prevalent chronic inflammatory diseases caused by demyelination and axonal damage in the central nervous system. Structural retinal imaging via optical coherence tomography (OCT) shows promise as... Read More about Discrimination of multiple sclerosis using OCT images from two different centers.

Optimal Deep Learning Architecture for Automated Segmentation of Cysts in OCT Images Using X-Let Transforms (2023)
Journal Article
Darooei, R., Nazari, M., Kafieh, R., & Rabbani, H. (2023). Optimal Deep Learning Architecture for Automated Segmentation of Cysts in OCT Images Using X-Let Transforms. Diagnostics, 13(12), Article 1994. https://doi.org/10.3390/diagnostics13121994

The retina is a thin, light-sensitive membrane with a multilayered structure found in the back of the eyeball. There are many types of retinal disorders. The two most prevalent retinal illnesses are Age-Related Macular Degeneration (AMD) and Diabetic... Read More about Optimal Deep Learning Architecture for Automated Segmentation of Cysts in OCT Images Using X-Let Transforms.

CNV-Net: Segmentation, Classification and Activity Score Measurement of Choroidal Neovascularization (CNV) Using Optical Coherence Tomography Angiography (OCTA) (2023)
Journal Article
Vali, M., Nazari, B., Sadri, S., Pour, E. K., Riazi-Esfahani, H., Faghihi, H., Ebrahimiadib, N., Azizkhani, M., Innes, W., Steel, D. H., Hurlbert, A., Read, J. C., & Kafieh, R. (2023). CNV-Net: Segmentation, Classification and Activity Score Measurement of Choroidal Neovascularization (CNV) Using Optical Coherence Tomography Angiography (OCTA). Diagnostics, 13(7), Article 1309. https://doi.org/10.3390/diagnostics13071309

This paper aims to present an artificial intelligence-based algorithm for the automated segmentation of Choroidal Neovascularization (CNV) areas and to identify the presence or absence of CNV activity criteria (branching, peripheral arcade, dark halo... Read More about CNV-Net: Segmentation, Classification and Activity Score Measurement of Choroidal Neovascularization (CNV) Using Optical Coherence Tomography Angiography (OCTA).

Differentiating Glaucomatous Optic Neuropathy from Non-Glaucomatous Optic Neuropathies Using Deep Learning Algorithms (2023)
Journal Article
Vali, M., Mohammadi, M., Zarei, N., Samadi, M., Atapour-Abarghouei, A., Supakontanasan, W., Suwan, Y., Subramanian, P. S., Miller, N. R., Kafieh, R., & Fard, M. A. (2023). Differentiating Glaucomatous Optic Neuropathy from Non-Glaucomatous Optic Neuropathies Using Deep Learning Algorithms. American Journal of Ophthalmology, 252, 1-8. https://doi.org/10.1016/j.ajo.2023.02.016

Purpose : A deep learning framework to differentiate glaucomatous optic disc changes (GON) from non-glaucomatous optic neuropathy-related disc changes (NGON). Design : Cross-sectional study. Method : A deep-learning system was trained, validated, and... Read More about Differentiating Glaucomatous Optic Neuropathy from Non-Glaucomatous Optic Neuropathies Using Deep Learning Algorithms.

Diagnostic value of intereye difference metrics for optic neuritis in aquaporin-4 antibody seropositive neuromyelitis optica spectrum disorders (2023)
Journal Article
Oertel, F. C., Zimmermann, H. G., Motamedi, S., Chien, C., Aktas, O., Albrecht, P., Ringelstein, M., Dcunha, A., Pandit, L., Martinez-Lapiscina, E. H., Sanchez-Dalmau, B., Villoslada, P., Palace, J., Roca-Fernández, A., Leite, M. I., Sharma, S. M., Leocani, L., Pisa, M., Radaelli, M., Lana-Peixoto, M. A., …Petzold, A. (2023). Diagnostic value of intereye difference metrics for optic neuritis in aquaporin-4 antibody seropositive neuromyelitis optica spectrum disorders. Journal of Neurology, Neurosurgery and Psychiatry, 94(7), 560-566. https://doi.org/10.1136/jnnp-2022-330608

Background: The novel optic neuritis (ON) diagnostic criteria include intereye differences (IED) of optical coherence tomography (OCT) parameters. IED has proven valuable for ON diagnosis in multiple sclerosis but has not been evaluated in aquaporin-... Read More about Diagnostic value of intereye difference metrics for optic neuritis in aquaporin-4 antibody seropositive neuromyelitis optica spectrum disorders.

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.

Introduction to Optical Coherence Tomography (2023)
Book Chapter
Hajizadeh, F., Kafieh, R., & Tajmirriahi, M. (2023). Introduction to Optical Coherence Tomography. In F. Hajizadeh (Ed.), Atlas of Ocular Optical Coherence Tomography (1-34). (2). Springer Verlag. https://doi.org/10.1007/978-3-031-07410-3_1

(1) Basics and principles of optical coherence tomography (OCT), which briefly discuss the mechanisms and operation of OCT systems and a comparison of old and new systems (time domain vs. spectral domain) and their reproducibility. It concisely expla... Read More about Introduction to Optical Coherence Tomography.

Segmentation of pancreatic ductal adenocarcinoma (PDAC) and surrounding vessels in CT images using deep convolutional neural networks and texture descriptors (2022)
Journal Article
Mahmoudi, T., Kouzahkanan, Z. M., Radmard, A. R., Kafieh, R., Salehnia, A., Davarpanah, A. H., Arabalibeik, H., & Ahmadian, A. (2022). Segmentation of pancreatic ductal adenocarcinoma (PDAC) and surrounding vessels in CT images using deep convolutional neural networks and texture descriptors. Scientific Reports, 12(1), Article 3092. https://doi.org/10.1038/s41598-022-07111-9