Pooya Khosravi

2024

1. AJO

   Retinal Nerve Fiber Layer Optical Texture Analysis and 10-2 Visual Field Assessment in Glaucoma

   Alireza Kamalipour, Sasan Moghimi, Pooya Khosravi, Natchada Tansuebchueasai, and 8 more authors

   American Journal of Ophthalmology, 2024

   Abs

   Purpose To apply retinal nerve fiber layer (RNFL) optical texture analysis (ROTA) to 1) investigate the association between papillomacular and papillofoveal bundle defects with 10-2 visual field (VF) sensitivity abnormalities, and 2) integrate the information from RNFL bundle defect and 24-2 VF central test locations to determine the likelihood of 10-2 VF sensitivity abnormalities. Design Cross-sectional Methods A total of 841 eyes (144 healthy, 317 glaucoma suspect, and 380 glaucoma) of 442 participants were included. Eyes underwent 24-2, and 10-2 VF testing and OCT for ROTA. The borders of RNFL defects were delineated from ROTA, and the involvement of the arcuate, papillomacular, and papillofoveal bundles was determined for each eye. Multilevel logistic regression analysis was applied to evaluate the structure–function association. Results Papillomacular (92.1%) and papillofoveal (37.9%) RNFL bundle defects were prevalent in eyes with glaucoma. A 10-2 VF location that was projected onto a papillomacular or a papillofoveal RNFL bundle defect had a significantly increased likelihood of reduced sensitivity (ORs of 18.61 at PDP < 5%, and 20.17 at TDP < 5%, respectively, P < 0.001 for both). When predicting the likelihood of VF abnormality in a 10-2 test location, noticeably higher odds ratios were observed when overlapping with an RNFL bundle defect, compared to when an abnormal corresponding 24-2 central point was present. Conclusions Papillomacular and papillofoveal RNFL bundle defects are present in a considerable proportion of eyes with glaucoma. When detected, they significantly increase the likelihood of abnormality in the corresponding central VF test locations assessed by the 10-2 test.

2023

1. IJMS

   Deep Learning Approach for Differentiating Etiologies of Pediatric Retinal Hemorrhages: A Multicenter Study

   Pooya Khosravi, Nolan A. Huck, Kourosh Shahraki, Stephen C. Hunter, and 8 more authors

   International Journal of Molecular Sciences, 2023

   Abs

   Retinal hemorrhages in pediatric patients can be a diagnostic challenge for ophthalmologists. These hemorrhages can occur due to various underlying etiologies, including abusive head trauma, accidental trauma, and medical conditions. Accurate identification of the etiology is crucial for appropriate management and legal considerations. In recent years, deep learning techniques have shown promise in assisting healthcare professionals in making more accurate and timely diagnosis of a variety of disorders. We explore the potential of deep learning approaches for differentiating etiologies of pediatric retinal hemorrhages. Our study, which spanned multiple centers, analyzed 898 images, resulting in a final dataset of 597 retinal hemorrhage fundus photos categorized into medical (49.9%) and trauma (50.1%) etiologies. Deep learning models, specifically those based on ResNet and transformer architectures, were applied; FastViT-SA12, a hybrid transformer model, achieved the highest accuracy (90.55%) and area under the receiver operating characteristic curve (AUC) of 90.55%, while ResNet18 secured the highest sensitivity value (96.77%) on an independent test dataset. The study highlighted areas for optimization in artificial intelligence (AI) models specifically for pediatric retinal hemorrhages. While AI proves valuable in diagnosing these hemorrhages, the expertise of medical professionals remains irreplaceable. Collaborative efforts between AI specialists and pediatric ophthalmologists are crucial to fully harness AI’s potential in diagnosing etiologies of pediatric retinal hemorrhages.

2. MICCAI

   MedGen3D: A Deep Generative Framework for Paired 3D Image and Mask Generation

   Kun Han, Yifeng Xiong, Chenyu You, Pooya Khosravi, and 4 more authors

   In Medical Image Computing and Computer Assisted Intervention – MICCAI 2023, 2023

   Abs

   Acquiring and annotating sufficient labeled data is crucial in developing accurate and robust learning-based models, but obtaining such data can be challenging in many medical image segmentation tasks. One promising solution is to synthesize realistic data with ground-truth mask annotations. However, no prior studies have explored generating complete 3D volumetric images with masks. In this paper, we present MedGen3D, a deep generative framework that can generate paired 3D medical images and masks. First, we represent the 3D medical data as 2D sequences and propose the Multi-Condition Diffusion Probabilistic Model (MC-DPM) to generate multi-label mask sequences adhering to anatomical geometry. Then, we use an image sequence generator and semantic diffusion refiner conditioned on the generated mask sequences to produce realistic 3D medical images that align with the generated masks. Our proposed framework guarantees accurate alignment between synthetic images and segmentation maps. Experiments on 3D thoracic CT and brain MRI datasets show that our synthetic data is both diverse and faithful to the original data, and demonstrate the benefits for downstream segmentation tasks. We anticipate that MedGen3D’s ability to synthesize paired 3D medical images and masks will prove valuable in training deep learning models for medical imaging tasks.

3. AJO

   Deep learning estimation of 10-2 visual field map based on circumpapillary retinal nerve fiber layer thickness measurements

   Alireza Kamalipour, Sasan Moghimi, Pooya Khosravi, Mohammad Sadegh Jazayeri, and 7 more authors

   American Journal of Ophthalmology, 2023
4. AJO

   Combining Optical Coherence Tomography and Optical Coherence Tomography Angiography Longitudinal Data for the Detection of Visual Field Progression in Glaucoma

   Alireza Kamalipour, Sasan Moghimi, Pooya Khosravi, Vahid Mohammadzadeh, and 7 more authors

   American Journal of Ophthalmology, 2023