Pooya Khosravi

UCI - Computer Science 6210 Donald Bren Hall Irvine, CA 92697-3425

I am currently an MD/PhD candidate at University of California, Irvine, in my second year of Computer Science PhD program advised by professor Xiaohui Xie. Combining my passion for medicine and technology, I aspire to utilize artificial intelligence (AI) and machine learning (ML) to transform healthcare and enhance patient outcomes.

Research Interests:

Computer vision and deep learning in medical image analysis
AI and ML applications in healthcare
Precision medicine and personalized healthcare
Translation of AI and ML research into practical healthcare applications
Artificial Intelligence
Machine Learning

selected publications

2023

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

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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.
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.
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
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