About

Akram Belghith is an Associate Project Scientist in the Department of Ophthalmology at UC San Diego. His research focuses on the application of artificial intelligence and deep learning techniques to ophthalmology, particularly in the detection and diagnosis of glaucoma. His work involves multimodal implementation of foundation AI models using optic nerve head fundus photographs and OCT imaging, as well as the development of structured OCT report generation and the analysis of retinal nerve fiber layer changes over time. Belghith has contributed to advancing glaucoma detection through the use of deep learning classifiers, vision-language models, and multimodal large language models. His research includes improving diagnostic accuracy in high myopia, analyzing microvascular dropout, and predicting disease progression. His work aims to enhance clinical decision support and automate glaucoma diagnosis, leveraging innovative AI methodologies to improve patient outcomes in ophthalmology.

Research topics

• Computer Science
• Artificial Intelligence
• Ophthalmology
• Medicine
• Optometry
• Machine Learning
• Algorithm
• Computer vision
• Engineering

Selected publications

• Association of Deep Optic Nerve Head Structural Remodeling with Choroidal Microvasculature Dropout in Glaucoma with and without Myopia

  American Journal of Ophthalmology · 2026-04-15

  article
  PublisherDOI

• Performance of General-Purpose Vision Language Models and Ophthalmology Foundation Models in Glaucoma Detection and Function Prediction

  Translational Vision Science & Technology · 2025-11-19 · 1 citations

  articleOpen access

  Purpose: To evaluate the performance of vision-language models (VLMs), in glaucoma detection and visual field (VF) mean deviation (MD) prediction tasks using optical coherence tomography (OCT) images. Methods: A total of 27,610 SPECTRALIS OCT images from 1025 participants (1690 eyes), collected between 2008 and 2021 as part of the Diagnostic Innovations in Glaucoma Study (DIGS) and the African Descent and Glaucoma Evaluation Study (ADAGES), were included. Vision components of LLaVA and PaliGemma, as well as RETFound and ResNet-50 models, were fine-tuned for glaucoma classification and VF MD prediction. Models were trained using OCT circle scans centered on the optic nerve head. Three training configurations were compared. Performance was evaluated using area under the receiver operating characteristic curve (AUC), mean absolute error (MAE), and related metrics. Results: The LLaVA model, when both vision encoder and multi-layer projector were fine-tuned, achieved the best performance with an AUC of 0.92 (95% confidence interval [CI], 0.86-0.95) for glaucoma classification and an MAE of 1.79 dB (95% CI, 1.55-2.00) for VF MD prediction. RETFound and PaliGemma also performed well, with AUCs of 0.91 and 0.90 and MAEs of 1.87 dB and 1.84 dB, respectively. Models with frozen vision encoders showed reduced accuracy. Stratified analysis showed better glaucoma classification in older individuals and moderate-to-advanced cases. VF MD prediction was more accurate in younger individuals, with higher errors in advanced glaucoma. Conclusions: Fine-tuned VLMs demonstrated high performance in glaucoma detection and VF MD prediction, matching or exceeding specialized foundation models and traditional convolutional neural network (CNN)-based methods. Translational Relevance: This study highlights the potential of general-purpose AI models to be adapted for glaucoma care, enabling scalable decision support from OCT imaging.

  PublisherOA PDFDOI

• Glaucoma detection in myopic eyes using deep learning autoencoder-based regions of interest

  Frontiers in Ophthalmology · 2025-08-04 · 2 citations

  articleOpen access

  Purpose: To evaluate the diagnostic accuracy of a deep learning autoencoder-based model utilizing regions of interest (ROI) from optical coherence tomography (OCT) texture enface images for detecting glaucoma in myopic eyes. Methods: This cross-sectional study included a total of 453 eyes from 315 participants from the multi-center "Swept-Source OCT (SS-OCT) Myopia and Glaucoma Study", composed of 268 eyes from 168 healthy individuals and 185 eyes from 147 glaucomatous individuals. All participants underwent swept-source optical coherence tomography (SS-OCT) imaging, from which texture enface images were constructed and analyzed. The study compared four methods: (1) global RNFL thickness, (2) texture enface image, (3) a single autoencoder model trained only on healthy eyes, and (4) a dual autoencoder model trained on both healthy and glaucomatous eyes. Diagnostic accuracy was assessed using the area under the receiver operating curves (AUROC) and precision recall curves (AUPRC). Results: The dual autoencoder model achieved the highest AUROC (95% CI) (0.92 [0.88, 0.95]), significantly outperforming the single autoencoder model trained only on healthy eyes (0.86 [0.83, 0.88], p = 0.01), the global RNFL thickness model (0.84 [0.80, 0.86], p = 0.003), and the texture enface model (0.83 [0.79, 0.85], p = 0.005). Using AUPRC (95% CI), the dual autoencoder model (0.86 [0.83, 0.89]) also outperformed the single autoencoder model trained only on healthy eyes (0.80 [0.78, 0.82], p = 0.02), the global RNFL thickness model (0.74 [0.70, 0.76], p = 0.001), and the texture enface model (0.71 [0.68, 0.73], p<0.001). No significant difference was observed between the global RNFL thickness measurement and the texture enface measurement (p = 0.47). Discussion: The dual autoencoder model, which integrates reconstruction errors from both healthy and glaucomatous training data, demonstrated superior diagnostic accuracy compared to the single autoencoder model, global RNFL thickness and texture enface-based approaches. These findings suggest that deep learning models leveraging ROI-based reconstruction error from texture enface images may enhance glaucoma classification in myopic eyes, providing a robust alternative to conventional structural thickness metrics.

  PublisherOA PDFDOI

• Deep Learning Approach Predicts Longitudinal Retinal Nerve Fiber Layer Thickness Changes

  Bioengineering · 2025-01-31 · 2 citations

  articleOpen access

  This study aims to develop deep learning (DL) models to predict the retinal nerve fiber layer (RNFL) thickness changes in glaucoma, facilitating the early diagnosis and monitoring of disease progression. Using the longitudinal data from two glaucoma studies (Diagnostic Innovations in Glaucoma Study (DIGS) and African Descent and Glaucoma Evaluation Study (ADAGES)), we constructed models using optical coherence tomography (OCT) scans from 251 participants (437 eyes). The models were trained to predict the RNFL thickness at a future visit based on previous scans. We evaluated four models: linear regression (LR), support vector regression (SVR), gradient boosting regression (GBR), and a custom 1D convolutional neural network (CNN). The GBR model achieved the best performance in predicting pointwise RNFL thickness changes (MAE = 5.2 μm, R2 = 0.91), while the custom 1D CNN excelled in predicting changes to average global and sectoral RNFL thickness, providing greater resolution and outperforming the traditional models (MAEs from 2.0–4.2 μm, R2 from 0.94–0.98). Our custom models used a novel approach that incorporated longitudinal OCT imaging to achieve consistent performance across different demographics and disease severities, offering potential clinical decision support for glaucoma diagnosis. Patient-level data splitting enhances the evaluation robustness, while predicting detailed RNFL thickness provides a comprehensive understanding of the structural changes over time.

  PublisherOA PDFDOI

• Glaucoma Detection and Structured OCT Report Generation via a Fine-tuned Multimodal Large Language Model

  ArXiv.org · 2025-10-01

  preprintOpen access

  Objective: To develop an explainable multimodal large language model (MM-LLM) that (1) screens optic nerve head (ONH) OCT circle scans for quality and (2) generates structured clinical reports that include glaucoma diagnosis and sector-wise retinal nerve fiber layer (RNFL) thinning assessments. Design: Retrospective cohort study of 1,310 subjects contributing 43,849 Spectralis ONH OCT circle scans (1,331 glaucomatous and 867 healthy eyes) from the DIGS and ADAGES cohorts. Methods: A MM-LLM (Llama 3.2 Vision-Instruct model) was fine-tuned to generate clinical descriptions of OCT imaging data. Training data included paired OCT images and automatically generated, structured clinical reports that described global and sectoral RNFL thinning. Poor-quality scans were labeled as unusable and paired with a fixed refusal statement. The model was evaluated on a held-out test set for three tasks: quality assessment, glaucoma detection, and RNFL thinning classification across seven anatomical sectors. Evaluation metrics included accuracy, sensitivity, specificity, precision, and F1-score. Model description quality was also evaluated using standard text evaluation metrics. Results: The model achieved 0.90 accuracy and 0.98 specificity for quality triage. For glaucoma detection, accuracy was 0.86 (sensitivity 0.91, specificity 0.73, F1-score 0.91). RNFL thinning prediction accuracy ranged from 0.83 to 0.94, with highest performance in global and temporal sectors. Text generation scores showed strong alignment with reference reports (BLEU: 0.82; ROUGE-1: 0.94; ROUGE-2: 0.87; ROUGE-L: 0.92; BERTScore-F1: 0.99). Conclusions: The fine-tuned MM-LLM generated accurate clinical descriptions based on OCT imaging. The model achieved high accuracy in identifying image quality issues and detecting glaucoma. The model also provided sectoral descriptions of RNFL thinning to help support clinical OCT evaluation.

  PublisherOA PDFDOI

• A Novel Multimodal Implementation of a Foundation Artificial Intelligence Model Using Optic Nerve Head Fundus Photographs and OCT Imaging for Glaucoma Detection

  Ophthalmology Science · 2025-11-20

  articleOpen access

  Purpose: To compare the performance of unimodal and multimodal implementation of the self-supervised learning model RETFound in detecting glaucoma using color fundus photographs (CFPs) and OCT images, and to assess its generalizability across different ethnicities, age groups, and disease severities. Design: Evaluation of a diagnostic technology. Subjects Participants and Controls: Fourteen thousand five hundred ten CFPs and 32 640 OCTs from 1948 eyes of 1098 participants (60.8% glaucoma, 39.2% healthy) from the Diagnostic Innovations in Glaucoma Study and the African Descent and Glaucoma Evaluation Study were included. Glaucoma was defined as photograph-based glaucomatous optic neuropathy with or without repeatable glaucoma visual field damage. Methods: A multimodal RETFound model was developed using paired CFPs and OCT images. The model was compared to unimodal RETFound models using solely CFP or OCT images. Performance was also stratified by race (Black vs. White), age (<60 vs. ≥60 years), and disease severity (mild vs. moderate-to-severe glaucoma). Main Outcome Measures: Diagnostic accuracy of unimodal and multimodal RETFound models using CFP and OCT for detecting glaucoma was assessed using the area under the receiver operating characteristic curve (AUC), precision, and recall. Results: = 0.005) models. Conclusions: The multimodal RETFound model demonstrated improved diagnostic ability compared with the CFP unimodal model but did not significantly outperform the OCT unimodal model in glaucoma detection. As clinical implementation of a unimodal artificial intelligence (AI) model is easier than a multimodal counterpart, our results suggest unimodal OCT AI models may be sufficient for detecting glaucoma. Financial Disclosures: Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

  PublisherDOI

• Relationship of 24-2C Central Visual Field Damage to Juxtapapillary Choriocapillaris Dropout in Glaucoma Eyes With or Without Axial Myopia

  Journal of Glaucoma · 2025-06-19 · 1 citations

  articleCorresponding

  PRÉCIS: Larger choriocapillaris microvasculature dropout area and wider angular circumference are significantly associated with 24-2C central visual field damage in primary open angle glaucoma eyes with and without axial myopia. PURPOSE: To evaluate the relationship between a juxtapapillary choriocapillaris microvasculature dropout (MvD) and central visual field (VF) damage in primary open angle glaucoma (POAG) patients with or without axial myopia. METHODS: This cross-sectional study included 125 patients with POAG or glaucoma suspects stratified into no axial myopia (axial length (AL) ≤24 mm; 46 eyes), mild axial myopia (24 mm < AL ≤26 mm; 81 eyes), and high axial myopia (AL >26 mm; 59 eyes). Presence, area, and angular circumference of juxtapapillary MvD were evaluated on OCT-A en-face choroidal images and B-scans. Perimetry was conducted using the 24-2C and 10-2 Humphrey program. RESULTS: Mean 24-2C VF mean deviation was significantly worse in eyes with MvD compared with eyes without MvD across all groups (all P <0.042). Central VF defects detected in the 24-2C and 10-2 VF tests were significantly more prevalent among eyes with MvD (68.3% and 81.7%, respectively) compared with eyes without MvD (19.0% and 38.1%, respectively) ( P <0.001) in the mild axial myopia group. In multivariable analysis, larger MvD area ( P =0.014) and wider MvD angular circumference ( P =0.006) were significantly associated with higher likelihood of the presence of 24-2C central VF damage in overall cohort. CONCLUSIONS: MvD area and angular circumference are significantly associated with central VF damage detected by VF 24-2C in POAG eyes with and without axial myopia. Choriocapillaris MvD assessment shows promise for identifying POAG patients with a higher risk of having central VF defects and may provide clinical insights into the pathogenesis of glaucoma in myopia.

  PublisherDOI

• Diagnostic Accuracy of 3D Deep Learning Classifiers for Glaucoma Detection: A Comparison of Cross-Domain and Device-Specific Models

  Translational Vision Science & Technology · 2025-08-20 · 1 citations

  articleOpen access1st author

  Purpose: To evaluate the accuracy of a three-dimensional (3D) deep learning (3D DL) and 3D cross domain deep learning (3D CD-DL) classifiers compared to standard macular ganglion cell-inner plexiform layer (GCIPL) thickness measurements for classifying eyes with glaucoma using optical coherence tomography (OCT). Methods: A total of 502 primary open-angle glaucoma eyes from 295 patients and 119 healthy eyes from 63 individuals were included. Two classifiers were compared: (1) a 3D DL model trained on Spectralis macular OCT and applied to Spectralis macular OCT images and (2) 3D CD-DL model trained on synthetic Spectralis images generated from 3D Cirrus macular OCT using Cycle-consistent adversarial networks (CycleGAN) and applied to real Spectralis macula OCT images. An additional 100 different eyes (50 Cirrus, 50 Spectralis) were used to train the CycleGAN. Age, axial length, and disc area adjusted area under the receiver operating curves (AUROC) were used to compare model accuracy. Results: Adjusted AUROC for 3D DL model was 0.92 (95% confidence interval [CI], 0.85-0.95). This was significantly higher than global GCIPL thickness (0.83 [0.78-0.85], p ≤ 0.001) but similar to 3D CD-DL (0.91 [0.84-0.95], P = 0.45). Using only early glaucoma eyes (mean deviation ≥ -3.0 dB), the 3D DL model showed significantly higher diagnostic accuracy (0.90 [0.84-0.94]) compared to global GCIPL thickness (0.80 [0.76-0.82], P ≤ 0.001) but similar to the 3D CD-DL model (0.90 [0.83-0.93], P = 0.51). Conclusions: The 3D DL classifier showed significantly higher diagnostic accuracy than global GCIPL thickness but was similar in performance to the 3D CD-DL classifier. By using synthetic data and diverse training sets, cross-domain learning produces robust, generalizable models across different imaging devices as demonstrated by the comparable accuracy of the 3D CD-DL and device-specific 3D DL models. More data from other OCT devices are needed to further validate these findings. Translational Relevance: The 3D Deep learning models significantly surpass traditional GCIPL thickness measurements for accurately detecting glaucoma. The cross-domain model closely matches the performance of the device-specific model in glaucoma classification potentially reducing the need for device-specific models in clinical practice.

  PublisherOA PDFDOI

• Rates of Choriocapillaris Microvascular Dropout and Macular Structural Changes in Glaucomatous Optic Neuropathy With and Without Myopia

  American Journal of Ophthalmology · 2024-07-08 · 2 citations

  article
  PublisherDOI

• Evaluating glaucoma in myopic eyes: Challenges and opportunities

  Survey of Ophthalmology · 2024-12-18 · 15 citations

  review
  PublisherDOI

Frequent coauthors

• Linda M. Zangwill

  University of California, San Diego

  174 shared

• Robert N. Weinreb

  University of California, San Diego

  170 shared

• Christopher Bowd

  Fleet Science Center

  146 shared

• Jasmin Rezapour

  University of California, San Diego

  89 shared

• Felipe A. Medeiros

  University of Miami

  72 shared

• Mark Christopher

  University of California, San Diego

  68 shared

• Massimo A. Fazio

  University of Alabama at Birmingham

  58 shared

• Michael H. Goldbaum

  52 shared

Education

• Ph.D., Ophthalmology

  University of California, San Diego

  2007

• M.S., Ophthalmology

  University of California, San Diego

  2003

• B.S., Biology

  University of California, San Diego

  2001