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Article

Refining a Deep Learning Model’s Ability to Identify GA Area

The GEODE study evaluated the use of 3D volumetric OCT data to accurately segment the area.

By: Simrat K. Sodhi, MSc, MB BChir (Cantab), Hasenin Al-Khersan, MD, David S. Boyer, MD, Charles C. Wykoff, MD, PhD, Netan Choudhry, MD, FRCSC

Retinal Physician March 1, 2025 Vol 22, Issue March 2025 Page(s): 19-21

Clinical Scorecard: Refining a Deep Learning Model’s Ability to Identify GA Area

At a Glance

Category	Detail
Condition	Geographic Atrophy (GA)
Key Mechanisms	Progressive degeneration of the choriocapillaris, retinal pigment epithelium, and photoreceptors leading to vision loss.
Target Population	Patients with GA, including those with concurrent exudative AMD.
Care Setting	Clinical practice using 3D volumetric OCT data.

Key Highlights

Automated segmentation of GA using a U-Net–based architecture directly from 3D OCT data.
Average coefficient of determination (R2) for Spectralis OCT scans was 0.906.
Average Dice coefficient (DSC) was 0.826, indicating strong agreement between manual and automated grading.
Inclusion of near infrared (nIR) images did not significantly improve DSC.
Systematic case review identified factors affecting segmentation accuracy.

Guideline-Based Recommendations

Diagnosis

Use optical coherence tomography (OCT) as the reference standard for diagnosing and staging GA.

Management

Consider complement inhibitor therapies such as pegcetacoplan and avacincaptad pegol for GA.

Monitoring & Follow-up

Regularly assess GA progression using automated segmentation tools.

Risks

Potential for segmentation errors in cases with small islands of atrophy.

Patient & Prescribing Data

Mean age of 79 years with an average baseline best-corrected visual acuity of 0.64 logMAR.

Automated segmentation may enhance monitoring and treatment planning for GA patients.

Clinical Best Practices

Utilize 3D volumetric OCT data for accurate GA segmentation.
Incorporate systematic reviews of cases to improve algorithm performance.
Ensure machine-agnostic capabilities of the segmentation algorithm.

References

GEODE Study

Hasenin Al-Khersan

Specialties: Ophthalmology

Bio: Hasenin Al-Khersan, MD, is a physician author in retina whose published work in Retinal Physician addresses geographic atrophy in age-related macular degeneration. He coauthored an article on the GEODE study evaluating a deep learning approach using 3D volumetric OCT data to segment areas of geographic atrophy in real-world clinical imaging.

Charles C. Wykoff, MD, PhD

Specialties: Ophthalmology

Bio: Charles C. Wykoff, MD, PhD, is a physician-scientist author in retinal disease. He coauthored a Retinal Physician article on the GEODE study, which evaluated a deep learning approach using 3D volumetric OCT data to segment geographic atrophy area in age-related macular degeneration.

Ashley Brissette

Ophthalmologist

Affiliations: Kelly Vision

Specialties: Ophthalmology

Bio: Ashley Brissette, MD, MSc, FRCSC, is an ophthalmologist in private practice at Kelly Vision in New York City. After a decade in academic medicine, she transitioned into private practice with a focus on patient-centered communication and refractive decision-making. Dr. Brissette writes about the use of social media as an extension of patient education, emphasizing clear, accurate, platform-specific digital content to help patients understand refractive surgery options and support practice growth.

Iqbal K. Ahmed

Specialties: Ophthalmology

Bio: Iqbal K. Ahmed, MD, FRCSC, authored clinical content for Glaucoma Physician on at-home intraocular pressure monitoring and the role of home rebound tonometry in assessing diurnal IOP fluctuations in glaucoma care.

Samuel N. Markowitz

Specialties: Ophthalmology

Bio: Samuel N. Markowitz, MD, FRCSC, is an ophthalmologist who has written on microperimetry and its expanding role in clinical visual function assessment. His work discusses the use of microperimeters to assess retinal sensitivity, scotoma characteristics, fixation stability, and preferred retinal loci in patients with central vision loss.

Netan Choudhry

Specialties: Ophthalmology

Bio: Netan Choudhry, MD, FRCSC, is an ophthalmologist and retina specialist who coauthored a 2025 Retinal Physician article on the GEODE study, which evaluated a deep learning approach using 3D volumetric OCT data to segment geographic atrophy area. His published work in this source focuses on medical retina, including geographic atrophy and age-related macular degeneration, with particular emphasis on imaging and artificial intelligence applications in retinal disease assessment.

Ashley Brissette

Ophthalmologist

Affiliations: Kelly Vision

Specialties: Surgery

Bio: Ashley Brissette is an ophthalmologist at Kelly Vision in New York City. In her Ophthalmology Management article, she describes using social media as an extension of patient education to reinforce refractive surgery decision-making and improve patient understanding. She notes a transition from a decade in academic medicine into private practice, and her current work focuses on creating clear, patient-centered digital educational content across social platforms.