• COVID-19
  • Biosimilars
  • Cataract Therapeutics
  • DME
  • Gene Therapy
  • Workplace
  • Ptosis
  • Optic Relief
  • Imaging
  • Geographic Atrophy
  • AMD
  • Presbyopia
  • Ocular Surface Disease
  • Practice Management
  • Pediatrics
  • Surgery
  • Therapeutics
  • Optometry
  • Retina
  • Cataract
  • Pharmacy
  • IOL
  • Dry Eye
  • Understanding Antibiotic Resistance
  • Refractive
  • Cornea
  • Glaucoma
  • OCT
  • Ocular Allergy
  • Clinical Diagnosis
  • Technology

AI: Capturing more information from OCT in neovascular AMD


Reviewed by Anat Loewentstein, MD, MHA

Using optical coherence tomography (OCT), physicians can determine with even more accuracy what is happening in patients’ eyes with neovascular age-related macular degeneration (nAMD) because of the potential afforded by the application of artificial intelligence (AI), according to Anat Loewenstein, MD, MHA.

Loewenstein is a professor and the director of the Department of Ophthalmology at Tel Aviv Medical Center. She also is the Sidney Fox Chair in Ophthalmology, and vice dean of the Sackler Faculty of Medicine at Tel Aviv University in Israel.

OCT is a major step forward in patient diagnosis, treatment, and monitoring but shortcomings remain. For example, physicians routinely make qualitative assessments of the presence and degrees of intraretinal/subretinal fluid and pigment epithelial detachments, but these are not precise assessments that are likely to result in poor intergrader agreement and intragrader consistency; OCT also provides the central subfield thickness, but the retinal fluid and neural tissue are not considered separately.

As Loewenstein pointed out, with neovascular AMD it is important to distinguish between retinal fluid localization in the intraretinal and subretinal compartments and their volumetric information for informing retreatment decisions and predicting visual outcomes.

One technology to quantify OCT data, according to Loewenstein, is the home-based Notal OCT Analyzer (NOA), a machine learning algorithm that distinguishes normal morphologic features from elevated or distorted contours to quantify macular fluid of OCT volumes. The NOA quantifies intraretinal/subretinal fluid on Spectralis and Cirrus spectral-domain OCT devices. Both algorithms display their respective fluid output in nanoliters, which allows repeatable measurements and the precise monitoring of disease activity.

“Recent advances in machine learning and deep learning have resulted in the development of software that can process routinely acquired spectral-domain OCT data and precisely determine the location and volumetric information of macular fluid within different tissue compartments,” Loewenstein said.

According to Loewenstein, the software then can automatically generate multiple quantitative metrics related to macular fluid variables that may provide substantial advantages to research and clinical practice.

Loewenstein suggested that using volume instead of thickness, separating fluid volume from neural tissue volume, distinguishing between intraretinal and subretinal fluid, and monitoring dynamics over time might improve OCT’s ability to predict visual acuity changes.

Moreover, she emphasized the following:

  • Because of the importance of OCT in assessing nAMD parameters, for which the fluid volumes in the retina are generally the key biomarkers, the availability of software that can analyze them quantitatively, automatically, and at scale is paramount.
  • In practice, the ability to extract quantitative metrics of exudative activity from OCT scans is an important advantage over qualitative descriptions.
  • By eliminating low intragrader consistency and intergrader agreement, exudative activity can be compared more meaningfully between sequential visits and visits that are widely separated of the same patient, or between patients, regardless of the individual physician’s discretion.
  • Quantitative metrics may result in better recordkeeping and visualization of exudative activity; the values from each visit can be recorded. This can facilitate graphs of fluid volume plotted over time based on changes in treatment intervals, drugs, and doses.
  • AI methods allow assessments and comparisons to be made separately for intraretinal/subretinal fluid and pigment epithelial detachments.
  • Not all exudative activity is equal regarding visual prognosis and determining retreatment decisions.
  • Future studies may lead to refined retreatment protocols possibly incorporating more nuanced approaches according to fluid compartment/volume.
  • Algorithms that identify and quantify fluid types should help facilitate these approaches.
  • “AI tools may lead to more tailored treatment, ideally so that individuals achieve optimal visual outcomes with as few injections and visits as possible,” Loewenstein concluded. “This might help address some of the shortfall in visual outcomes that has consistently been observed between real-world practice and clinical trials.”

Anat Loewenstein, MD, MHA

E: anatl@tlvmc.gov.il

This article was adapted from Loewenstein’s presentation at the European Society of Retina Specialists virtual annual congress. She is a consultant to Notal Vision.

Related Videos
Eye stroke: Mount Sinai program stresses awareness and emergent care for sudden vision loss
What the re:Vive 2.0 platform may mean for the future of vision diagnostics
Deep learning analysis detects diabetic retinopathy on ultra-widefield scanning laser ophthalmoscope images
Exploring ancillary testing orders by retinal specialists according to the IRIS registry
© 2024 MJH Life Sciences

All rights reserved.