Evidence map›Paper›PMID 41552657›Full record

ArticleOphthalmology science2026

Longitudinal Validation of the Artificial Intelligence Algorithm in Home OCT for Age-Related Macular Degeneration-Report 3.

Theodore Leng, Ella H Leung, S Krishna Mukkamala, Mehran R Taban, Moshe Havilio, Kester Nahen, Nishant Mohan, Gidi Benyamini, Tiarnan D L Keenan

Abstract read
In one paragraph

Article in Ophthalmology science, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

9 authors.

Theodore LengByers Eye Institute at Stanford, Stanford University School of Medicine, Palo Alto, California.
Ella H LeungGeorgia Retina, Atlanta, Georgia.
S Krishna MukkamalaGeorgia Retina, Atlanta, Georgia.
Mehran R TabanMacula Retina Vitreous Research Institute, Torrance, California.
Moshe HavilioNotal Vision Inc., Manassas, Virginia.
Kester NahenNotal Vision Inc., Manassas, Virginia.
Nishant MohanNotal Vision Inc., Manassas, Virginia.
Gidi BenyaminiNotal Vision Inc., Manassas, Virginia.
Tiarnan D L KeenanDivision of Epidemiology and Clinical Applications, National Eye Institute, National Institutes of Health, Bethesda, Maryland.

Funding

Stanford Vision Research CoreP30EY026877 · NEI · STANFORD UNIVERSITY · PI Jeffrey L Goldberg · 2017 to 2026
$8.0M
NEI NIH HHS P30 EY026877
6 · The paper itself

Abstract

Purpose: Longitudinal validation of the artificial intelligence-based Notal OCT Analyzer (NOA) for identification of clinically significant changes in the trajectories of retinal total hypo-reflective volume (TRO) from daily home OCT (HOCT) self-imaging in eyes with age-related macular degeneration. Design: Post hoc analysis of the HOCT Fluid Visualization Agreement Study. Participants: Three hundred seventeen eyes of 180 participants who self-imaged daily using the HOCT for 5 weeks. Methods: For each eye study, the ground truth of TRO stability or change was defined by human experts grading the 5-week time series of HOCT volume scans. The TRO trajectory of the 5 weeks was plotted separately for each study eye by NOA. Three approaches to identifying the optimal threshold (OT) for a clinically significant change in TRO were pursued: (1) personalized approach based on the reference change value methodology used in laboratory medicine; (2) optimized uniform approach; (3) uniform approach commonly used of 10 volume units (VU). The personalized approach comprised TRO curve-fitting to evaluate the change in amplitude (signal) and within-subject variations (noise), followed by receiver operating characteristic analysis of the signal-to-noise ratio (SNR) to identify the OT for determination of a clinically significant change in TRO. Main Outcome Measures: Area under the receiver operating characteristic curve (AUROC); sensitivity, specificity, and accuracy at the OT. Results: Of the 296 trajectories analyzed, 107 (36.1%) were classified as changing and the remaining 189 (63.9%) as stable. The personalized approach had an AUROC of 0.9811 (with 99.1% sensitivity, 89.4% specificity, and 94.2% accuracy), at OT of SNR = 2.42. The optimized uniform approach had an AUROC of 0.9687 (with 94.4% sensitivity, 89.4% specificity, and 91.9% accuracy), at OT of 3.88 VU. The 10 VU uniform approach had 76.6% sensitivity, 94.7% specificity, and 85.7% accuracy. Conclusions: The NOA-generated trajectories permitted highly accurate determination of TRO change vs. stability, with favorable SNR. The personalized approach had higher sensitivity in detecting TRO change at 99.1% than the uniform approaches. Notal OCT analyzer and its trajectories were a reliable tool for identifying clinically relevant changes in retinal fluid status by the quality standards of laboratory medicine. Financial Disclosures: Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Indexed as

Artificial intelligenceHome OCTNeovascular age-related macular degenerationOCTReference change value

Identifiers

PMID41552657
PMCPMC12811451

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.