Evidence map›Paper›PMID 41743165›Full record

ArticleOphthalmology science2026

Generative Artificial Intelligence-Enabled Saliency Analysis of Eye Tracking in Cerebral/Cortical Visual Impairment (SET-CVI).

Kate Matsunaga, Kleanthis Avramidis, Mark S Borchert, Shrikanth Narayanan, Melinda Y Chang

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. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

What it found

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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

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3 · Its place in the literature

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0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Kate MatsunagaKeck School of Medicine, University of Southern California, Los Angeles, California.
Kleanthis AvramidisViterbi School of Engineering, University of Southern California, Los Angeles, California.
Mark S BorchertKeck School of Medicine, University of Southern California, Los Angeles, California.
Shrikanth NarayananViterbi School of Engineering, University of Southern California, Los Angeles, California.
Melinda Y ChangKeck School of Medicine, University of Southern California, Los Angeles, California.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Purpose: Children with cerebral/cortical visual impairment (CVI) have neurological conditions that impact visual pathways in the brain, leading to deficits in both lower- and higher-order visual function that can be challenging to measure, especially in children with neurodevelopmental delays. We developed a method that combines eye tracking with generative artificial intelligence (AI) to characterize attention to lower- and higher-level visual characteristics in children with CVI. Design: A cross-sectional, comparative cohort study. Participants: Forty-three children with CVI and 40 typically developing pediatric controls. Methods: Participants viewed a series of still images while gaze location was recorded by an eye tracker. For each image, saliency maps highlighting 15 lower- and higher-level visual features were created by a combination of generative AI and pixel-based techniques. Participants' eye tracking fixation maps were compared to saliency maps to calculate fixation saliency values. Fixation saliency values were then compared between CVI participants and controls. The 95% prediction interval of control fixation saliency values was used to establish a normative range, and each CVI participant's fixation saliency values were categorized as normal or abnormal. The distribution of normal and abnormal fixation saliency values for each participant was termed the saliency analysis of eye tracking in CVI (SET-CVI) signature. Main Outcome Measures: Fixation saliency values in children with CVI and controls; accuracy, sensitivity, specificity; and area under the receiver operating characteristic curve (AUC) of SET-CVI in classifying children with CVI and controls. Results: Children with CVI had lower fixation saliency values for higher-level visual features such as human faces and bodies ( Conclusions: Eye tracking combined with generative AI can quantify attention to lower- and higher-level visual characteristics in children with CVI and distinguish affected children from controls. Saliency analysis of eye tracking in CVI signatures may be useful for guiding individualized interventions, tracking progress longitudinally, and serving as an outcome measure in future clinical trials to identify an evidence-based medical treatment for CVI. Financial Disclosures: Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Indexed as

Cerebral visual impairmentCortical visual impairmentEye trackingGenerative artificial intelligence

Identifiers

PMID41743165
PMCPMC12930050

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LicenceCC BY-NC-ND
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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.