A Multihead Attention Deep Learning Algorithm to Detect Amblyopia Using Fixation Eye Movements
To develop an attention-based deep learning (DL) model based on eye movements acquired during a simple visual fixation task to detect amblyopic subjects across different types and severity from controls. An observational study. We recruited 40 controls and 95 amblyopic subjects (anisometropic = 32;...
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| Veröffentlicht in: | Ophthalmology science (Online) Jg. 5; H. 5; S. 100775 |
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01.09.2025
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| ISSN: | 2666-9145, 2666-9145 |
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| Abstract | To develop an attention-based deep learning (DL) model based on eye movements acquired during a simple visual fixation task to detect amblyopic subjects across different types and severity from controls.
An observational study.
We recruited 40 controls and 95 amblyopic subjects (anisometropic = 32; strabismic = 29; and mixed = 34) at the Cleveland Clinic from 2020 to 2024.
Binocular horizontal and vertical eye positions were recorded using infrared video-oculography during binocular and monocular viewing. Amblyopic subjects were classified as those without nystagmus (n = 42) and those with nystagmus with fusion maldevelopment nystagmus (FMN) or nystagmus that did not meet the criteria of FMN or infantile nystagmus syndrome (n = 53). A multihead attention-based transformer encoder model was trained and cross-validated on deblinked and denoised eye position data acquired during fixation.
Detection of amblyopia across types (anisometropia, strabismus, or mixed) and severity (treated, mild, moderate, or severe) and subjects with and without nystagmus was evaluated with area under the receiver-operator characteristic curves, area under the precision–recall curve (AUPRC), and accuracy.
Area under the receiver-operator characteristic curves for classification of subjects per type were 0.70 ± 0.16 for anisometropia (AUPRC: 0.72 ± 0.08), 0.78 ± 0.15 for strabismus (AUPRC: 0.81 ± 0.16), and 0.80 ± 0.13 for mixed (AUPRC: 0.82 ± 0.15). Area under the receiver-operator characteristic curves for classification of amblyopia subjects per severity were 0.77 ± 0.12 for treated/mild (AUPRC: 0.76 ± 0.18), and 0.78 ± 0.09 for moderate/severe (AUPRC: 0.79 ± 0.16). Th area under the receiver-operator characteristic curve for classification of subjects with nystagmus was 0.83 ± 0.11 (AUPRC: 0.81 ± 0.18), and the area under the receiver-operator characteristic curve for those without nystagmus was 0.75 ± 0.15 (AUPRC: 0.76 ± 0.09).
The multihead transformer DL model classified amblyopia subjects regardless of the type, severity, and presence of nystagmus. The model's ability to identify amblyopia using eye movements alone demonstrates the feasibility of using eye-tracking data in clinical settings to perform objective classifications and complement traditional amblyopia evaluations.
Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article. |
|---|---|
| AbstractList | To develop an attention-based deep learning (DL) model based on eye movements acquired during a simple visual fixation task to detect amblyopic subjects across different types and severity from controls.
An observational study.
We recruited 40 controls and 95 amblyopic subjects (anisometropic = 32; strabismic = 29; and mixed = 34) at the Cleveland Clinic from 2020 to 2024.
Binocular horizontal and vertical eye positions were recorded using infrared video-oculography during binocular and monocular viewing. Amblyopic subjects were classified as those without nystagmus (n = 42) and those with nystagmus with fusion maldevelopment nystagmus (FMN) or nystagmus that did not meet the criteria of FMN or infantile nystagmus syndrome (n = 53). A multihead attention-based transformer encoder model was trained and cross-validated on deblinked and denoised eye position data acquired during fixation.
Detection of amblyopia across types (anisometropia, strabismus, or mixed) and severity (treated, mild, moderate, or severe) and subjects with and without nystagmus was evaluated with area under the receiver-operator characteristic curves, area under the precision-recall curve (AUPRC), and accuracy.
Area under the receiver-operator characteristic curves for classification of subjects per type were 0.70 ± 0.16 for anisometropia (AUPRC: 0.72 ± 0.08), 0.78 ± 0.15 for strabismus (AUPRC: 0.81 ± 0.16), and 0.80 ± 0.13 for mixed (AUPRC: 0.82 ± 0.15). Area under the receiver-operator characteristic curves for classification of amblyopia subjects per severity were 0.77 ± 0.12 for treated/mild (AUPRC: 0.76 ± 0.18), and 0.78 ± 0.09 for moderate/severe (AUPRC: 0.79 ± 0.16). Th area under the receiver-operator characteristic curve for classification of subjects with nystagmus was 0.83 ± 0.11 (AUPRC: 0.81 ± 0.18), and the area under the receiver-operator characteristic curve for those without nystagmus was 0.75 ± 0.15 (AUPRC: 0.76 ± 0.09).
The multihead transformer DL model classified amblyopia subjects regardless of the type, severity, and presence of nystagmus. The model's ability to identify amblyopia using eye movements alone demonstrates the feasibility of using eye-tracking data in clinical settings to perform objective classifications and complement traditional amblyopia evaluations.
Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article. Objective: To develop an attention-based deep learning (DL) model based on eye movements acquired during a simple visual fixation task to detect amblyopic subjects across different types and severity from controls. Design: An observational study. Subjects: We recruited 40 controls and 95 amblyopic subjects (anisometropic = 32; strabismic = 29; and mixed = 34) at the Cleveland Clinic from 2020 to 2024. Methods: Binocular horizontal and vertical eye positions were recorded using infrared video-oculography during binocular and monocular viewing. Amblyopic subjects were classified as those without nystagmus (n = 42) and those with nystagmus with fusion maldevelopment nystagmus (FMN) or nystagmus that did not meet the criteria of FMN or infantile nystagmus syndrome (n = 53). A multihead attention-based transformer encoder model was trained and cross-validated on deblinked and denoised eye position data acquired during fixation. Main Outcome Measures: Detection of amblyopia across types (anisometropia, strabismus, or mixed) and severity (treated, mild, moderate, or severe) and subjects with and without nystagmus was evaluated with area under the receiver-operator characteristic curves, area under the precision–recall curve (AUPRC), and accuracy. Results: Area under the receiver-operator characteristic curves for classification of subjects per type were 0.70 ± 0.16 for anisometropia (AUPRC: 0.72 ± 0.08), 0.78 ± 0.15 for strabismus (AUPRC: 0.81 ± 0.16), and 0.80 ± 0.13 for mixed (AUPRC: 0.82 ± 0.15). Area under the receiver-operator characteristic curves for classification of amblyopia subjects per severity were 0.77 ± 0.12 for treated/mild (AUPRC: 0.76 ± 0.18), and 0.78 ± 0.09 for moderate/severe (AUPRC: 0.79 ± 0.16). Th area under the receiver-operator characteristic curve for classification of subjects with nystagmus was 0.83 ± 0.11 (AUPRC: 0.81 ± 0.18), and the area under the receiver-operator characteristic curve for those without nystagmus was 0.75 ± 0.15 (AUPRC: 0.76 ± 0.09). Conclusions: The multihead transformer DL model classified amblyopia subjects regardless of the type, severity, and presence of nystagmus. The model's ability to identify amblyopia using eye movements alone demonstrates the feasibility of using eye-tracking data in clinical settings to perform objective classifications and complement traditional amblyopia evaluations. Financial Disclosure(s): Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article. To develop an attention-based deep learning (DL) model based on eye movements acquired during a simple visual fixation task to detect amblyopic subjects across different types and severity from controls. An observational study. We recruited 40 controls and 95 amblyopic subjects (anisometropic = 32; strabismic = 29; and mixed = 34) at the Cleveland Clinic from 2020 to 2024. Binocular horizontal and vertical eye positions were recorded using infrared video-oculography during binocular and monocular viewing. Amblyopic subjects were classified as those without nystagmus (n = 42) and those with nystagmus with fusion maldevelopment nystagmus (FMN) or nystagmus that did not meet the criteria of FMN or infantile nystagmus syndrome (n = 53). A multihead attention-based transformer encoder model was trained and cross-validated on deblinked and denoised eye position data acquired during fixation. Detection of amblyopia across types (anisometropia, strabismus, or mixed) and severity (treated, mild, moderate, or severe) and subjects with and without nystagmus was evaluated with area under the receiver-operator characteristic curves, area under the precision–recall curve (AUPRC), and accuracy. Area under the receiver-operator characteristic curves for classification of subjects per type were 0.70 ± 0.16 for anisometropia (AUPRC: 0.72 ± 0.08), 0.78 ± 0.15 for strabismus (AUPRC: 0.81 ± 0.16), and 0.80 ± 0.13 for mixed (AUPRC: 0.82 ± 0.15). Area under the receiver-operator characteristic curves for classification of amblyopia subjects per severity were 0.77 ± 0.12 for treated/mild (AUPRC: 0.76 ± 0.18), and 0.78 ± 0.09 for moderate/severe (AUPRC: 0.79 ± 0.16). Th area under the receiver-operator characteristic curve for classification of subjects with nystagmus was 0.83 ± 0.11 (AUPRC: 0.81 ± 0.18), and the area under the receiver-operator characteristic curve for those without nystagmus was 0.75 ± 0.15 (AUPRC: 0.76 ± 0.09). The multihead transformer DL model classified amblyopia subjects regardless of the type, severity, and presence of nystagmus. The model's ability to identify amblyopia using eye movements alone demonstrates the feasibility of using eye-tracking data in clinical settings to perform objective classifications and complement traditional amblyopia evaluations. Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article. To develop an attention-based deep learning (DL) model based on eye movements acquired during a simple visual fixation task to detect amblyopic subjects across different types and severity from controls.ObjectiveTo develop an attention-based deep learning (DL) model based on eye movements acquired during a simple visual fixation task to detect amblyopic subjects across different types and severity from controls.An observational study.DesignAn observational study.We recruited 40 controls and 95 amblyopic subjects (anisometropic = 32; strabismic = 29; and mixed = 34) at the Cleveland Clinic from 2020 to 2024.SubjectsWe recruited 40 controls and 95 amblyopic subjects (anisometropic = 32; strabismic = 29; and mixed = 34) at the Cleveland Clinic from 2020 to 2024.Binocular horizontal and vertical eye positions were recorded using infrared video-oculography during binocular and monocular viewing. Amblyopic subjects were classified as those without nystagmus (n = 42) and those with nystagmus with fusion maldevelopment nystagmus (FMN) or nystagmus that did not meet the criteria of FMN or infantile nystagmus syndrome (n = 53). A multihead attention-based transformer encoder model was trained and cross-validated on deblinked and denoised eye position data acquired during fixation.MethodsBinocular horizontal and vertical eye positions were recorded using infrared video-oculography during binocular and monocular viewing. Amblyopic subjects were classified as those without nystagmus (n = 42) and those with nystagmus with fusion maldevelopment nystagmus (FMN) or nystagmus that did not meet the criteria of FMN or infantile nystagmus syndrome (n = 53). A multihead attention-based transformer encoder model was trained and cross-validated on deblinked and denoised eye position data acquired during fixation.Detection of amblyopia across types (anisometropia, strabismus, or mixed) and severity (treated, mild, moderate, or severe) and subjects with and without nystagmus was evaluated with area under the receiver-operator characteristic curves, area under the precision-recall curve (AUPRC), and accuracy.Main Outcome MeasuresDetection of amblyopia across types (anisometropia, strabismus, or mixed) and severity (treated, mild, moderate, or severe) and subjects with and without nystagmus was evaluated with area under the receiver-operator characteristic curves, area under the precision-recall curve (AUPRC), and accuracy.Area under the receiver-operator characteristic curves for classification of subjects per type were 0.70 ± 0.16 for anisometropia (AUPRC: 0.72 ± 0.08), 0.78 ± 0.15 for strabismus (AUPRC: 0.81 ± 0.16), and 0.80 ± 0.13 for mixed (AUPRC: 0.82 ± 0.15). Area under the receiver-operator characteristic curves for classification of amblyopia subjects per severity were 0.77 ± 0.12 for treated/mild (AUPRC: 0.76 ± 0.18), and 0.78 ± 0.09 for moderate/severe (AUPRC: 0.79 ± 0.16). Th area under the receiver-operator characteristic curve for classification of subjects with nystagmus was 0.83 ± 0.11 (AUPRC: 0.81 ± 0.18), and the area under the receiver-operator characteristic curve for those without nystagmus was 0.75 ± 0.15 (AUPRC: 0.76 ± 0.09).ResultsArea under the receiver-operator characteristic curves for classification of subjects per type were 0.70 ± 0.16 for anisometropia (AUPRC: 0.72 ± 0.08), 0.78 ± 0.15 for strabismus (AUPRC: 0.81 ± 0.16), and 0.80 ± 0.13 for mixed (AUPRC: 0.82 ± 0.15). Area under the receiver-operator characteristic curves for classification of amblyopia subjects per severity were 0.77 ± 0.12 for treated/mild (AUPRC: 0.76 ± 0.18), and 0.78 ± 0.09 for moderate/severe (AUPRC: 0.79 ± 0.16). Th area under the receiver-operator characteristic curve for classification of subjects with nystagmus was 0.83 ± 0.11 (AUPRC: 0.81 ± 0.18), and the area under the receiver-operator characteristic curve for those without nystagmus was 0.75 ± 0.15 (AUPRC: 0.76 ± 0.09).The multihead transformer DL model classified amblyopia subjects regardless of the type, severity, and presence of nystagmus. The model's ability to identify amblyopia using eye movements alone demonstrates the feasibility of using eye-tracking data in clinical settings to perform objective classifications and complement traditional amblyopia evaluations.ConclusionsThe multihead transformer DL model classified amblyopia subjects regardless of the type, severity, and presence of nystagmus. The model's ability to identify amblyopia using eye movements alone demonstrates the feasibility of using eye-tracking data in clinical settings to perform objective classifications and complement traditional amblyopia evaluations.Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.Financial DisclosuresProprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article. ObjectiveTo develop an attention-based deep learning (DL) model based on eye movements acquired during a simple visual fixation task to detect amblyopic subjects across different types and severity from controls. DesignAn observational study. SubjectsWe recruited 40 controls and 95 amblyopic subjects (anisometropic = 32; strabismic = 29; and mixed = 34) at the Cleveland Clinic from 2020 to 2024. MethodsBinocular horizontal and vertical eye positions were recorded using infrared video-oculography during binocular and monocular viewing. Amblyopic subjects were classified as those without nystagmus (n = 42) and those with nystagmus with fusion maldevelopment nystagmus (FMN) or nystagmus that did not meet the criteria of FMN or infantile nystagmus syndrome (n = 53). A multihead attention-based transformer encoder model was trained and cross-validated on deblinked and denoised eye position data acquired during fixation. Main Outcome MeasuresDetection of amblyopia across types (anisometropia, strabismus, or mixed) and severity (treated, mild, moderate, or severe) and subjects with and without nystagmus was evaluated with area under the receiver-operator characteristic curves, area under the precision–recall curve (AUPRC), and accuracy. ResultsArea under the receiver-operator characteristic curves for classification of subjects per type were 0.70 ± 0.16 for anisometropia (AUPRC: 0.72 ± 0.08), 0.78 ± 0.15 for strabismus (AUPRC: 0.81 ± 0.16), and 0.80 ± 0.13 for mixed (AUPRC: 0.82 ± 0.15). Area under the receiver-operator characteristic curves for classification of amblyopia subjects per severity were 0.77 ± 0.12 for treated/mild (AUPRC: 0.76 ± 0.18), and 0.78 ± 0.09 for moderate/severe (AUPRC: 0.79 ± 0.16). Th area under the receiver-operator characteristic curve for classification of subjects with nystagmus was 0.83 ± 0.11 (AUPRC: 0.81 ± 0.18), and the area under the receiver-operator characteristic curve for those without nystagmus was 0.75 ± 0.15 (AUPRC: 0.76 ± 0.09). ConclusionsThe multihead transformer DL model classified amblyopia subjects regardless of the type, severity, and presence of nystagmus. The model's ability to identify amblyopia using eye movements alone demonstrates the feasibility of using eye-tracking data in clinical settings to perform objective classifications and complement traditional amblyopia evaluations. Financial Disclosure(s)Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article. |
| ArticleNumber | 100775 |
| Author | Sahoo, Satya S. Cakir, Gokce Albert, Jeffrey Shaikh, Aasef Ramat, Stefano Upadhyaya, Dipak P. Ghasia, Fatema |
| Author_xml | – sequence: 1 givenname: Dipak P. surname: Upadhyaya fullname: Upadhyaya, Dipak P. organization: Department of Computer and Systems Engineering, School of Engineering, Case Western Reserve University, Cleveland, Ohio – sequence: 2 givenname: Gokce surname: Cakir fullname: Cakir, Gokce organization: Department of Ophthalmology, Cole Eye Institute, Cleveland Clinic, Cleveland, Ohio – sequence: 3 givenname: Stefano surname: Ramat fullname: Ramat, Stefano organization: Department of Biomedical Engineering, University of Pavia, Pavia, Lombardy, Italy – sequence: 4 givenname: Jeffrey surname: Albert fullname: Albert, Jeffrey organization: Department of Population and Quantitative Health Sciences, School of Medicine, Case Western Reserve University, Cleveland, Ohio – sequence: 5 givenname: Aasef surname: Shaikh fullname: Shaikh, Aasef organization: Daroff-Dell'Osso Ocular Motility Laboratory, Louis Stokes Cleveland VA Medical Center, Cleveland, Ohio – sequence: 6 givenname: Satya S. surname: Sahoo fullname: Sahoo, Satya S. email: satya.sahoo@case.edu organization: Department of Computer and Systems Engineering, School of Engineering, Case Western Reserve University, Cleveland, Ohio – sequence: 7 givenname: Fatema surname: Ghasia fullname: Ghasia, Fatema email: ghasiaf@ccf.org organization: Department of Ophthalmology, Cole Eye Institute, Cleveland Clinic, Cleveland, Ohio |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/40458668$$D View this record in MEDLINE/PubMed |
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| Keywords | Strabismus D FMN DL MV Fixation eye movements FEM AEV BV AUPRC FEV Amblyopia Artificial intelligence Eye tracking fixation eye movement amblyopic eye viewing binocular viewing fusion maldevelopment nystagmus deep learning area under the precision–recall curve diopters fellow eye viewing monocular viewin |
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| PublicationDateYYYYMMDD | 2025-09-01 |
| PublicationDate_xml | – month: 09 year: 2025 text: 2025-09-01 day: 01 |
| PublicationDecade | 2020 |
| PublicationPlace | Netherlands |
| PublicationPlace_xml | – name: Netherlands |
| PublicationTitle | Ophthalmology science (Online) |
| PublicationTitleAlternate | Ophthalmol Sci |
| PublicationYear | 2025 |
| Publisher | Elsevier Inc Elsevier |
| Publisher_xml | – name: Elsevier Inc – name: Elsevier |
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| Snippet | To develop an attention-based deep learning (DL) model based on eye movements acquired during a simple visual fixation task to detect amblyopic subjects across... ObjectiveTo develop an attention-based deep learning (DL) model based on eye movements acquired during a simple visual fixation task to detect amblyopic... Objective: To develop an attention-based deep learning (DL) model based on eye movements acquired during a simple visual fixation task to detect amblyopic... |
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| SubjectTerms | Amblyopia Artificial intelligence Eye tracking Fixation eye movements Ophthalmology Original Strabismus |
| Title | A Multihead Attention Deep Learning Algorithm to Detect Amblyopia Using Fixation Eye Movements |
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