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A novel system for the automatic reconstruction of visual field based on eye tracking and machine learning

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Abstract

Eye movement perimetry (EMP) is a paradigm developed to assess the visual field without the necessity of suppressing the natural eye movements during the test. Unlike the standard automated perimetry (SAP) where the patient’s responses are recorded using a button, EMP uses the natural eye movements reflex as responses during the evaluation. The reliability of EMP depends on correctly determining whether a stimulus is seen or not which, in turn, depends on an adequate analysis of the eye movement data. However, many studies in EMP have focused on characterizing eye movements and only a few authors have documented their methods to determine whether a peripheral stimulus was seen during the test. Furthermore, many of them use static thresholds to perform the classification, but it is not clear how these threshold values were obtained. Based on the foregoing, we develop a threshold test based on FASTPAC C24-2 and EMP for the visual field assessment. Our method uses two machine learning techniques: (1) cascaded K-Means and Bayesian classifiers (KBC) and (2) an Artificial Neural Network (ANN) to classify whether a stimulus was seen or not. Our method was validated with twenty healthy participants (13 women and 7 men) aged 19–43 years (µ = 26 ± 5 years), where the participants performed both an EMP test and an SAP emulation test. Results were compared with gaze trajectories annotations performed by an expert, obtaining accuracy values between 96.8% and 98.9% for KBC and ANN, and values between 90.5% and 92% for SAP emulation.

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Acknowledgements

E.A. Martínez-González acknowledges CONACYT- México for the scholarship 712805.

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Authors and Affiliations

  1. Facultad de Ciencias, Universidad Autónoma de San Luis Potosí, San Luis Potosí, S.L.P, México

    Eduardo A. Martínez-González, Alfonso Alba, Edgar Arce-Santana & Martin O. Mendez

  2. Laboratorio Nacional CI3M, Universidad Autónoma de San Luis Potosí, San Luis Potosí, S.L.P, México

    Alfonso Alba, Edgar Arce-Santana & Martin O. Mendez

  3. Hospital de Especialidades Médicas de la Salud, San Luis Potosí, S.L.P, México

    Jorge Fernández-Wong

Authors
  1. Eduardo A. Martínez-González
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  2. Alfonso Alba
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  3. Edgar Arce-Santana
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  4. Jorge Fernández-Wong
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  5. Martin O. Mendez
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Correspondence to Martin O. Mendez.

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The study presented in this article was conducted following the ethical standards of the institution (Universidad Autónoma de San Luis Potosí) and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

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Signed informed consent was obtained from all the participants included in the study.

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Maintaining the privacy of the participants and the data confidentiality, all the participants gave their consent to use their data acquired during the experiment in this research.

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Martínez-González, E.A., Alba, A., Arce-Santana, E. et al. A novel system for the automatic reconstruction of visual field based on eye tracking and machine learning. Multimed Tools Appl 82, 27193–27215 (2023). https://doi.org/10.1007/s11042-023-14464-4

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  • DOI: https://doi.org/10.1007/s11042-023-14464-4

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