Evaluating machine learning techniques for enhanced glaucoma screening through Pupillary Light Reflex analysis Article Swipe
Hedenir Monteiro Pinheiro
,
Eduardo Nery Rossi Camilo
,
Augusto Paranhos
,
Afonso U. Fonseca
,
Gustavo Teodoro Laureano
,
Ronaldo Martins da Costa
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1016/j.array.2024.100359
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1016/j.array.2024.100359
Glaucoma is a leading cause of irreversible visual field degradation, significantly impacting ocular health. Timely identification and diagnosis of this condition are critical to prevent vision loss. A range of diagnostic techniques is employed to achieve this, from traditional methods reliant on expert interpretation to modern, fully computerized diagnostic approaches. The integration of computerized systems designed for the early detection and classification of clinical indicators of glaucoma holds immense potential to enhance the accuracy of disease diagnosis. Pupillary Light Reflex (PLR) analysis emerges as a promising avenue for glaucoma screening, mainly due to its cost-effectiveness compared to exams such as Optical Coherence Tomography (OCT), Humphrey Field Analyzer (HFA), and fundoscopic examinations. The noninvasive nature of PLR testing obviates the need for disposable components and agents for pupil dilation. This facilitates multiple successive administrations of the test and enables the possibility of remote execution. This study aimed to improve the automated diagnosis of glaucoma using PLR data, conducting an extensive comparative analysis incorporating neural networks and machine learning techniques. It also compared the performance of different data processing methods, including filtering techniques, feature extraction, data balancing, feature selection, and their effects on classification. The findings offer insights and guidelines for future methodologies in glaucoma screening utilizing pupillary light response signals.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1016/j.array.2024.100359
- OA Status
- gold
- Cited By
- 2
- References
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- Related Works
- 10
- OpenAlex ID
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All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4401266158Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1016/j.array.2024.100359Digital Object Identifier
- Title
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Evaluating machine learning techniques for enhanced glaucoma screening through Pupillary Light Reflex analysisWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-08-02Full publication date if available
- Authors
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Hedenir Monteiro Pinheiro, Eduardo Nery Rossi Camilo, Augusto Paranhos, Afonso U. Fonseca, Gustavo Teodoro Laureano, Ronaldo Martins da CostaList of authors in order
- Landing page
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https://doi.org/10.1016/j.array.2024.100359Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
- OA URL
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https://doi.org/10.1016/j.array.2024.100359Direct OA link when available
- Concepts
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Glaucoma, Pupillary light reflex, Computer science, Pupillary response, Artificial intelligence, Optical coherence tomography, Pupil, Feature extraction, Machine learning, Feature selection, Optometry, Medicine, Ophthalmology, Biology, NeuroscienceTop concepts (fields/topics) attached by OpenAlex
- Cited by
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2Total citation count in OpenAlex
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2025: 1, 2024: 1Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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