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Author Spotlight: An Automated Method for Assessing Visual Acuity in Infants and Toddlers Using an Eye-Tracking System
Published on: March 17, 2023
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Automatic eye localization for hospitalized infants and children using convolutional neural networks
Vanessa Prinsen1, Philippe Jouvet2, Sally Al Omar3
1École de technologie supérieure, 1100 Notre-Dame St W, Montréal, Québec H3C 1K3 Canada; CHU Sainte-Justine, 3175 Chemin de la Côte-Sainte-Catherine, Montréal, QC H3T 1C5.
International Journal of Medical Informatics
|December 28, 2020
Summary
Developing custom datasets and models significantly improves eye localization for pediatric patients in intensive care units (ICUs). This enhances clinical decision support and patient monitoring accuracy in challenging hospital environments.
Area of Science:
- Computer Vision
- Medical Imaging
- Artificial Intelligence
Background:
- Accurate eye localization is crucial for pediatric patient monitoring and clinical decision support.
- Existing eye localization models perform poorly in pediatric hospital settings due to variations in appearance and medical equipment.
Purpose of the Study:
- To develop and evaluate improved eye localization models for pediatric intensive care units (ICUs).
- To address the limitations of existing models in busy pediatric hospital environments.
Main Methods:
- Created two datasets: one for training using public data and another for testing with pediatric ICU recordings.
- Trained two eye localization models using the Faster R-CNN algorithm and a pre-trained ResNet base network.
Main Results:
- A convolutional neural network trained on combined adult and child data achieved a 79.7% eye localization rate.
- Image contrast equalization further improved the localization rate to 84%.
Conclusions:
- Convolutional neural networks show promise for eye localization and tracking in pediatric ICUs, even with limited data.
- Task-specific datasets and transfer learning are effective for developing specialized clinical computer vision applications.
