Robust Physician Gaze Prediction Using a Deep Learning Approach
| Autor: | Jacob D. Furst, Tianyi Tan, Enid Montague, Daniela Raicu |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Process (engineering)
business.industry Computer science media_common.quotation_subject Deep learning Sample (statistics) 010501 environmental sciences Machine learning computer.software_genre 01 natural sciences Convolutional neural network Gaze 03 medical and health sciences 0302 clinical medicine Key (cryptography) Quality (business) 030212 general & internal medicine Artificial intelligence Transfer of learning business computer 0105 earth and related environmental sciences media_common |
| Zdroj: | BIBE |
| Popis: | The patient-physician relationship is an integral part of primary care visits. To build a better relationship, understanding the communication between patient and physician is the key. This study focused on analyzing the gaze, one of the most important non-verbal behaviors found to influence patient outcomes. Gaze analysis often needs a manual rating process which might be time-consuming, costly, and unreliable. This research aimed to support automated analysis of physician-patient interaction using a deep convolutional neural network with transfer learning to a build robust model for physician gaze prediction. Utilizing only 3 minutes of 15 videos capturing 3 physicians interacting with different patients in a clinical setting, the model achieved over 98% accuracy for train, test, and validation sets. By visualizing the convolutional layers and comparing sample frames from different interactions, results highlighted several patterns shared across frames predicted correctly from both seen and unseen video sequences. The proposed work has the potential to informed the future design of technologies used to capture the clinical interaction and provide real-time feedback for physicians, which will contribute to the improvement of care quality. |
| Databáze: | OpenAIRE |
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