Vision-language models for decoding provider attention during neonatal resuscitation
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arXiv
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| Auteurs principaux: | , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866911821604061184 |
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| author | Parodi, Felipe Matelsky, Jordan Regla-Vargas, Alejandra Foglia, Elizabeth Lim, Charis Weinberg, Danielle Kording, Konrad Herrick, Heidi Platt, Michael |
| author_facet | Parodi, Felipe Matelsky, Jordan Regla-Vargas, Alejandra Foglia, Elizabeth Lim, Charis Weinberg, Danielle Kording, Konrad Herrick, Heidi Platt, Michael |
| contents | Neonatal resuscitations demand an exceptional level of attentiveness from providers, who must process multiple streams of information simultaneously. Gaze strongly influences decision making; thus, understanding where a provider is looking during neonatal resuscitations could inform provider training, enhance real-time decision support, and improve the design of delivery rooms and neonatal intensive care units (NICUs). Current approaches to quantifying neonatal providers' gaze rely on manual coding or simulations, which limit scalability and utility. Here, we introduce an automated, real-time, deep learning approach capable of decoding provider gaze into semantic classes directly from first-person point-of-view videos recorded during live resuscitations. Combining state-of-the-art, real-time segmentation with vision-language models (CLIP), our low-shot pipeline attains 91\% classification accuracy in identifying gaze targets without training. Upon fine-tuning, the performance of our gaze-guided vision transformer exceeds 98\% accuracy in gaze classification, approaching human-level precision. This system, capable of real-time inference, enables objective quantification of provider attention dynamics during live neonatal resuscitation. Our approach offers a scalable solution that seamlessly integrates with existing infrastructure for data-scarce gaze analysis, thereby offering new opportunities for understanding and refining clinical decision making. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_01207 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Vision-language models for decoding provider attention during neonatal resuscitation Parodi, Felipe Matelsky, Jordan Regla-Vargas, Alejandra Foglia, Elizabeth Lim, Charis Weinberg, Danielle Kording, Konrad Herrick, Heidi Platt, Michael Computer Vision and Pattern Recognition Neonatal resuscitations demand an exceptional level of attentiveness from providers, who must process multiple streams of information simultaneously. Gaze strongly influences decision making; thus, understanding where a provider is looking during neonatal resuscitations could inform provider training, enhance real-time decision support, and improve the design of delivery rooms and neonatal intensive care units (NICUs). Current approaches to quantifying neonatal providers' gaze rely on manual coding or simulations, which limit scalability and utility. Here, we introduce an automated, real-time, deep learning approach capable of decoding provider gaze into semantic classes directly from first-person point-of-view videos recorded during live resuscitations. Combining state-of-the-art, real-time segmentation with vision-language models (CLIP), our low-shot pipeline attains 91\% classification accuracy in identifying gaze targets without training. Upon fine-tuning, the performance of our gaze-guided vision transformer exceeds 98\% accuracy in gaze classification, approaching human-level precision. This system, capable of real-time inference, enables objective quantification of provider attention dynamics during live neonatal resuscitation. Our approach offers a scalable solution that seamlessly integrates with existing infrastructure for data-scarce gaze analysis, thereby offering new opportunities for understanding and refining clinical decision making. |
| title | Vision-language models for decoding provider attention during neonatal resuscitation |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2404.01207 |