SafeTriage: Facial Video De-identification for Privacy-Preserving Stroke Triage

• URL: http://arxiv.org/abs/2506.16578v1
• Date: Thu, 19 Jun 2025 20:02:47 GMT
• Title: SafeTriage: Facial Video De-identification for Privacy-Preserving Stroke Triage
• Authors: Tongan Cai, Haomiao Ni, Wenchao Ma, Yuan Xue, Qian Ma, Rachel Leicht, Kelvin Wong, John Volpi, Stephen T. C. Wong, James Z. Wang, Sharon X. Huang,
• Abstract summary: Effective stroke triage in emergency settings often relies on clinicians' ability to identify subtle abnormalities in facial muscle coordination.<n>Recent AI models have shown promise in detecting such patterns from patient facial videos, but their reliance on real patient data raises ethical and privacy challenges.<n>We propose SafeTriage, a novel method designed to de-identify patient facial videos while preserving essential motion cues crucial for stroke diagnosis.
• Score: 9.63818565304539
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Effective stroke triage in emergency settings often relies on clinicians' ability to identify subtle abnormalities in facial muscle coordination. While recent AI models have shown promise in detecting such patterns from patient facial videos, their reliance on real patient data raises significant ethical and privacy challenges -- especially when training robust and generalizable models across institutions. To address these concerns, we propose SafeTriage, a novel method designed to de-identify patient facial videos while preserving essential motion cues crucial for stroke diagnosis. SafeTriage leverages a pretrained video motion transfer (VMT) model to map the motion characteristics of real patient faces onto synthetic identities. This approach retains diagnostically relevant facial dynamics without revealing the patients' identities. To mitigate the distribution shift between normal population pre-training videos and patient population test videos, we introduce a conditional generative model for visual prompt tuning, which adapts the input space of the VMT model to ensure accurate motion transfer without needing to fine-tune the VMT model backbone. Comprehensive evaluation, including quantitative metrics and clinical expert assessments, demonstrates that SafeTriage-produced synthetic videos effectively preserve stroke-relevant facial patterns, enabling reliable AI-based triage. Our evaluations also show that SafeTriage provides robust privacy protection while maintaining diagnostic accuracy, offering a secure and ethically sound foundation for data sharing and AI-driven clinical analysis in neurological disorders.

Related papers

• Towards Privacy-aware Mental Health AI Models: Advances, Challenges, and Opportunities [61.633126163190724]
  Mental illness is a widespread and debilitating condition with substantial societal and personal costs.<n>Recent advances in Artificial Intelligence (AI) hold great potential for recognizing and addressing conditions such as depression, anxiety disorder, bipolar disorder, schizophrenia, and post-traumatic stress disorder.<n>Privacy concerns, including the risk of sensitive data leakage from datasets and trained models, remain a critical barrier to deploying these AI systems in real-world clinical settings.
  arXiv  Detail & Related papers  (2025-02-01T15:10:02Z)
• FedDP: Privacy-preserving method based on federated learning for histopathology image segmentation [2.864354559973703]
  This paper addresses the dispersed nature and privacy sensitivity of medical image data by employing a federated learning framework. The proposed method, FedDP, minimally impacts model accuracy while effectively safeguarding the privacy of cancer pathology image data.
  arXiv  Detail & Related papers  (2024-11-07T08:02:58Z)
• Which Client is Reliable?: A Reliable and Personalized Prompt-based Federated Learning for Medical Image Question Answering [51.26412822853409]
  We present a novel personalized federated learning (pFL) method for medical visual question answering (VQA) models. Our method introduces learnable prompts into a Transformer architecture to efficiently train it on diverse medical datasets without massive computational costs.
  arXiv  Detail & Related papers  (2024-10-23T00:31:17Z)
• Towards Synthetic Data Generation for Improved Pain Recognition in Videos under Patient Constraints [11.515273901289472]
  This study introduces a novel approach that leverages synthetic data to enhance video-based pain recognition models. We present a pipeline that synthesizes realistic 3D facial models by capturing nuanced facial movements from a small participant pool. This process generates 8,600 synthetic faces, accurately reflecting genuine pain expressions from varied angles and perspectives.
  arXiv  Detail & Related papers  (2024-09-24T18:33:57Z)
• CFCPalsy: Facial Image Synthesis with Cross-Fusion Cycle Diffusion Model for Facial Paralysis Individuals [6.9859169233703104]
  This study aims to synthesize a high-quality facial paralysis dataset to address this gap.<n>A novel Cross-Fusion Cycle Palsy Expression Generative Model (PalsyCFC) based on the diffusion model is proposed.<n>We have qualitatively and quantitatively evaluated the proposed method on the commonly used public clinical datasets of facial paralysis.
  arXiv  Detail & Related papers  (2024-09-11T13:46:35Z)
• Remembering Everything Makes You Vulnerable: A Limelight on Machine Unlearning for Personalized Healthcare Sector [0.873811641236639]
  This thesis aims to address the vulnerability of personalized healthcare models, particularly in the context of ECG monitoring. We propose an approach termed "Machine Unlearning" to mitigate the impact of exposed data points on machine learning models.
  arXiv  Detail & Related papers  (2024-07-05T15:38:36Z)
• OpticalDR: A Deep Optical Imaging Model for Privacy-Protective Depression Recognition [66.91236298878383]
  Depression Recognition (DR) poses a considerable challenge, especially in the context of privacy concerns. We design a new imaging system to erase the identity information of captured facial images while retain disease-relevant features. It is irreversible for identity information recovery while preserving essential disease-related characteristics necessary for accurate DR.
  arXiv  Detail & Related papers  (2024-02-29T01:20:29Z)
• Diff-Privacy: Diffusion-based Face Privacy Protection [58.1021066224765]
  In this paper, we propose a novel face privacy protection method based on diffusion models, dubbed Diff-Privacy. Specifically, we train our proposed multi-scale image inversion module (MSI) to obtain a set of SDM format conditional embeddings of the original image. Based on the conditional embeddings, we design corresponding embedding scheduling strategies and construct different energy functions during the denoising process to achieve anonymization and visual identity information hiding.
  arXiv  Detail & Related papers  (2023-09-11T09:26:07Z)
• Private, fair and accurate: Training large-scale, privacy-preserving AI models in medical imaging [47.99192239793597]
  We evaluated the effect of privacy-preserving training of AI models regarding accuracy and fairness compared to non-private training. Our study shows that -- under the challenging realistic circumstances of a real-life clinical dataset -- the privacy-preserving training of diagnostic deep learning models is possible with excellent diagnostic accuracy and fairness.
  arXiv  Detail & Related papers  (2023-02-03T09:49:13Z)
• One-shot action recognition towards novel assistive therapies [63.23654147345168]
  This work is motivated by the automated analysis of medical therapies that involve action imitation games. The presented approach incorporates a pre-processing step that standardizes heterogeneous motion data conditions. We evaluate the approach on a real use-case of automated video analysis for therapy support with autistic people.
  arXiv  Detail & Related papers  (2021-02-17T19:41:37Z)
• Hide-and-Seek Privacy Challenge [88.49671206936259]
  The NeurIPS 2020 Hide-and-Seek Privacy Challenge is a novel two-tracked competition to accelerate progress in tackling both problems. In our head-to-head format, participants in the synthetic data generation track (i.e. "hiders") and the patient re-identification track (i.e. "seekers") are directly pitted against each other by way of a new, high-quality intensive care time-series dataset.
  arXiv  Detail & Related papers  (2020-07-23T15:50:59Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.