End to End AI System for Surgical Gesture Sequence Recognition and Clinical Outcome Prediction

Fine-grained analysis of intraoperative behavior and its impact on patient outcomes remain a longstanding challenge. We present Frame-to-Outcome (F2O), an end-to-end system that translates tissue dissection videos into gesture sequences and uncovers patterns associated with postoperative outcomes. Leveraging transformer-based spatial and temporal modeling and frame-wise classification, F2O robustly detects consecutive short (~2 seconds) gestures in the nerve-sparing step of robot-assisted radical prostatectomy (AUC: 0.80 frame-level; 0.81 video-level). F2O-derived features (gesture frequency, duration, and transitions) predicted postoperative outcomes with accuracy comparable to human annotations (0.79 vs. 0.75; overlapping 95% CI). Across 25 shared features, effect size directions were concordant with small differences (~ 0.07), and strong correlation (r = 0.96, p < 1e-14). F2O also captured key patterns linked to erectile function recovery, including prolonged tissue peeling and reduced energy use. By enabling automatic interpretable assessment, F2O establishes a foundation for data-driven surgical feedback and prospective clinical decision support.

Privacy Aware Question-Answering System for Online Mental Health Risk Assessment

Social media platforms have enabled individuals suffering from mental illnesses to share their lived experiences and find the online support necessary to cope. However, many users fail to receive genuine clinical support, thus exacerbating their symptoms. Screening users based on what they post online can aid providers in administering targeted healthcare and minimize false positives. Pre-trained Language Models (LMs) can assess users' social media data and classify them in terms of their mental health risk. We propose a Question-Answering (QA) approach to assess mental health risk using the Unified-QA model on two large mental health datasets. To protect user data, we extend Unified-QA by anonymizing the model training process using differential privacy. Our results demonstrate the effectiveness of modeling risk assessment as a QA task, specifically for mental health use cases. Furthermore, the model's performance decreases by less than 1% with the inclusion of differential privacy. The proposed system's performance is indicative of a promising research direction that will lead to the development of privacy-aware diagnostic systems.