Recent advances of AI in health: computational approaches for the screening and monitoring of autism spectrum disorder, dysexecutive syndromes, and cardiovascular health.

This presentation will focus on computational behavioral phenotyping, illustrating how machine learning (ML) methods can be leveraged to transform raw complex multimodal health data into objective and interpretable biomarkers. I first present computer vision and ML algorithms that (i) extract digital behavioral biomarkers from video and gamified visuomotor assessments and (ii) combine them for early autism screening. I then describe computational approaches for analyzing egocentric video to characterize the execution of a chocolate cake cooking task, by producing compact symbolic representations of behavioral trajectories that enable alignment and comparison across patients with sensorimotor and executive disorders. Finally, I present a methodology for learning compact and generalizable representations of wearable biosignals to improve the sensitivity to subtle cardiovascular health changes.

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[2] Perochon S., Di Martino J.M. et al. A tablet-based game for the assessment of visual motor skills in autistic children. npj Digit. Med. (2023).

[3] Perochon, S., Di Martino, J.M. et al. Early detection of autism using digital behavioral phenotyping. Nat Med. (2023).

[4] Perochon S., Oudre L. Recursive prototyping for computational behavior analysis from egocentric videos. In review at ICPR 2026

[5] Perochon, S. et al. Time-varying representations of longitudinal biosignals using self-supervised learning. NeurIPS 2024 Workshop: Self-Supervised Learning-Theory and Practice.