Author

Constance de Monts

Stanford MedicineORCID 0009-0004-7659-8069Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Approach:deep learning· 2 Organism:human· 2 Assay:imaging· 2 Research Focus:cell type annotation· 1 Research Focus:clustering· 1 Research Focus:database / resource· 1 Research Focus:differential expression· 1 Assay:fluorescence / live-cell microscopy· 1

Frequent Co-Authors

Antoine Motte dit FalisseiD· 2 shared John DayiD· 2 shared Parker S. RuthiD· 2 shared Scott D. UhlrichiD· 2 shared Scott L. DelpiD· 2 shared Shelby Vogt-DomkeiD· 2 shared Sydney C. CovitziD· 2 shared Tina DuongiD· 2 shared

Activity by Year

2025-2026 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 45 mutual co-author links

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2 shared papersBioengineering

Antoine Motte dit FalisseORCID

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2 papers

Recent·Most cited

Deep learning models detect neuromuscular disease and quantify functional impairment from video-derived biomechanics data

Sydney C. Covitz, Parker S. Ruth, Shelby Vogt-Domke, Carmichael F. Ong, Tian Tan, Audrey Chun, sarah Ismail, Lin Karman, Antoine Motte dit Falisse, Shannon Li, Melina Rogers, Scott D. Uhlrich, John Day, Jennifer L. Hicks, Constance de Monts, , Scott L. Delp

This paper develops deep learning models to detect neuromuscular diseases and quantify movement impairment using video-derived biomechanics data from smartphone recordings.

bioRxiv · Oct 2, 2026 · PreprintBioengineering

Video-based biomechanical analysis captures disease-specific movement signatures of different neuromuscular diseases

Parker S. Ruth, Scott D. Uhlrich, Constance de Monts, Antoine Motte dit Falisse, Julie Muccini, Sydney C. Covitz, Shelby Vogt-Domke, John Day, Tina Duong, Scott L. Delp

Smartphone-video biomechanics (OpenCap) reproduced four timed function tests (r > 0.98) and separated facioscapulohumeral from myotonic dystrophy better than test times (balanced accuracy 82% vs 50%; 129 participants).

NEJM AI · Aug 28, 2025Peer-reviewedBioengineering✦ In-depth digest