Automated detection of chewing movements in videofluoroscopic swallowing studies using deep learning for landmark detection and motion analysis.

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Bibliographic Details
Title: Automated detection of chewing movements in videofluoroscopic swallowing studies using deep learning for landmark detection and motion analysis.
Authors: Bandini A; Health Science Interdisciplinary Research Center, Scuola Superiore, Sant'Anna, Pisa, Italy; The Biorobotics Institute & the Department of Excellence for Robotics and AI, Scuola Superiore Sant'Anna, Pisa, Italy; KITE Research Institute, University Health Network, Toronto, ON, Canada. Electronic address: andrea.bandini@ieee.org., Lasala A; The Biorobotics Institute & the Department of Excellence for Robotics and AI, Scuola Superiore Sant'Anna, Pisa, Italy., Peladeau-Pigeon M; KITE Research Institute, University Health Network, Toronto, ON, Canada., Dharmarathna I; KITE Research Institute, University Health Network, Toronto, ON, Canada., Gandhi P; KITE Research Institute, University Health Network, Toronto, ON, Canada; Communication Sciences & Disorders, University of Alberta, Edmonton, AB, Canada., Meng D; KITE Research Institute, University Health Network, Toronto, ON, Canada., Milne H; KITE Research Institute, University Health Network, Toronto, ON, Canada., Panes V; KITE Research Institute, University Health Network, Toronto, ON, Canada., Simmons MM; KITE Research Institute, University Health Network, Toronto, ON, Canada., Steele CM; KITE Research Institute, University Health Network, Toronto, ON, Canada; Rehabilitation Sciences Institute, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada.
Source: Computers in biology and medicine [Comput Biol Med] 2026 Jan 01; Vol. 200, pp. 111361. Date of Electronic Publication: 2025 Dec 01.
Publication Type: Journal Article
Journal Info: Publisher: Elsevier Country of Publication: United States NLM ID: 1250250 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1879-0534 (Electronic) Linking ISSN: 00104825 NLM ISO Abbreviation: Comput Biol Med Subsets: MEDLINE
Database: MEDLINE Ultimate
Description
ISSN:1879-0534
DOI:10.1016/j.compbiomed.2025.111361