Experimental gerontology · 2026
Deep learning-derived magnetic resonance imaging masseter muscle volume is associated with oral impairment and cognitive frailty in older Japanese adults.
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Chen Liying, Thyreau Benjamin, Tatewaki Yasuko, Liu Yingxu, Takeda Ken, Nakase Taizen, Nakaji Shigeyuki, Mikami Tatsuya, Tamura Yoshihiro, Kobayashi Wataru, Murashita Koichi, Ninomiya Toshiharu, Taki Yasuyuki
Аннотация
BACKGROUND AND OBJECTIVES: The masseter muscle is associated with oral impairment and age-related functional decline, yet scalable and objective indicators remain limited in epidemiological studies. We developed a convolutional neural network (CNN) to segment the masseter muscle on T1-weighted magnetic resonance images (T1WI) and examined whether CNN-derived masseter muscle volume, as an imaging-based structural phenotype, is associated with oral impairment and cognitive frailty in older Japanese adults.
METHODS: A CNN was developed using an iterative human-in-the-loop procedure based on T1WI acquired at Tohoku University Hospital. The trained model was then applied to available 3D T1WI from the Hirosaki site of the Japan Prospective Studies Collaboration for Aging and Dementia, and 2077 participants (mean age, 69.9 ± 4.1 years) were included in the final analyses after image quality control and application of the study exclusion criteria. Associations among masseter volume, oral impairment, and cognitive frailty were analyzed.
RESULTS: Greater masseter volume was associated with a lower composite oral impairment score [odds ratio (OR) 0.67; 95% confidence interval: 0.60-0.74; p < 0.001]. Supplementary analyses showed the strongest association with structural abnormalities (OR 0.60), followed by periodontal inflammation (OR 0.81) and oral functional decline (OR 0.86). Masseter muscle volume showed an inverse association with cognitive frailty; however, this association was not significant after full adjustment. The pretrained CNN is available at https://github.com/bthyreau/masseter_mri.
CONCLUSIONS: CNN-derived masseter muscle parameters from magnetic resonance imaging were inversely correlated with oral impairment in older adults. This approach enables scalable and objective oral phenotyping from imaging data and highlights its potential application in large-scale epidemiological studies of aging and functional decline.
METHODS: A CNN was developed using an iterative human-in-the-loop procedure based on T1WI acquired at Tohoku University Hospital. The trained model was then applied to available 3D T1WI from the Hirosaki site of the Japan Prospective Studies Collaboration for Aging and Dementia, and 2077 participants (mean age, 69.9 ± 4.1 years) were included in the final analyses after image quality control and application of the study exclusion criteria. Associations among masseter volume, oral impairment, and cognitive frailty were analyzed.
RESULTS: Greater masseter volume was associated with a lower composite oral impairment score [odds ratio (OR) 0.67; 95% confidence interval: 0.60-0.74; p < 0.001]. Supplementary analyses showed the strongest association with structural abnormalities (OR 0.60), followed by periodontal inflammation (OR 0.81) and oral functional decline (OR 0.86). Masseter muscle volume showed an inverse association with cognitive frailty; however, this association was not significant after full adjustment. The pretrained CNN is available at https://github.com/bthyreau/masseter_mri.
CONCLUSIONS: CNN-derived masseter muscle parameters from magnetic resonance imaging were inversely correlated with oral impairment in older adults. This approach enables scalable and objective oral phenotyping from imaging data and highlights its potential application in large-scale epidemiological studies of aging and functional decline.