Imani Mahdi, Borda Miguel G, Vogrin Sara, Meijering Erik, Aarsland Dag, Duque Gustavo
Department of Medicine, Melbourne Medical School, University of Melbourne, St. Albans, Australia.
Centre for Age-Related Medicine (SESAM), Stavanger University Hospital, Stavanger, Norway.
JMIR Aging. 2025 Mar 19;8:e63686. doi: 10.2196/63686.
Sarcopenia (loss of muscle mass and strength) increases adverse outcomes risk and contributes to cognitive decline in older adults. Accurate methods to quantify muscle mass and predict adverse outcomes, particularly in older persons with dementia, are still lacking.
This study's main objective was to assess the feasibility of using deep learning techniques for segmentation and quantification of musculoskeletal tissues in magnetic resonance imaging (MRI) scans of the head in patients with neurocognitive disorders. This study aimed to pave the way for using automated techniques for opportunistic detection of sarcopenia in patients with neurocognitive disorder.
In a cross-sectional analysis of 53 participants, we used 7 U-Net-like deep learning models to segment 5 different tissues in head MRI images and used the Dice similarity coefficient and average symmetric surface distance as main assessment techniques to compare results. We also analyzed the relationship between BMI and muscle and fat volumes.
Our framework accurately quantified masseter and subcutaneous fat on the left and right sides of the head and tongue muscle (mean Dice similarity coefficient 92.4%). A significant correlation exists between the area and volume of tongue muscle, left masseter muscle, and BMI.
Our study demonstrates the successful application of a deep learning model to quantify muscle volumes in head MRI in patients with neurocognitive disorders. This is a promising first step toward clinically applicable artificial intelligence and deep learning methods for estimating masseter and tongue muscle and predicting adverse outcomes in this population.
肌肉减少症（肌肉质量和力量的丧失）会增加不良后果的风险，并导致老年人认知能力下降。目前仍缺乏准确量化肌肉质量和预测不良后果的方法，尤其是在患有痴呆症的老年人中。
本研究的主要目的是评估在神经认知障碍患者的头部磁共振成像（MRI）扫描中，使用深度学习技术对肌肉骨骼组织进行分割和量化的可行性。本研究旨在为使用自动化技术对神经认知障碍患者进行肌肉减少症的机会性检测铺平道路。
在对53名参与者的横断面分析中，我们使用了7个类似U-Net的深度学习模型对头MRI图像中的5种不同组织进行分割，并使用Dice相似系数和平均对称表面距离作为主要评估技术来比较结果。我们还分析了BMI与肌肉和脂肪体积之间的关系。
我们的框架准确地量化了头部左右两侧的咬肌、皮下脂肪和舌肌（平均Dice相似系数为92.4%）。舌肌、左侧咬肌的面积和体积与BMI之间存在显著相关性。
我们的研究证明了深度学习模型在量化神经认知障碍患者头部MRI中的肌肉体积方面的成功应用。这是朝着临床适用的人工智能和深度学习方法迈出的有希望的第一步，这些方法可用于估计咬肌和舌肌，并预测该人群的不良后果。
