Abstract
Sarcopenia is a progressive loss of muscle mass and function linked to poor surgical outcomes such as prolonged hospital stays, impaired mobility, and increased mortality. Although it can be assessed through cross-sectional imaging by measuring skeletal muscle area (SMA), the process is time-consuming and adds to clinical workloads, limiting timely detection and management; however, this process could become more efficient and scalable with the assistance of artificial intelligence applications. This paper presents high-quality three-dimensional cross-sectional computed tomography (CT) images of patients with sarcopenia collected at the Freeman Hospital, Newcastle upon Tyne Hospitals NHS Foundation Trust. Expert clinicians manually annotated the SMA at the third lumbar vertebra, generating precise segmentation masks. We develop deep-learning models to measure SMA in CT images and automate this task. Our methodology employed transfer learning and self-supervised learning approaches using labelled and unlabeled CT scan datasets. While we developed qualitative assessment models for detecting sarcopenia, we observed that the quantitative assessment of SMA is more precise and informative. This approach also mitigates the issue of class imbalance and limited data availability. Our model predicted the SMA, on average, with an error of +-3 percentage points against the manually measured SMA. The average dice similarity coefficient of the predicted masks was 93%. Our results, therefore, show a pathway to full automation of sarcopenia assessment and detection.
Abstract (translated)
肌少症是一种进行性的肌肉质量和功能丧失，与较差的手术结果相关，如住院时间延长、行动能力受损和死亡率增加。虽然可以通过测量骨骼肌面积（SMA）来利用横断面成像评估肌少症，但这一过程耗时且增加了临床工作量，从而限制了及时发现和管理；然而，在人工智能应用的帮助下，该流程可以变得更高效和可扩展。 本文介绍了由纽卡斯尔泰恩河畔弗里曼医院收集的患有肌少症患者高质量三维横断面CT图像。专家医生手动在第三腰椎水平标注了SMA，生成了精确的分割掩模。我们开发了一种深度学习模型来测量CT图像中的骨骼肌面积（SMA）并自动化这一任务。我们的方法采用了迁移学习和自监督学习方法，使用标记和未标记的CT扫描数据集。尽管我们为检测肌少症开发了定性评估模型，但我们发现定量评估SMA更为准确且信息量更大。这种方法还缓解了类不平衡和数据可用性有限的问题。 我们的模型预测的骨骼肌面积（SMA）平均误差与手动测量的结果相比约为正负3个百分点。预测掩模的平均Dice相似系数为93%。因此，我们的结果表明了一条实现肌少症评估和检测完全自动化的路径。
URL
https://arxiv.org/abs/2508.17275
