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"given_name": "Huizhi", "identifiers": [ { "identifier": "0000-0003-4408-4528", "scheme": "orcid" } ], "name": "Liang, Huizhi", "type": "personal" } }, { "affiliations": [ { "id": "01kj2bm70", "identifiers": [ { "identifier": "01kj2bm70", "scheme": "ror" }, { "identifier": "grid.1006.7", "scheme": "grid" }, { "identifier": "0000 0001 0462 7212", "scheme": "isni" } ], "name": "Newcastle University" } ], "person_or_org": { "family_name": "Ojha", "given_name": "Varun", "identifiers": [ { "identifier": "0000-0002-9256-1192", "scheme": "orcid" } ], "name": "Ojha, Varun", "type": "personal" } } ], "description": "

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.

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