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Development and clinical application of a multi-modal system for sarcopenia diagnosis based on deep learning

Development and cohort study of a deep learn-based multimodal system for sarcopenia diagnosis

Status
Active, not recruiting
Phases
Unknown
Study type
Observational
Source
ChiCTR
Registry ID
ChiCTR2100049705
Enrollment
Unknown
Registered
2021-08-08
Start date
2021-09-01
Completion date
Unknown
Last updated
2022-04-19

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Sarcopenia

Interventions

case series:No

Sponsors

Beijing Jishuitan Hospital
Lead Sponsor

Eligibility

Sex/Gender
All

Inclusion criteria

Inclusion criteria: The inclusion criteria for scans were patients underlying abdomen or lumbar CT, and the date of servicing for examination was between September 2021 and March 2022.

Exclusion criteria

Exclusion criteria: Patient fractures, motion and sporadic artifacts, hardware, surgery, mass, scoliosis and kyphosis.

Design outcomes

Primary

MeasureTime frame
muscle area;CT attenuation value;muscle fat infiltration (MFI);proton density fat fraction (PDFF) of the muscle;—

Countries

China

Contacts

Public ContactYao Ning

Beijing Jishuitan Hospital

yao_ning_happy@126.com+86 18511912025

Outcome results

None listed

Source: ChiCTR (via WHO ICTRP) · Data processed: Feb 4, 2026