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Automatic diagnosis of sarcopenia by muscle ultrasound images using convolutional neural network models

Automatic diagnosis of sarcopenia by muscle ultrasound images using convolutional neural network models

Status
Recruiting
Phases
Unknown
Study type
Observational
Source
ChiCTR
Registry ID
ChiCTR2300073651
Enrollment
Unknown
Registered
2023-07-18
Start date
2023-07-06
Completion date
Unknown
Last updated
2023-07-25

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

Conditions

Muscle disease

Interventions

Gold Standard:Reference criteria for the diagnosis of sarcopenia according to the consensus of Asian Working Group for Sarcopenia (AWGS): low muscle mass, low muscle strength and/or low physical perfo
Index test:Ultrasound image evaluation

Sponsors

Shanghai Tenth People's Hospital
Lead Sponsor

Eligibility

Sex/Gender
All
Age
60 Years to 90 Years

Inclusion criteria

Inclusion criteria: 1. Age =60 years old; 2. Patients with clinically suspected sarcopenia, for example, decreased physical function, weight loss, etc.

Exclusion criteria

Exclusion criteria: 1. Inability to complete muscle strength and physical function assessments; 2. Unwilling to participate in this study.

Design outcomes

Primary

MeasureTime frame
Muscle thickness on ultrasound;Muscle cross-sectional area on ultrasound;AUC;accuracy;sensitivity;specificity;positive predictive value;negative predictive value;—

Countries

China

Contacts

Public ContactLehang Guo

Shanghai Tenth People's Hospital

gopp1314@hotmail.com+86 137 6453 8305

Outcome results

None listed

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