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A novel lightweight convolutional neural network models for automatic diagnosis and category in pressure ulcer

Construction and empirical research of intelligent recognition database of pressure injury based on AI

Status
Recruiting
Phases
Unknown
Study type
Observational
Source
ChiCTR
Registry ID
ChiCTR2400094362
Enrollment
Unknown
Registered
2024-12-20
Start date
2024-04-15
Completion date
Unknown
Last updated
2025-01-06

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

Conditions

Pressure ulcer

Interventions

Gold Standard:The included images are assessed for pressure ulcer category by two specialized nurses holding international certification in wound and ostomy care. If there is a disagreement between th
Index test:Lightweight convolutional neural network was used to construct a small program for diagnosing stress injury, and to diagnose and stage stress injury

Sponsors

The First Hospital of Jiaxing
Lead Sponsor

Eligibility

Sex/Gender
All
Age
18 Years to 80 Years

Inclusion criteria

Inclusion criteria: (1) the age range of the subjects was >=18 years old; (2) the subjects were patients with stage 1-4 stress injury.

Exclusion criteria

Exclusion criteria: (1)patients with skin diseases such as systemic lupus erythematosus, psoriasis, burns, etc; (2) There is cognitive impairment or mental illness; (3) Patients suffering from severe organ failure or malignant tumors (4) patients terminating treatment midway due to various reasons (such as abandonment of treatment, death, etc.)

Design outcomes

Primary

MeasureTime frame
Diagnostic accuracy of pressure ulcer;Accurate efficiency of pressure ulcer;

Countries

China

Contacts

Public ContactZhihong Zhu

The First Hospital of Jiaxing

837624562@qq.com+86 137 5076 0025

Outcome results

None listed

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