Development of an Artificial Intelligence System for Hip Fracture Detection A YOLOv8 Model Performance Study for Junior Doctors

Status

Active, not recruiting

Phases

Unknown

Study type

Observational

Source

TCTR

Registry ID

TCTR20250214002

Enrollment

3000

Registered

2025-02-14

Start date

2017-01-01

Completion date

Unknown

Last updated

2026-08-03

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

Conditions

hip fracture artificial intelligence model Hip fractures, artificial intelligence, YOLOv8, radiographic diagnosis

Interventions

Images showing fractures in the intertrochanteric region of the femur,Images indicating fractures at the neck of the femur.,Images showing no signs of fractures

Diagnostic,Diagnostic,Diagnostic

intertrochanteric fracture,Neck Fracture,Normal

Eligibility

Sex/Gender

All

Age

30 Years to No maximum

Inclusion criteria

Inclusion criteria: 1. intertrochanteric fracture 2. neck fracture 3. normal 4. radiographic 2017-2023

Exclusion criteria

Exclusion criteria: 1. Osteoporosis 2. Hip osteoarthritis 3. Septic arthritis of hip 4. improper radiograph 5. previous hip surgery with implant

Design outcomes

Primary

Measure	Time frame
Diagnostic Accuracy within one year Mean Average Precision	—

Secondary

Measure	Time frame
diagnostic performance of AI model within one year Sensitivity , specificity , accuracy	—

Countries

Thailand

Contacts

Public Contactwithoone kittipichai

Samut sakhon hospital

withoone@gmail.com0815586102

Outcome results

None listed

Source: TCTR (via WHO ICTRP) · Data processed: Aug 10, 2026