Proximal Femur Image Database Validation

NCT06351943 | Active Not Recruiting

• 1 other identifier: ImageDB pilot
• Study Type: observational
• Target: 2,932
• Locations: 1 country
• Sites: 1

Brief Summary

The AO@AI Turin project is a collaborative project with a Turin group and the AO (Arbeitsgemeinschaft für Osteosynthesefragen, or in English, Association for the Study of Internal Fixation) foundation. An Image database (DB) has been built to host AP pelvic radiographs ready for artificial intelligence (AI) development. The goal of this project is to determine the agreement between the Turin annotation of fracture status and the annotation from an external group of AO expert surgeons for a random subset of the Turin images.

Conditions

Proximal Femoral Fracture

Keywords

Artificial Intelligence; Hip Fractures; Radiographic Image Interpretation, Computer-Assisted; Reference Standards

Outcome Measures

Primary Outcomes (6)

• Annotations of fracture status of the image

  Fracture status (fracture vs no fracture) classification

  Day 0/Baseline

• In case of fracture, Arbeitsgemeinschaft für Osteosynthesefragen (AO, in English, Association for the Study of Internal Fixation)/Orthopedic Trauma Association (OTA) classification: Type

  AO/OTA classification: Type: 31A/31B/31C

  Day 0/Baseline

• In case of fracture, AO/OTA classification: Group

  AO/OTA classification: Group: A1/A2/A3, B1/B2//B3, C1/C2

  Day 0/Baseline

• In case of fracture, AO/OTA classification: Subgroup

  AO/OTA classification: Subgroup: A1.1/A1.2/A1.3/A2.2/A2.3/A3.1/A3.2/A3.3/B1.1/B1.2/B1.3/B2.1/B2.2/B2.3/C1.1/C1.2/C1.3/C2.1/C2.2/C2.3

  Day 0/Baseline

• In case of fracture, AO/OTA classification: Qualifier for 31A1.1

  AO/OTA classification: Qualifier for 31A1.1: n/o

  Day 0/Baseline

• In case of fracture, AO/OTA classification: Qualifier for 31B2

  AO/OTA classification: Qualifier for 31B2: p/q/r

  Day 0/Baseline

Interventions

Fracture classification annotations provided by the Turin group DIAGNOSTIC_TEST

Fracture classification annotations provided by the Turin group: fracture vs non-fracture, and, if fracture, the Arbeitsgemeinschaft für Osteosynthesefragen (AO, in English, Association for the Study of Internal Fixation)/Orthopedic Trauma Association (OTA) classification.

Fracture classification annotations provided by the AO expert surgeon group DIAGNOSTIC_TEST

Fracture classification annotations provided by the AO expert surgeon group: fracture vs non-fracture, and, if fracture, the AO/OTA classification.

Eligibility Criteria

• Sex: all
• Healthy Volunteers: No
• Age Groups: Child (0-17), Adult (18-64), Older Adult (65+)
• Sampling Method: Probability Sample

Study Population

Anonymized anteroposterior x-ray images in the Image database (DB). No patients will be enrolled for purposes of this study. The selection of 300 images for the pilot validation is random; therefore, the sampling method indicated below refers to this process of random selection of images from the Image DB.

You may qualify if:

• Not applicable.
• The study utilizes the anonymized images in the Image database (DB). No patients will be enrolled for purposes of this study.

You may not qualify if:

• Not applicable.

Sponsors & Collaborators

• AO Innovation Translation Center: lead
• University of Turin, Italy: collaborator

Study Sites (1)

AO Foundation

Dübendorf, 8600, Switzerland

Location

Related Publications (4)

• Tanzi L, Vezzetti E, Moreno R, Aprato A, Audisio A, Masse A. Hierarchical fracture classification of proximal femur X-Ray images using a multistage Deep Learning approach. Eur J Radiol. 2020 Dec;133:109373. doi: 10.1016/j.ejrad.2020.109373. Epub 2020 Oct 23.

  PMID: 33126175 RESULT

• Audige L, Bhandari M, Hanson B, Kellam J. A concept for the validation of fracture classifications. J Orthop Trauma. 2005 Jul;19(6):401-6. doi: 10.1097/01.bot.0000155310.04886.37.

  PMID: 16003200 RESULT

• Langerhuizen DWG, Janssen SJ, Mallee WH, van den Bekerom MPJ, Ring D, Kerkhoffs GMMJ, Jaarsma RL, Doornberg JN. What Are the Applications and Limitations of Artificial Intelligence for Fracture Detection and Classification in Orthopaedic Trauma Imaging? A Systematic Review. Clin Orthop Relat Res. 2019 Nov;477(11):2482-2491. doi: 10.1097/CORR.0000000000000848.

  PMID: 31283727 RESULT

• Meinberg EG, Agel J, Roberts CS, Karam MD, Kellam JF. Fracture and Dislocation Classification Compendium-2018. J Orthop Trauma. 2018 Jan;32 Suppl 1:S1-S170. doi: 10.1097/BOT.0000000000001063. No abstract available.

  PMID: 29256945 RESULT

MeSH Terms

Conditions

Proximal Femoral Fractures; Hip Fractures

Condition Hierarchy (Ancestors)

Femoral Neck Fractures; Femoral Fractures; Fractures, Bone; Wounds and Injuries; Hip Injuries; Leg Injuries

Study Officials

• Alessandro Aprato, MD

  University of Turin, Italy

  PRINCIPAL INVESTIGATOR

Study Design

• Study Type: observational
• Observational Model: OTHER
• Time Perspective: CROSS SECTIONAL
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: March 26, 2024
• First Posted: April 8, 2024
• Study Start: May 1, 2021
• Primary Completion: October 1, 2024
• Study Completion: June 30, 2025
• Last Updated: April 8, 2024

Record last verified: 2024-03

Locations

CH (1)