Clinical trial NCT06351943: Proximal Femur Image Database Validation
Official title: Validation of the Fracture Classification Accuracy (Ground Truth) of Anteroposterior X-ray of the Proximal Femur According to the Arbeitsgemeinschaft für Osteosynthesefragen/Orthopedic Trauma Association Classification Done by a Single Center: A Pilot Validation Study
Study type: OBSERVATIONAL
Overall status: ACTIVE_NOT_RECRUITING
Conditions: Proximal Femoral Fracture
Keywords: Artificial Intelligence
Hip Fractures
Radiographic Image Interpretation, Computer-Assisted
Reference Standards
Lead sponsor: AO Innovation Translation Center
Sponsor class: OTHER
Collaborators: name: University of Turin, Italy
First posted: 2024-04-08
Enrollment: 2932
Sex: ALL
Standard ages: CHILD
ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: OTHER
Time perspective: CROSS_SECTIONAL
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.

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.

Detailed description: The AO@AI Turin project is a collaborative project with a Turin group who has collected 2,932 anteroposterior (AP) pelvic radiographs, of which 1,811 are fracture images, and 1,121 are non-fracture images. The Turin group has developed an artificial intelligence (AI) algorithm for fracture classification using these images. These anonymized images (with all metadata or personal identifiers removed) have been uploaded to a cloud-based image database (DB) hosted and managed by the AO Foundation.

The Turin group has established the "ground truth" using the methods of "consensus by experts". Two radiologists from their medical team have reviewed and classified the fracture status (fracture vs non-fracture, and, if fracture, the AO/Orthopedic Trauma Association \[OTA\] classification).

The next step's goal is the ground truth validation plan to test the accuracy of the Turin annotation of fracture classification of the already uploaded AP pelvic images. This is to ensure that the image DB offers accurate quality annotations to allow AI development.

For the pilot phase, a random subset of the Turin images (300 of images) will be drawn from the image DB. These images will be reviewed by an external group of AO expert surgeons who will annotate the images per their fracture status, i.e., fracture vs non-fracture, and, if fracture, the AO/OTA classification.

The group of AO expert surgeons consists of four surgeons who will independently review the 300 images and a fifth surgeon who serves as an adjudicator if necessary. The expert surgeons will be given access to the 300 images via the cloud-based image DB and annotate the images. The expert surgeons will be blinded to the Turin annotations. The expert surgeons' annotations will be entered into a DB built for the purpose for the pilot study.

To determine the ground truth, the annotations of the four surgeons will be compared, and discrepancies will be identified. A meeting will then be arranged among the surgeons to resolve, by consensus, the discrepancies, with the potential involvement of the fifth surgeon as the adjudicator. After the resolution meeting, there will be a single set of annotations for the 300 images from the exert surgeon group.

The Turin annotations will also be entered into the study DB to allow comparisons with the expert surgeon group's annotation.

In case of disagreement between the Turin annotation and the AO expert surgeon annotations, a consensus will be sought to establish a new ground truth. If this process results in significant revisions to the annotations, the entire dataset will be reviewed to set this new standard. Following such a comprehensive dataset revision, the algorithm for automated fracture classification of the proximal femur, which has already been developed by the Turin group, will be re-trained. After re-training, the algorithm's performance will be evaluated through metrics such as precision, recall, and F1-score to ensure its accuracy and effectiveness in classifying proximal femur fractures.

Eligibility criteria: Inclusion criteria

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

Exclusion criteria

• Not applicable.

Primary outcomes:
measure: Annotations of fracture status of the image
timeFrame: Day 0/Baseline
measure: In case of fracture, Arbeitsgemeinschaft für Osteosynthesefragen (AO, in English, Association for the Study of Internal Fixation)/Orthopedic Trauma Association (OTA) classification: Type
timeFrame: Day 0/Baseline
measure: In case of fracture, AO/OTA classification: Group
timeFrame: Day 0/Baseline
measure: In case of fracture, AO/OTA classification: Subgroup
timeFrame: Day 0/Baseline
measure: In case of fracture, AO/OTA classification: Qualifier for 31A1.1
timeFrame: Day 0/Baseline
measure: In case of fracture, AO/OTA classification: Qualifier for 31B2
timeFrame: Day 0/Baseline
Secondary outcomes:
Other outcomes:
Locations:
city: Dübendorf
country: Switzerland
facility: AO Foundation
Study officials:
affiliation: University of Turin, Italy
name: Alessandro Aprato, MD
role: PRINCIPAL_INVESTIGATOR
References:
citation: 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.
citation: 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.
citation: 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.
citation: 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.