QOCA®-Image Medical Platform - Smart VCF Risk Management System

Taipei Medical University

Status: Unknown

Conditions: Compression Fracture

Treatments: Other: Computer Assisted Detection Software For Vertebral Fractures

Study type: Observational

Funder types: Other

Identifiers

NCT04384211

N201909056

Details and patient eligibility

About

This project aims to develop and validate an automatic detection and classification system for vertebral compression fractures on computer tomography (CT) images using an artificial intelligence (AI) system (named Smart Bone) by Quanta.

Full description

A computer search of CT scans (2010.01.01-2018.09.30) was performed in Wan Fang Hospital. Those CT images that were retrospectively reviewed by experienced radiologists. The CT scans of 1000-1500 subjects aged 50 and above with and without thoracic or lumbar compression fractures were included in this project for machine learning and deep learning. The control group included those without compression fractures while the patient group were those with compression fractures. Subjects that did not meet the inclusion criteria were excluded.

The cortical layer of the T12-L5 spine images were manually labelled with the labeling software by the the technologists and confirmed the correctness of the image by an experienced radiologist. All the de-linked and completed images were provided to Quanta Computer Inc. for subsequent classification and analysis of AI machines for deep learning to facilitate the development of a system for automatic detection of pressure fractures by CT. This newly developed automatic system will be of valuable clinical impact in assisting radiologists to detect and classify vertebral compression fractures precisely and accurately.

Enrollment: 1,500 estimated patients

Sex: All

Ages: 50 to 90 years old

Volunteers: Accepts Healthy Volunteers

Inclusion criteria

• Cases with CT examinations acquired between 2010.01-2018.09
• Cases with CT examinations performed with one of the following protocol: whole body, abdomen, and spine
• Cases must be >/= 50 years of age
• Cases with reports from CT examinations ditched as positive or negative compression fractures within a search range from T12 to L5 vertebrae.
• CT images with raw data that are allowed to be reconstructed in axial view with a slice thickness of 1.3 mm
• CT images with raw data that are allowed to be reconstructed in sagittal view with a slice thickness of 2.5 mm

Exclusion criteria

• CT images with imaging artifacts, foreign bodies, or implants
• Cases with comorbid conditions, such as infection, cancer metastasis, chronic osteomyelitis, or other nonosteoporotic compression fracture

Trial design

1,500 participants in 2 patient groups

Radiologists

Description:

A computer search of CT scans (2010.01.01-2018.09.30) was performed in Wan Fang Hospital. These CT images were retrospectively reviewed by an experienced radiologist who classified and marked with annotations of vertebral fractures by the Genant's semiquantitative method.

Smart Bone

Description:

The same CT images were separately reviewed and processed by the artificial intelligence system (Smart Bone) by Quanta for compression fractures. The two results, one by the radiologists and the other by artificial intelligence system, will be compared to statistically quantify equivalence (CADe).

Treatment:

Other: Computer Assisted Detection Software For Vertebral Fractures