Clinical trial NCT04384211: QOCA®-Image Medical Platform - Smart VCF Risk Management System
Official title: Smart Bone: An Intelligent Fracture Risk Capture and Management System
Study type: OBSERVATIONAL
Overall status: UNKNOWN
Conditions: Compression Fracture
Lead sponsor: Taipei Medical University WanFang Hospital
Sponsor class: OTHER
Collaborators: name: Quanta Computer Inc.
First posted: 2020-05-12
Enrollment: 1500
Sex: ALL
Minimum age (years): 50
Maximum age (years): 90
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: yes
Observational model: COHORT
Time perspective: RETROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: A total of 1000-1500 subjects were recruited in both sex, aged 50 and above, and who had come to Wan Fang Hospital for CT scans, including those with or without vertebral compression fractures as the compression fractures group and the control group respectively.

Brief summary: 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.

Detailed 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.

Eligibility criteria: 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

Primary outcomes:
measure: Concordance rate
timeFrame: 2019.06 to 2020.03
Secondary outcomes:
measure: Accuracy
timeFrame: 2019.06 to 2020.03
Other outcomes:
Locations:
city: Taipei
country: Taiwan
facility: Taipei Medical University WanFang Hospital
Study officials:
affiliation: Taipei Medical University WanFang Hospital
name: Wing P. Chan, M.D.
role: PRINCIPAL_INVESTIGATOR
References: