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

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

Get a concise snapshot of the trial, including recruitment status, study phase, enrollment targets, and key timeline milestones.

Recruitment Status

UNKNOWN

Total Enrollment

1500 participants

Study Classification

OBSERVATIONAL

Study Start Date

2019-06-01

Study Completion Date

2021-05-31

Brief Summary

Review the sponsor-provided synopsis that highlights what the study is about and why it is being conducted.

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.

Related Clinical Trials

Explore similar clinical trials based on study characteristics and research focus.

Low-dose Dual-energy CT for the Pre-surgical Planning of Patients With Progression of Scoliosis

NCT03483844

Scoliosis

COMPLETED

Posterior Ligament Complex Assessment Without Magnetic Resonance Image in Thoracolumbar Fractures

NCT02950532

Spinal Deformity Spinal Fractures Spinal Injuries +1 more

COMPLETED

Vertebral Augmentation With Kyphoplasty vs Nonsurgical Mgmt for Vertebral Body Compression Fractures

NCT01175278

Multiple Myeloma

WITHDRAWN NA

Vertebral Bone Quality and Prediction of Screw Loosening in Spine Fusion

NCT03650855

Spine Fusion for Degenerative Spine Disease

COMPLETED

Edge Computing Platform for Spine Health Risk Management Based on IoT Technology

NCT06092138

Cervical Disc Herniation Cervical Spinal Stenosis Ossification of Posterior Longitudinal Ligament +1 more

COMPLETED NA

Detailed Description

Dive into the extended narrative that explains the scientific background, objectives, and procedures in greater depth.

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.

Conditions

See the medical conditions and disease areas that this research is targeting or investigating.

Compression Fracture

Study Design

Understand how the trial is structured, including allocation methods, masking strategies, primary purpose, and other design elements.

Observational Model Type

COHORT

Study Time Perspective

RETROSPECTIVE

Study Groups

Review each arm or cohort in the study, along with the interventions and objectives associated with them.

Radiologists

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.

No interventions assigned to this group

Smart Bone

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).

Computer Assisted Detection Software For Vertebral Fractures

Intervention Type OTHER

A device named Smart Bone that is using CT image retrospectively acquired to entails a second review of CT images with Vertebral Compression Fractures through an interactive AI program developed by Quanta Computer Inc. was applied to the CT images. The device is a computer-aided detection (CADe) software application and is designed to assist radiologists to analyze Spine CT images. The device uses deep learning methods to perform vertebrae detection and classification of images.

Interventions

Learn about the drugs, procedures, or behavioral strategies being tested and how they are applied within this trial.

Computer Assisted Detection Software For Vertebral Fractures

A device named Smart Bone that is using CT image retrospectively acquired to entails a second review of CT images with Vertebral Compression Fractures through an interactive AI program developed by Quanta Computer Inc. was applied to the CT images. The device is a computer-aided detection (CADe) software application and is designed to assist radiologists to analyze Spine CT images. The device uses deep learning methods to perform vertebrae detection and classification of images.

Intervention Type OTHER

Other Intervention Names

Discover alternative or legacy names that may be used to describe the listed interventions across different sources.

Smart Bone

Eligibility Criteria

Check the participation requirements, including inclusion and exclusion rules, age limits, and whether healthy volunteers are accepted.

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

Minimum Eligible Age

50 Years

Maximum Eligible Age

90 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

Yes