Validation of the Utility of an Artificial System for the Large-scale Screening of Scoliosis

Validation of the Utility of an Artificial System for the Large-scale Screening of Scoliosis Using Back Images

Status

Completed

Phases

Study type

Interventional

Source

ClinicalTrials.gov

Registry ID

NCT03773458

Enrollment

500

Registered

2018-12-12

Start date

2018-06-01

Completion date

2018-07-30

Last updated

2018-12-12

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Artificial Intelligence, Orthopedic Disorder of Spine, Scoliosis

Keywords

Orthopedic Disorder of Spine

Brief summary

Traditional school scoliosis screening approaches remains debatable due to unnecessary referal and excessive cost. Deep learning algorithms have proven to be powerful tools for the detection of multiple diseases; however, the application of such methods in scoliosis screening requires further assessment and validation. Here, the investigators develop an artificial system for the automated screening of scoliosis using disrobed back images, and conduct clinical trial to validate if the diagnostic system can offsetting the shortcomings of human doctors.

Interventions

DEVICEAn artificial system for the screening of scoliosis

An artificial intelligence to make evaluation of scoliosis using back images

Study design

Allocation

Intervention model

SINGLE_GROUP

Primary purpose

DIAGNOSTIC

Masking

NONE

Eligibility

Age

10 Years to 22 Years

Healthy volunteers

Yes

Inclusion criteria

* 1.Patients included both pretreatment back photos and whole spine (C7-S1) standing X-ray or ultrasound images (for healthy population); 2. All the documents are clear to be recognized by naked eyes; 3. Back photos and are taken at the same time (not \>1month); 4.Patients were consider as idiopathic scoliosis according to clinical photos.

Exclusion criteria

* 1\. Patients were considered as non-idiopathic scoliosis for obvious abnormal features of trunck,such as Cafe-au-Lait spots for neurofibromatosis, Spider finger, Abnormal hair spot of back, pelvic tilt, lower limb discrepancy and so on; 2.The taken time between back photo and X-ray or ultrasound was more than 1month; 3.The clinical photos and images were not clear; 4. The X-ray film or ultrasound images not including whole spine (C7-S1).

Design outcomes

Primary

Measure	Time frame
The proportion of accurate, mistaken and miss detection of the intelligent visual acuity diagnostic system.	Up to 5 years

Countries

China

Outcome results

None listed

Source: ClinicalTrials.gov · Data processed: Feb 4, 2026