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

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.

Study Overview

• Status: Completed
• Conditions: Scoliosis; Artificial Intelligence; Orthopedic Disorder of Spine
• Intervention / Treatment: Device: An artificial system for the screening of scoliosis

• Study Type: Interventional
• Enrollment (Actual): 500
• Phase: Not Applicable

Contacts and Locations

Study Locations

• China
  • Guangdong
    • Guangzhou, Guangdong, China, 510000
      • Zhongshan Ophthalmic Center, Sun Yat-sen University

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: 10 years to 22 years (ADULT, CHILD)
• Accepts Healthy Volunteers: Yes

Description

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).

Study Plan

How is the study designed?

Design Details

• Primary Purpose: DIAGNOSTIC
• Allocation: NA
• Interventional Model: SINGLE_GROUP
• Masking: NONE

Number of Arms

1

Arms and Interventions

Participant Group / Arm	Intervention / Treatment
OTHER: Eligible patients for AI test.; Device: An artificial system for the screening of scoliosis	Device: An artificial system for the screening of scoliosis; An artificial intelligence to make evaluation of scoliosis using back images

What is the study measuring?

Primary Outcome Measures

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

Collaborators and Investigators

• Sponsor: Sun Yat-sen University

Study record dates

Study Major Dates

• Study Start (ACTUAL): June 1, 2018
• Primary Completion (ACTUAL): July 30, 2018
• Study Completion (ACTUAL): July 30, 2018

Study Registration Dates

• First Submitted: December 10, 2018
• First Submitted That Met QC Criteria: December 10, 2018
• First Posted (ACTUAL): December 12, 2018

Study Record Updates

• Last Update Posted (ACTUAL): December 12, 2018
• Last Update Submitted That Met QC Criteria: December 10, 2018
• Last Verified: December 1, 2018

More Information

Terms related to this study

• Keywords: Orthopedic Disorder of Spine
• Additional Relevant MeSH Terms: Bone Diseases; Spinal Curvatures; Scoliosis; Musculoskeletal Diseases; Spinal Diseases
• Other Study ID Numbers: CCPMOH2018-China-13

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No