Artificial Intelligence and Scoliosis

NCT06589583 | Completed DMC

• 1 other identifier: F.P.T2407029
• Study Type: observational
• Target: 5,000
• Locations: 1 country
• Sites: 1

Brief Summary

The aim of the study to use artificial intelligence technology in assessment of scoliosis degree of severity and to personalize for treatment plan for each pa tient .

Conditions

Scoliosis Patients

Outcome Measures

Primary Outcomes (3)

• test the validity and reliability of software

  ability of the models to differentiate normal from scoliotic x-ray (automatic classification) which helps in diagnosis of scoliosis by identifying the curve .

  2 months

• determine degree of curve severity via measurement cobb's angle by degree

  for grading of scoliosis severity (mild or moderate or sever )

  2 months

• suggested treatment program according to each patient need .in form points

  in case of mild scoliosis the intervention will be conservative on other hand in moderate curve intervention will be conservatives and orthotic management while in sever suggested surgical interface.

  2 moths

Eligibility Criteria

• Age: 15 Years - 37 Years
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Child (0-17), Adult (18-64)
• Sampling Method: Non-Probability Sample

Study Population

All spinal X-ray involved in this study were retrospectively compiled from manifold sources, including BUU \&amp Datasets, Kaggle, Mendeley Data, Huggingface, Dropbox , and Roboflow, ensuring a diverse and comprehensive collection of scoliosis-related images .

You may qualify if:

• ). Patient with scoliosis were
• diagnosed with scoliosis for different etiology.
• Clear X- Ray for spine for all spinal curvatures including; cervical, thoracic and lumber(PA)view.
• C shaped and double C shape scoliosis such as (Thoracolumbar-cervicothoracic scoliosis).
• Mild, moderate and severe scoliotic degrees.
• Adolescents with mean age 17 years old
• Male and female gender

You may not qualify if:

• children with scoliosis
• age less than 12 years old .
• lateral view spine x- ray.

Sponsors & Collaborators

• Delta University for Science and Technology: lead

Study Sites (1)

Delta university

Gamasa, Dakahlia Governorate, 35511, Egypt

Location

Study Design

• Study Type: observational
• Observational Model: OTHER
• Time Perspective: RETROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Demonstrator of pediatrics and its surgery

Study Record Dates

• First Submitted: September 2, 2024
• First Posted: September 19, 2024
• Study Start: July 1, 2024
• Primary Completion: September 1, 2024
• Study Completion: September 1, 2024
• Last Updated: September 20, 2024

Record last verified: 2024-09

Locations

EG (1)