Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Cross-cultural adaptation of evidence-based practice measure among Hong Kong healthcare providers.

PloS one·2026
Same author

Generalized Joint Hypermobility in Adolescent Idiopathic Scoliosis: Greater Curve Flexibility, Larger Thoracic Kyphosis, but Higher Complication Risk.

Orthopaedic surgery·2026
Same author

Targeting Grade-Specific Endoplasmic Reticulum Stress Vulnerabilities in Chondrosarcoma: Divergent Roles of Protein Kinase R-Like Endoplasmic Reticulum Kinase and Inositol-Requiring Enzyme 1α Signaling.

Cancer communications (London, England)·2026
Same author

Preoperative Fulcrum Flexibility >80% Is Associated With Clinical Success in Vertebral Body Tethering.

Global spine journal·2026
Same author

What's New in Spine Surgery.

The Journal of bone and joint surgery. American volume·2026
Same author

Distal Adding-On After Selective Thoracolumbar/Lumbar Fusion for Lenke 5C AIS: The Role of Proximal Coronal Construct Position and Postoperative UIV Translation.

Orthopaedic surgery·2026
Same journal

American Medical Association Shares Framework to Address the Escalating Risk of Physician Deepfakes.

Journal of medical Internet research·2026
Same journal

Online Social Interaction, Neighborhood Perception, and the Mediating Role of Social Capital in Charitable Giving for Seriously Ill Patients: Cross-Sectional Study.

Journal of medical Internet research·2026
Same journal

Evaluation of Large Language Models for Structured Data Extraction From Interstitial Lung Disease Clinical Notes: Comparative Study.

Journal of medical Internet research·2026
Same journal

Digital Interventions Targeting Parents to Improve Early Childhood Movement, Nutrition, and Sleep Behaviors: Systematic Review.

Journal of medical Internet research·2026
Same journal

Physical Activity Interventions Using Digital Health Interventions for Cancer-Related Fatigue in People With a History of Cancer: Scoping Review.

Journal of medical Internet research·2026
Same journal

Effectiveness of a Home-Based and Group-Based Tele-Exercise Program for Breast Cancer Survivors: Pilot Randomized Controlled Trial.

Journal of medical Internet research·2026
See all related articles

Related Experiment Video

Updated: Mar 22, 2026

A Spine Robotic-Assisted Navigation System for Pedicle Screw Placement
06:24

A Spine Robotic-Assisted Navigation System for Pedicle Screw Placement

Published on: May 11, 2020

9.5K

Scalable and Robust Artificial Intelligence for Spine Alignment Assessment: Multicenter Study Enabled by Real-Time

Guilin Chen1, Nan Meng2, Yipeng Zhuang2

  • 1Department of Orthopaedic Surgery, State Key Laboratory of Complex Severe and Rare Diseases, Beijing Key of Big Data Innovation and Application for Skeletal Health Medical Care, Key Laboratory of Big Data for Spinal Deformities, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, 100730, China.

Journal of Medical Internet Research
|March 20, 2026
PubMed
Summary
This summary is machine-generated.

A new data transformation method enhances AI accuracy for adolescent idiopathic scoliosis (AIS) spinal assessment across hospitals. This improves cobb angle prediction and disease grading, making AI more reliable in diverse clinical settings.

Keywords:
adolescent idiopathic scoliosisartificial intelligencedata heterogeneitydata transformationmulticentre validation

More Related Videos

Pedicle Screw Placement Using an Augmented Reality Head-Mounted Display in a Porcine Model
06:18

Pedicle Screw Placement Using an Augmented Reality Head-Mounted Display in a Porcine Model

Published on: May 24, 2024

2.9K
Author Spotlight: Advancing CBCT and Digital Dental Image Integration with AI-Assisted Digitization
05:49

Author Spotlight: Advancing CBCT and Digital Dental Image Integration with AI-Assisted Digitization

Published on: February 23, 2024

1.6K

Related Experiment Videos

Last Updated: Mar 22, 2026

A Spine Robotic-Assisted Navigation System for Pedicle Screw Placement
06:24

A Spine Robotic-Assisted Navigation System for Pedicle Screw Placement

Published on: May 11, 2020

9.5K
Pedicle Screw Placement Using an Augmented Reality Head-Mounted Display in a Porcine Model
06:18

Pedicle Screw Placement Using an Augmented Reality Head-Mounted Display in a Porcine Model

Published on: May 24, 2024

2.9K
Author Spotlight: Advancing CBCT and Digital Dental Image Integration with AI-Assisted Digitization
05:49

Author Spotlight: Advancing CBCT and Digital Dental Image Integration with AI-Assisted Digitization

Published on: February 23, 2024

1.6K

Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Spine Deformities

Background:

  • Artificial intelligence (AI) shows potential for automating spinal alignment assessment in adolescent idiopathic scoliosis (AIS).
  • AI model performance often decreases across multiple medical centers due to variations in imaging protocols and data.
  • This variability can compromise clinical diagnosis and treatment decisions for AIS.

Purpose of the Study:

  • To develop a real-time, plug-and-play data transformation method to improve AI model robustness against data heterogeneity in radiographs.
  • To enhance the performance of deep learning models for AIS assessment across multiple medical centers.

Main Methods:

  • A retrospective multicenter study included 3899 full-spine radiographs from 7 hospitals.
  • A novel pixel intensity-based data transformation method standardized image contrast and brightness.
  • The method was integrated into the SpineHRNet+ AI model for evaluating cobb angle (CA) prediction and severity classification accuracy and robustness.

Main Results:

  • The data transformation method significantly reduced contrast variability between datasets.
  • The enhanced SpineHRNet+ achieved consistent CA predictions across external datasets (mean error within 4°) with R² > 0.90.
  • Sensitivity and negative predictive value for disease severity grading improved to 90.18% and 93.16%, respectively.

Conclusions:

  • The data transformation approach effectively improved AI accuracy and robustness for multicenter AIS assessments.
  • The method's real-time processing and preservation of anatomical integrity enhance clinical practicality.
  • This enables scalable and reliable AI applications in diverse healthcare environments for AIS management.