A Study Evaluating the Performance of Using AI Software and Chest X-rays to Screen for Osteoporosis as Compared to Standard DXA Scans in Patients 50 Years and Older

NCT07421713 | Recruiting

• 1 other identifier: CIP-OSTEO3-001
• Study Type: observational
• Target: 595
• Locations: 2 countries
• Sites: 3

Brief Summary

The goal of this study is to assess the performance of an artificial intelligence software (Osteo Signal) in detecting osteoporosis risk in adults 50 years and older. The main question it aims to answer is: What is the accuracy of the software in detecting osteoporosis risk on chest x-ray images as compared to the standard technique of dual-energy x-ray absorptiometry (DXA)? There is no direct involvement of participants in this study as it will use data from individuals who have already had a chest x-ray and a DXA scan taken in the past.

Conditions

Osteoporosis, Osteopenia

Keywords

Osteoporosis; Bone Diseases, Metabolic

Outcome Measures

Primary Outcomes (1)

• Two class bone status classification (osteoporosis/non osteoporosis)

  Device output against the lowest DXA as measured by sensitivity, specificity, and area under the ROC curve (AUC)

  At a time following the participant having had a chest x-ray and DXA scan within 6 months of each other

Secondary Outcomes (2)

• Three class bone status classification (osteoporosis/osteopenia/normal)

  At a time following the participant having had a chest x-ray and DXA scan within 6 months of each other

• Classification for osteoporosis and osteopenia

  At a time following the participant having had a chest x-ray and DXA scan within 6 months of each other

Interventions

Osteo Signal DEVICE

Artificial Intelligence (AI) software

Eligibility Criteria

• Age: 50 Years+
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

Patients selected hospitals in the Netherlands and UK that have previously had chest x-rays and DXA scans

You may qualify if:

• Age ≥50 years at the time of chest X-ray
• Availability of
• Posteroanterior (PA) Chest X-ray and
• DXA scan with a valid T-score measurement where both the DXA scan and Chest x-ray examinations were performed within 6 months (182 days) of each other and
• Available X-ray and DXA scans performed on or after 01-Jan-2010.
• Complete metadata (sex and age) available on PACS or hospital records.

You may not qualify if:

• Non-diagnostic Quality or Incomplete Imaging of:
• DXA scan inadequacy: Missing T-score from a technically inadequate scan.
• PA Chest X-ray technical inadequacy: Non-PA projection, significant motion artefacts, inadequate visualisation of thoracic structures, or missing DICOM metadata.
• Image format incompatibility: Non-DICOM format or corrupted image files that would prevent AI analysis
• PA Chest X-ray \> ±6 months from the date of the DXA scan.
• Previous diagnosis or treatment of Osteoporosis prior to the DXA scan used in the Study.
• diagnosis of osteoporosis (T-score ≤-2.5), or current/recent anti- osteoporotic medications, within 12 months prior to the DXA scan used in the study.
• Current or recent anti-osteoporotic medications for 3 months (91 days) continuously prior to the Chest x-ray used in the Study.
• Diagnosis of any metabolic bone disease prior to the DXA scan used in the study.
• Diagnosis of metabolic bone disease other than Osteoporosis or Osteopenia, up to a maximum of 3 months (91 days) after the DXA scan.
• Major Chest Wall or Spinal Deformities, including but not limited to:
• Severe structural deformities: Thoracic scoliosis with Cobb angle \>40°, severe kyphosis, or chest wall deformities (pectus excavatum/carinatum) affecting \>30% of the thoracic cage.
• Surgical hardware: Previous thoracic/spinal surgery with metallic implants obscuring anatomical landmarks required for AI analysis.
• Pathological conditions: Extensive pulmonary consolidation, pleural effusion, lesions or masses obscuring \>30% of thoracic skeletal structures
• Subject has opted out of confidential data being used for research purposes

Sponsors & Collaborators

• Promedius Inc: lead

Study Sites (3)

Erasmus University Medical Center Rotterdam,

Rotterdam, 3015 GD, Netherlands

NOT YET RECRUITING

Mid and South Essex NHS Foundation Trust

Westcliff-on-Sea, Essex, SS0 0RY, United Kingdom

NOT YET RECRUITING

Barts Health NHS Trust

London, E1 4DG, United Kingdom

RECRUITING

MeSH Terms

Conditions

Osteoporosis; Bone Diseases, Metabolic

Condition Hierarchy (Ancestors)

Bone Diseases; Musculoskeletal Diseases; Metabolic Diseases; Nutritional and Metabolic Diseases

Study Officials

• Jacob J Visser, Musculoskeletal Radiologist

  Erasmus Medical Center

  PRINCIPAL INVESTIGATOR

Central Study Contacts

Kim Bensalem

CONTACT

+447885369062; kim@medilife.net

Jung Jinho

CONTACT

+82 1083113509; jhjung@promedius.ai

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: RETROSPECTIVE
• Sponsor Type: INDUSTRY
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: February 12, 2026
• First Posted: February 19, 2026
• Study Start: February 10, 2026
• Primary Completion: March 1, 2026
• Study Completion: March 1, 2026
• Last Updated: February 19, 2026

Record last verified: 2026-02

Data Sharing

• IPD Sharing: Will not share

Locations

NL (1); GB (2)