A Prospective Observational Study of Artificial Intelligence Morphometric Evaluation of Vertebral Fractures

April 2, 2026 updated by: Luigi Di Filippo, IRCCS San Raffaele
The study will be conducted as a monocentric observational prospective study design wants to evaluate the prevalence of vertebral fractures in the cohort of patients that perform a chest-abdomen CT for medical indication other than osteometabolic pathologies.

Study Overview

Detailed Description

This study aimed to evaluate the prevalence of vertebral fractures in a cohort of patients that perform a chest-abdomen CT for medical indication other than osteometabolic pathologies.It is estimated that 250 patients will be enrolled (Patients will be enrolled in retrospective and prospective way between 01/03/2025 and 28/02/2026. The presence of one or more vertebral fractures will be evaluated through the radiological medical assessment with automatic 3D reconstruction of the thoracic and lumbar spine and by application of the AI software NanoxAIHealthVCF NANO-X IMAGING LTD on abdomen-chest CT studies.

Clinical, anthropometric, and anamnestic data will be collected from patients undergoing CT assessments. These data will be collected on the day of the radiological examination.

There will be only one evaluation at the time of the CT scan. Only in case of fracture detection, via radiological medical assessment and/or via AI software, the patient will be subsequently evaluated in the Endocrinology Unit as for standard of care.

Study Type

Observational

Enrollment (Estimated)

250

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Contact

Study Contact Backup

Study Locations

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

  • Adult
  • Older Adult

Accepts Healthy Volunteers

N/A

Sampling Method

Non-Probability Sample

Study Population

Male patients, with an age ≥ 50 years old, that perform CT scan, without osteo-metabolic conditions.

Description

Inclusion Criteria:

  1. Male subjects
  2. Age ≥ 50 years
  3. Clinical and medical history data available at abdomen-chest CT evaluation
  4. Signature of informed consent to the study

Exclusion Criteria:

  1. Hospitalized patients
  2. Patients known to have osteo-metabolic diseases.
  3. with primary and/or acquired immunodeficiency states, and/or severe impairment of general clinical condition (e.g. metastatic neoplasms; immunosuppressive therapies; worsening/reacute/compensated chronic diseases; moderate-severe renal failure)
  4. Patients unable to understand and sign the Informed Consent.

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

Cohorts and Interventions

Group / Cohort
Intervention / Treatment
Patients undergoing to CT abdomen-chest

Patients undergoing to CT abdomen-chest study at the UO of Radiology of the IRCCS San Raffaele Hospital for clinical indications not related to osteo-metabolic pathology.

Patients will be evaluated for vertebral fractures both through the radiological medical evaluation with automatic 3D reconstruction of the thoracic and lumbar spine and through application of the AI software NanoxAIHealthVCF, NANO-X IMAGING LTD on abdomen-chest CT studies.

Radiological medical evaluation with automatic 3D reconstruction of the thoracic and lumbar spine and through application of the AI software NanoxAIHealthVCF, NANO-X IMAGING LTD on abdomen-chest CT studies.

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Number of fractures detected with CT scans trough AI software and 3D reconstruction
Time Frame: from 01/03/2025 to 28/02/2026
The UO of Endocrinology, in collaboration with the UO of Radiology of the IRCCS San Raffaele Hospital, proposes to assess the prevalence of vertebral fractures prospectively in the population afferent to our hospital to perform abdomen-chest CT studies through the radiological medical evaluation with automatic 3D reconstruction of the thoracic and lumbar spine and the application of the AI software cNanoxAIHealthVCF, NANO-X IMAGING LTD.
from 01/03/2025 to 28/02/2026

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Investigators

  • Principal Investigator: Andrea Giustina, MD, IRCCS Ospedale San Raffaele and Vita-Salute San Raffaele University

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Actual)

March 1, 2025

Primary Completion (Actual)

March 30, 2026

Study Completion (Estimated)

March 1, 2027

Study Registration Dates

First Submitted

June 4, 2024

First Submitted That Met QC Criteria

June 7, 2024

First Posted (Actual)

June 10, 2024

Study Record Updates

Last Update Posted (Actual)

April 8, 2026

Last Update Submitted That Met QC Criteria

April 2, 2026

Last Verified

April 1, 2026

More Information

Terms related to this study

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

NO

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

Subscribe