AI-Assisted 3D Reconstruction for Lung Resection Planning

September 23, 2026 updated by: Giacomo Argento, S. Andrea Hospital

Prospective Validation of an Artificial Intelligence-Based 3D Reconstruction Model for Preoperative Planning of Lung Resection.

This prospective observational study aims to validate an artificial intelligence (AI)-based system for automated three-dimensional (3D) reconstruction of pulmonary anatomy from preoperative chest computed tomography (CT) scans in patients undergoing lung resection.

The AI system will automatically identify and reconstruct relevant pulmonary anatomical structures, including pulmonary arteries, veins, bronchi, lobes, and segments. AI-generated 3D reconstructions will be compared with expert manual 3D reconstructions and with anatomical findings documented during surgery.

The primary objective is to assess the anatomical accuracy of AI-generated 3D reconstructions in identifying surgically relevant bronchovascular anatomy and anatomical variants. Secondary objectives include evaluating accuracy for individual anatomical structures, discrepancies between automated and manual reconstructions, concordance with intraoperative findings, and the time required to generate and review the automated reconstruction.

The study is observational and will not modify the planned surgical procedure or standard patient care.

Study Overview

Status

Not yet recruiting

Conditions

Intervention / Treatment

Detailed Description

Patients scheduled for anatomical lung resection who meet the study eligibility criteria will be prospectively enrolled. Preoperative contrast-enhanced chest CT images acquired as part of routine clinical care will be used for 3D reconstruction.

For each patient, an AI-based segmentation system will generate a 3D reconstruction of the relevant pulmonary anatomy, including pulmonary arterial, venous and bronchial structures, as well as lobar and segmental anatomy when applicable. The automated reconstruction will be reviewed without modifying the planned surgical treatment.

AI-generated reconstructions will be compared with expert manual 3D reconstructions and with anatomical findings documented intraoperatively. Particular attention will be given to the identification of surgically relevant bronchovascular branches and anatomical variants.

The primary analysis will assess the proportion of patients in whom the AI-generated reconstruction accurately represents all predefined surgically relevant anatomical structures for the planned resection, without clinically relevant omissions or incorrect structures.

Secondary analyses will assess performance at the individual anatomical-structure level, including arterial, venous and bronchial structures; characterize errors and discrepancies between automated and manual reconstruction; evaluate concordance with intraoperative anatomy; and assess the time required for automated reconstruction and expert review.

The study is a prospective, single-center observational validation study. All diagnostic and therapeutic decisions, including the indication and type of lung resection, will be made according to standard clinical practice independently of study participation.

Study Type

Observational

Enrollment (Estimated)

50

Contacts and Locations

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

Study Locations

    • Lazio
      • Rome, Lazio, Italy, 00189

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

18 years and older (Adult, Older Adult)

Accepts Healthy Volunteers

No

Genders Eligible for Study

All

Sampling Method

Non-Probability Sample

Study Population

Adult patients undergoing anatomical lung resection at the Thoracic Surgery Unit of Sant'Andrea University Hospital who have a preoperative contrast-enhanced chest CT scan suitable for AI-based 3D reconstruction.

Description

Inclusion Criteria:

  • Age ≥18 years
  • Scheduled for anatomical lung resection (segmentectomy, lobectomy, bilobectomy, or pneumonectomy)
  • Availability of a preoperative contrast-enhanced chest CT scan suitable for 3D reconstruction, with slice thickness ≤1.5 mm.
  • Ability to provide informed consent.

Exclusion Criteria:

  • Preoperative CT images of insufficient quality or technical characteristics for 3D reconstruction.
  • Cancellation of the planned surgical procedure before surgery.
  • Inability or unwillingness to provide 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

  • Observational Models: COHORT
  • Time Perspectives: PROSPECTIVE

Cohorts and Interventions

Group / Cohort
Intervention / Treatment
Prospective Validation Cohort

Patients undergoing lung resection who are prospectively enrolled for validation of the AI-based 3D reconstruction system. Preoperative CT images will be processed by the AI system, and the resulting 3D reconstructions will be compared with expert manual reconstruction and intraoperative anatomical findings.

Preoperative contrast-enhanced chest CT images will be processed using an artificial intelligence-based segmentation system to automatically generate three-dimensional reconstructions of pulmonary anatomy, including pulmonary arteries, veins, bronchi, lobes, and segments when applicable. The AI-generated reconstructions will be evaluated against expert manual 3D reconstruction and intraoperative anatomical findings. The AI output will not determine or modify the planned surgical treatment.

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Patient-Level Anatomical Accuracy of AI-Generated 3D Reconstruction
Time Frame: From preoperative CT imaging through completion of the surgical procedure

Proportion of patients in whom the AI-generated 3D reconstruction correctly represents all predefined surgically relevant pulmonary arterial, venous, and bronchial structures and anatomical variants for the planned lung resection, without clinically relevant omissions or incorrectly represented structures. Accuracy will be assessed by comparison with expert manual 3D reconstruction and intraoperative anatomical findings.

From preoperative CT imaging through completion of the surgical procedure

Collaborators and Investigators

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

Sponsor

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 (Estimated)

November 2, 2026

Primary Completion (Estimated)

May 1, 2027

Study Completion (Estimated)

August 1, 2027

Study Registration Dates

First Submitted

September 23, 2026

First Submitted That Met QC Criteria

September 23, 2026

First Posted (Actual)

October 1, 2026

Study Record Updates

Last Update Posted (Actual)

October 1, 2026

Last Update Submitted That Met QC Criteria

September 23, 2026

Last Verified

September 1, 2026

More Information

Terms related to this study

Other Study ID Numbers

  • SANTANDREA-3D-LUNG-01

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.