AI-Assisted 3D Reconstruction for Lung Resection Planning
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.
Studieoversikt
Status
Intervensjon / Behandling
Detaljert beskrivelse
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.
Studietype
Registrering (Antatt)
Kontakter og plasseringer
Studiesteder
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Lazio
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Rome, Lazio, Italia, 00189
- Azienda Ospedaliera Universitaria Sant'Andrea
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Ta kontakt med:
- Giacomo Argento, MD
- Telefonnummer: +390633775773
- E-post: giargento@ospedalesantandrea.it
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Deltakelseskriterier
Kvalifikasjonskriterier
Alder som er kvalifisert for studier
Tar imot friske frivillige
Kjønn som er kvalifisert for studier
Prøvetakingsmetode
Studiepopulasjon
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.
Beskrivelse
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.
Studieplan
Hvordan er studiet utformet?
Designdetaljer
- Observasjonsmodeller: COHORT
- Tidsperspektiver: PROSPECTIVE
Kohorter og intervensjoner
Gruppe / Kohort |
Intervensjon / Behandling |
|---|---|
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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. |
Hva måler studien?
Primære resultatmål
Resultatmål |
Tiltaksbeskrivelse |
Tidsramme |
|---|---|---|
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Patient-Level Anatomical Accuracy of AI-Generated 3D Reconstruction
Tidsramme: From preoperative CT imaging through completion of the surgical procedure
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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
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Samarbeidspartnere og etterforskere
Sponsor
Studierekorddatoer
Studer hoveddatoer
Studiestart (Antatt)
Primær fullføring (Antatt)
Studiet fullført (Antatt)
Datoer for studieregistrering
Først innsendt
Først innsendt som oppfylte QC-kriteriene
Først lagt ut (Faktiske)
Oppdateringer av studieposter
Sist oppdatering lagt ut (Faktiske)
Siste oppdatering sendt inn som oppfylte QC-kriteriene
Sist bekreftet
Mer informasjon
Begreper knyttet til denne studien
Nøkkelord
Ytterligere relevante MeSH-vilkår
Andre studie-ID-numre
- SANTANDREA-3D-LUNG-01
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