Reliability of Rapid On‐Site Evaluation Achieved by Remote Sharing Systems (E‐ <scp>ROSE</scp> ) and <scp>AI</scp> Algorithms ( <scp>AI</scp> ‐ <scp>ROSE</scp> ) Compared With the Gold Standard in the Diagnosis of Lung Cancer
The study suggests that the application of innovative methods such as E-ROSE and AI-ROSE could provide valuable support to interventional pulmonologists in the diagnostic process.
Authors
- Maria Pia Foschino Barbaro (ORCID: https://orcid.org/0000-0001-9939-2697)
- Donato Lacedonia (ORCID: https://orcid.org/0000-0003-0429-7967)
- M Pirrelli
- Giuseppe Antonio Palmiotti (ORCID: https://orcid.org/0000-0002-0932-8761)
- Antonio Notaristefano
- Giulia Scioscia (ORCID: https://orcid.org/0000-0002-2667-077X)
- Crescenzio Gallo (ORCID: https://orcid.org/0000-0002-3929-462X)
- Francesco Inglese (ORCID: https://orcid.org/0000-0001-6467-8820)
- G Marangi
- Giancarlo D’Alagni
- Maria Cristina Colanardi
- Pasquale Tondo (ORCID: https://orcid.org/0000-0002-1202-8567)
- Renato Giua
- Gianluca Libero Ciliberti
- Armando Leone
- Terence Campanino (ORCID: https://orcid.org/0009-0009-1707-6638)
- Grazia Napoli
- Ruggiero Torraco
- Leonardo Monteleone
Institutions
- University of Foggia (IT)
- Azienda Ospedaliera S.Giuseppe Moscati (IT)
- Ospedale SS. Annunziata (IT)
Publication Details
- Journal
- Respirology
- Published
- 2025-08-11
- DOI
- https://doi.org/10.1111/resp.70104
- Citations
- 2
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 2.72