Integration of a Deep Learning-Based Module for the Quantification of Imaging Features into the Filling-in Process of the Radiological Structured Report

Camilla Scapicchio, Camilla Scapicchio, Elena Ballante, Elena Ballante, Francesca Brero, Raffaella Fiamma Cabini, Raffaella Fiamma Cabini, Andrea Chincarini, Maria Evelina Fantacci, Maria Evelina Fantacci, Silvia Figini, Silvia Figini, Alessandro Lascialfari, Francesca Lizzi, Ian Postuma, Alessandra Retico

2023

Abstract

The role of Computed Tomography (CT) in the characterization of COVID-19 pneumonia has been widely recognized. The aim of this work is to present the idea of integrating a Deep Learning (DL)-based software, able to automatically quantify qualitative information typically describing COVID-19 lesions on chest CT scans, into a structured report-filling pipeline. Different studies have highlighted the value of introducing the use of structured reports in clinical practice, as a reproducible instrument for diagnosis and follow-up rather than the commonly used free-text radiological report. Structured data are fundamental to helping clinical de- cision support systems and fostering precision medicine. We developed a Deep Learning based software that segments both the lungs and the lesions associated with COVID-19 pneumonia on chest CT scans and quan- tifies some indexes describing qualitative characteristics used to assess COVID-19 lesions clinically. Once assessed the robustness of the system by means of a multicenter clinical evaluation made by clinical experts, it can be used for the first stratification of patients, supporting radiologists with a computer-aided quantification, and the derived quantities, immediately intelligible for the clinicians, are suitable to be inserted in a structured report in COVID-19 pneumonia and then exploited as explainable features to build predictive models.

Paper Citation

in Harvard Style

Scapicchio C., Ballante E., Brero F., Cabini R., Chincarini A., Fantacci M., Figini S., Lascialfari A., Lizzi F., Postuma I. and Retico A. (2023). Integration of a Deep Learning-Based Module for the Quantification of Imaging Features into the Filling-in Process of the Radiological Structured Report. In Proceedings of the 16th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 5: CCH, ISBN 978-989-758-631-6, pages 663-670. DOI: 10.5220/0011921900003414

in Bibtex Style

@conference{cch23,
author={Camilla Scapicchio and Elena Ballante and Francesca Brero and Raffaella Fiamma Cabini and Andrea Chincarini and Maria Evelina Fantacci and Silvia Figini and Alessandro Lascialfari and Francesca Lizzi and Ian Postuma and Alessandra Retico},
title={Integration of a Deep Learning-Based Module for the Quantification of Imaging Features into the Filling-in Process of the Radiological Structured Report},
booktitle={Proceedings of the 16th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 5: CCH,},
year={2023},
pages={663-670},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011921900003414},
isbn={978-989-758-631-6},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 16th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 5: CCH,
TI - Integration of a Deep Learning-Based Module for the Quantification of Imaging Features into the Filling-in Process of the Radiological Structured Report
SN - 978-989-758-631-6
AU - Scapicchio C.
AU - Ballante E.
AU - Brero F.
AU - Cabini R.
AU - Chincarini A.
AU - Fantacci M.
AU - Figini S.
AU - Lascialfari A.
AU - Lizzi F.
AU - Postuma I.
AU - Retico A.
PY - 2023
SP - 663
EP - 670
DO - 10.5220/0011921900003414