View in EDS

An automatic radiomic-based approach for disease localization: A pilot study on COVID-19.

Saved in:
Bibliographic Details
Title: An automatic radiomic-based approach for disease localization: A pilot study on COVID-19.
Authors: Varriano, Giulia1 (AUTHOR) giulia.varriano@unimol.it, Nardone, Vittoria1 (AUTHOR) vittoria.nardone@unimol.it, Correra, Simona1 (AUTHOR) s.correra@studenti.unimol.it, Mercaldo, Francesco1 (AUTHOR) francesco.mercaldo@unimol.it, Santone, Antonella1 (AUTHOR) antonella.santone@unimol.it
Source: Computerized Medical Imaging & Graphics. Sep2024, Vol. 116, pN.PAG-N.PAG. 1p.
Subjects: False positive error, Medical specialties & specialists, Radiomics, Feature extraction, COVID-19
Abstract: Radiomics is an innovative field in Personalized Medicine to help medical specialists in diagnosis and prognosis. Mainly, the application of Radiomics to medical images requires the definition and delimitation of the Region Of Interest (ROI) on the medical image to extract radiomic features. The aim of this preliminary study is to define an approach that automatically detects the specific areas indicative of a particular disease and examines them to minimize diagnostic errors associated with false positives and false negatives. This approach aims to create a n x n grid on the DICOM image sequence and each cell in the matrix is associated with a region from which radiomic features can be extracted. The proposed procedure uses the Model Checking technique and produces as output the medical diagnosis of the patient, i.e., whether the patient under analysis is affected or not by a specific disease. Furthermore, the matrix-based method also localizes where appears the disease marks. To evaluate the performance of the proposed methodology, a case study on COVID-19 disease is used. Both results on disease identification and localization seem very promising. Furthermore, this proposed approach yields better results compared to methods based on the extraction of features using the whole image as a single ROI, as evidenced by improvements in Accuracy and especially Recall. Our approach supports the advancement of knowledge, interoperability and trust in the software tool, fostering collaboration among doctors, staff and Radiomics. • Not leaving out any image details, as all parts of the image are analyzed automatically. • Forgetting manual, time-consuming and resource-intensive segmentation. • Providing the medical specialist with aid in daily practice by suggesting the area with localized signs of disease. [ABSTRACT FROM AUTHOR]
Copyright of Computerized Medical Imaging & Graphics is the property of Pergamon Press - An Imprint of Elsevier Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
Description
Abstract:Radiomics is an innovative field in Personalized Medicine to help medical specialists in diagnosis and prognosis. Mainly, the application of Radiomics to medical images requires the definition and delimitation of the Region Of Interest (ROI) on the medical image to extract radiomic features. The aim of this preliminary study is to define an approach that automatically detects the specific areas indicative of a particular disease and examines them to minimize diagnostic errors associated with false positives and false negatives. This approach aims to create a n x n grid on the DICOM image sequence and each cell in the matrix is associated with a region from which radiomic features can be extracted. The proposed procedure uses the Model Checking technique and produces as output the medical diagnosis of the patient, i.e., whether the patient under analysis is affected or not by a specific disease. Furthermore, the matrix-based method also localizes where appears the disease marks. To evaluate the performance of the proposed methodology, a case study on COVID-19 disease is used. Both results on disease identification and localization seem very promising. Furthermore, this proposed approach yields better results compared to methods based on the extraction of features using the whole image as a single ROI, as evidenced by improvements in Accuracy and especially Recall. Our approach supports the advancement of knowledge, interoperability and trust in the software tool, fostering collaboration among doctors, staff and Radiomics. • Not leaving out any image details, as all parts of the image are analyzed automatically. • Forgetting manual, time-consuming and resource-intensive segmentation. • Providing the medical specialist with aid in daily practice by suggesting the area with localized signs of disease. [ABSTRACT FROM AUTHOR]
ISSN:08956111
DOI:10.1016/j.compmedimag.2024.102411