TY - JOUR
T1 - AIforCOVID: Predicting the clinical outcomes in patients with COVID-19 applying AI to chest-X-rays. An Italian multicentre study
T2 - Medical Image Analysis
AU - Soda, P.
AU - D'Amico, N.C.
AU - Tessadori, J.
AU - Valbusa, G.
AU - Guarrasi, V.
AU - Bortolotto, C.
AU - Akbar, M.U.
AU - Sicilia, R.
AU - Cordelli, E.
AU - Fazzini, D.
AU - Cellina, M.
AU - Oliva, G.
AU - Callea, G.
AU - Panella, S.
AU - Cariati, M.
AU - Cozzi, D.
AU - Miele, V.
AU - Stellato, E.
AU - Carrafiello, G.
AU - Castorani, G.
AU - Simeone, A.
AU - Preda, L.
AU - Iannello, G.
AU - Del Bue, A.
AU - Tedoldi, F.
AU - Alí, M.
AU - Sona, D.
AU - Papa, S.
N1 - Export Date: 13 September 2021
CODEN: MIAEC
Correspondence Address: Soda, P.; Unit of Computer Systems and Bioinformatics, Via Alvaro del Portillo 21, Italy; email: p.soda@unicampus.it
PY - 2021
Y1 - 2021
N2 - Recent epidemiological data report that worldwide more than 53 million people have been infected by SARS-CoV-2, resulting in 1.3 million deaths. The disease has been spreading very rapidly and few months after the identification of the first infected, shortage of hospital resources quickly became a problem. In this work we investigate whether artificial intelligence working with chest X-ray (CXR) scans and clinical data can be used as a possible tool for the early identification of patients at risk of severe outcome, like intensive care or death. Indeed, further to induce lower radiation dose than computed tomography (CT), CXR is a simpler and faster radiological technique, being also more widespread. In this respect, we present three approaches that use features extracted from CXR images, either handcrafted or automatically learnt by convolutional neuronal networks, which are then integrated with the clinical data. As a further contribution, this work introduces a repository that collects data from 820 patients enrolled in six Italian hospitals in spring 2020 during the first COVID-19 emergency. The dataset includes CXR images, several clinical attributes and clinical outcomes. Exhaustive evaluation shows promising performance both in 10-fold and leave-one-centre-out cross-validation, suggesting that clinical data and images have the potential to provide useful information for the management of patients and hospital resources.
AB - Recent epidemiological data report that worldwide more than 53 million people have been infected by SARS-CoV-2, resulting in 1.3 million deaths. The disease has been spreading very rapidly and few months after the identification of the first infected, shortage of hospital resources quickly became a problem. In this work we investigate whether artificial intelligence working with chest X-ray (CXR) scans and clinical data can be used as a possible tool for the early identification of patients at risk of severe outcome, like intensive care or death. Indeed, further to induce lower radiation dose than computed tomography (CT), CXR is a simpler and faster radiological technique, being also more widespread. In this respect, we present three approaches that use features extracted from CXR images, either handcrafted or automatically learnt by convolutional neuronal networks, which are then integrated with the clinical data. As a further contribution, this work introduces a repository that collects data from 820 patients enrolled in six Italian hospitals in spring 2020 during the first COVID-19 emergency. The dataset includes CXR images, several clinical attributes and clinical outcomes. Exhaustive evaluation shows promising performance both in 10-fold and leave-one-centre-out cross-validation, suggesting that clinical data and images have the potential to provide useful information for the management of patients and hospital resources.
U2 - 10.1016/j.media.2021.102216
DO - 10.1016/j.media.2021.102216
M3 - Article
VL - 74
JO - Med. Image Anal.
JF - Med. Image Anal.
SN - 1361-8415
ER -