AIforCOVID: predicting the clinical outcomes in patients with COVID-19 applying AI to chest-X-rays. An Italian multicentre study

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 chest X-ray (CXR) can be used as a possible tool for the early identification of patients at risk of severe outcome, like intensive care or death. CXR is a radiological technique that compared to computed tomography (CT) it is simpler, faster, more widespread and it induces lower radiation dose. We present a dataset including data collected from 820 patients by six Italian hospitals in spring 2020 during the first COVID-19 emergency. The dataset includes CXR images, several clinical attributes and clinical outcomes. We investigate the potential of artificial intelligence to predict the prognosis of such patients, distinguishing between severe and mild cases, thus offering a baseline reference for other researchers and practitioners. To this goal, we present three approaches that use features extracted from CXR images, either handcrafted or automatically by convolutional neuronal networks, which are then integrated with the clinical data. Exhaustive evaluation shows promising performance both in 10-fold and leave-one-centre-out cross-validation, implying that clinical data and images have the potential to provide useful information for the management of patients and hospital resources

La mia Russia. Storie da un paese perduto

Italian translation of the book of the Russian journalist Elena Kostyuchenk

Assessment of lumbar disc herniaton using fractional anisotropy in diffusion tensor imaging along with conventional T2-weighted imaging

To assess the usefulness of diffusion tensor imaging and its fractional anisotropy map along with conventional T2-weighted imaging in evaluating the anisotropic water diffusion variations of annulus fibres involved in herniation disc pathology

Diagnosis of COVID-19 in Patients with Negative Nasopharyngeal Swabs: Reliability of Radiological and Clinical Diagnosis and Accuracy Versus Serology

Background: The diagnosis of Coronavirus disease 2019 (COVID-19) relies on the positivity of nasopharyngeal swab. However, a significant percentage of symptomatic patients may test negative. We evaluated the reliability of COVID-19 diagnosis made by radiologists and clinicians and its accuracy versus serology in a sample of patients hospitalized for suspected COVID-19 with multiple negative swabs. Methods: Admission chest CT-scans and clinical records of swab-negative patients, treated according to the COVID-19 protocol or deceased during hospitalization, were retrospectively evaluated by two radiologists and two clinicians, respectively. Results: Of 254 patients, 169 swab-confirmed cases and one patient without chest CT-scan were excluded. A total of 84 patients were eligible for the reliability study. Of these, 21 patients died during hospitalization; the remaining 63 underwent serological testing and were eligible for the accuracy evaluation. Of the 63, 26 patients showed anti-Sars-Cov-2 antibodies, while 37 did not. The inter-rater agreement was “substantial” (kappa 0.683) between radiologists, “moderate” (kappa 0.454) between clinicians, and only “fair” (kappa 0.341) between radiologists and clinicians. Both radiologic and clinical evaluations showed good accuracy compared to serology. Conclusions: The radiologic and clinical diagnosis of COVID-19 for swab-negative patients proved to be sufficiently reliable and accurate to allow a diagnosis of COVID-19, which needs to be confirmed by serology and follow-up

AIforCOVID: Predicting the clinical outcomes in patients with COVID-19 applying AI to chest-X-rays. An Italian multicentre study

open28Recent 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.openPaolo Soda, Natascha Claudia D’Amico, Jacopo Tessadori, Giovanni Valbusa, Valerio Guarrasi, Chandra Bortolotto, Muhammad Usman Akbar, Rosa Sicilia, Ermanno Cordelli, Deborah Fazzini, Michaela Cellina, Giancarlo Oliva, Giovanni Callea, Silvia Panella, Maurizio Cariati, Diletta Cozzi, Vittorio Miele, Elvira Stellato, Gianpaolo Carrafiello, Giulia Castorani, Annalisa Simeone, Lorenzo Preda, Giulio Iannello, Alessio Del Bue, Fabio Tedoldi, Marco Alí, Diego Sona, Sergio PapaSoda, Paolo; Claudia D’Amico, Natascha; Tessadori, Jacopo; Valbusa, Giovanni; Guarrasi, Valerio; Bortolotto, Chandra; Usman Akbar, Muhammad; Sicilia, Rosa; Cordelli, Ermanno; Fazzini, Deborah; Cellina, Michaela; Oliva, Giancarlo; Callea, Giovanni; Panella, Silvia; Cariati, Maurizio; Cozzi, Diletta; Miele, Vittorio; Stellato, Elvira; Carrafiello, Gianpaolo; Castorani, Giulia; Simeone, Annalisa; Preda, Lorenzo; Iannello, Giulio; Del Bue, Alessio; Tedoldi, Fabio; Alí, Marco; Sona, Diego; Papa, Sergi