[en] In the course of the pandemic induced by the appearance of a new coronavirus (SARS-CoV-2; COVID-19) causing acute respiratory distress syndrome (ARDS), we had to rethink the diagnostic approach for patients suffering from respiratory symptoms. Indeed, although the use of RT-PCR remains the keystone of the diagnosis, the delay in diagnosis as well as the overload of the microbiological platforms have led us to make almost systematic the use of thoracic imaging for taking in charge of patients. In this context, thoracic imaging has shown a major interest in diagnostic aid in order to better guide the management of patients admitted to hospital. The most common signs encountered are particularly well described in thoracic computed tomography. Typical imaging combines bilateral, predominantly peripheral and posterior, multi-lobar, ground glass opacities. Of note, it is common to identify significant lesions in asymptomatic patients, with imaging sometimes preceding the onset of symptoms. Beyond conventional chest imaging, many teams have developed new artificial intelligence tools to better help clinicians in decision-making.
Disciplines :
Radiologie, médecine & imagerie nucléaire
Auteur, co-auteur :
Guiot, Julien ; Université de Liège - ULiège > Département de pharmacie > Département de pharmacie
Danthine, Denis ; Centre Hospitalier Universitaire de Liège - CHU > Département de Physique Médicale > Service médical de médecine nucléaire et imagerie onco
DEPREZ, Louis ; Centre Hospitalier Universitaire de Liège - CHU > Département de Physique Médicale > Service médical de radiodiagnostic
Louis, Renaud ; Université de Liège - ULiège > Département des sciences cliniques > Pneumologie - Allergologie
Lovinfosse, Pierre ; Université de Liège - ULiège > Département de pharmacie > Département de pharmacie
Meunier, Paul ; Université de Liège - ULiège > Département des sciences cliniques > Imagerie abdominale
Langue du document :
Français
Titre :
Les aspects radiologiques de la pneumopathie à COVID-19 : de l’imagerie conventionnelle à l’intelligence artificielle.
Titre traduit :
[en] Chest radiological lesions in COVID-19 : from classical imaging to artificial intelligence
Date de publication/diffusion :
2021
Titre du périodique :
Revue Médicale de Liège
ISSN :
0370-629X
eISSN :
2566-1566
Maison d'édition :
Université de Liège. Revue Médicale de Liège, Liège, Belgique
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