Deprecated: Implicit conversion from float 235.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534
Deprecated: Implicit conversion from float 235.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534
Deprecated: Implicit conversion from float 235.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534
Deprecated: Implicit conversion from float 235.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Med+Image+Anal 2021 ; 67 (ä): 101860 Nephropedia Template TP
gab.com Text
Twit Text FOAVip
Twit Text #
English Wikipedia
AI-driven quantification, staging and outcome prediction of COVID-19 pneumonia #MMPMID33171345
Chassagnon G; Vakalopoulou M; Battistella E; Christodoulidis S; Hoang-Thi TN; Dangeard S; Deutsch E; Andre F; Guillo E; Halm N; El Hajj S; Bompard F; Neveu S; Hani C; Saab I; Campredon A; Koulakian H; Bennani S; Freche G; Barat M; Lombard A; Fournier L; Monnier H; Grand T; Gregory J; Nguyen Y; Khalil A; Mahdjoub E; Brillet PY; Tran Ba S; Bousson V; Mekki A; Carlier RY; Revel MP; Paragios N
Med Image Anal 2021[Jan]; 67 (ä): 101860 PMID33171345show ga
Coronavirus disease 2019 (COVID-19) emerged in 2019 and disseminated around the world rapidly. Computed tomography (CT) imaging has been proven to be an important tool for screening, disease quantification and staging. The latter is of extreme importance for organizational anticipation (availability of intensive care unit beds, patient management planning) as well as to accelerate drug development through rapid, reproducible and quantified assessment of treatment response. Even if currently there are no specific guidelines for the staging of the patients, CT together with some clinical and biological biomarkers are used. In this study, we collected a multi-center cohort and we investigated the use of medical imaging and artificial intelligence for disease quantification, staging and outcome prediction. Our approach relies on automatic deep learning-based disease quantification using an ensemble of architectures, and a data-driven consensus for the staging and outcome prediction of the patients fusing imaging biomarkers with clinical and biological attributes. Highly promising results on multiple external/independent evaluation cohorts as well as comparisons with expert human readers demonstrate the potentials of our approach.