Deep Learning to Quantify Pulmonary Edema in Chest Radiographs

PURPOSE: To develop a machine learning model to classify the severity grades of pulmonary edema on chest radiographs. MATERIALS AND METHODS: In this retrospective study, 369 071 chest radiographs and associated radiology reports from 64 581 patients (mean age, 51.71 years; 54.51% women) from the MIM...

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Salvato in:
Dettagli Bibliografici
Pubblicato in:Radiol Artif Intell
Autori principali: Horng, Steven, Liao, Ruizhi, Wang, Xin, Dalal, Sandeep, Golland, Polina, Berkowitz, Seth J.
Natura: Artigo
Lingua:Inglês
Pubblicazione: Radiological Society of North America 2021
Soggetti:
Original Research
Accesso online:https://ncbi.nlm.nih.gov/pmc/articles/PMC8043362/
https://ncbi.nlm.nih.gov/pubmed/33937857
https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1148/ryai.2021190228
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