Interstitial lung disease diagnosis and prognosis using an AI system integrating longitudinal data.
Journal
Nature communications
ISSN: 2041-1723
Titre abrégé: Nat Commun
Pays: England
ID NLM: 101528555
Informations de publication
Date de publication:
20 04 2023
20 04 2023
Historique:
received:
16
06
2022
accepted:
29
03
2023
medline:
24
4
2023
pubmed:
21
4
2023
entrez:
20
04
2023
Statut:
epublish
Résumé
For accurate diagnosis of interstitial lung disease (ILD), a consensus of radiologic, pathological, and clinical findings is vital. Management of ILD also requires thorough follow-up with computed tomography (CT) studies and lung function tests to assess disease progression, severity, and response to treatment. However, accurate classification of ILD subtypes can be challenging, especially for those not accustomed to reading chest CTs regularly. Dynamic models to predict patient survival rates based on longitudinal data are challenging to create due to disease complexity, variation, and irregular visit intervals. Here, we utilize RadImageNet pretrained models to diagnose five types of ILD with multimodal data and a transformer model to determine a patient's 3-year survival rate. When clinical history and associated CT scans are available, the proposed deep learning system can help clinicians diagnose and classify ILD patients and, importantly, dynamically predict disease progression and prognosis.
Identifiants
pubmed: 37080956
doi: 10.1038/s41467-023-37720-5
pii: 10.1038/s41467-023-37720-5
pmc: PMC10119160
doi:
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Langues
eng
Sous-ensembles de citation
IM
Pagination
2272Subventions
Organisme : NCATS NIH HHS
ID : TL1 TR004420
Pays : United States
Informations de copyright
© 2023. The Author(s).
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