We investigated the prognostic utility of a novel deep learning algorithm for quantifying severity of traction bronchiectasis in patients with idiopathic pulmonary fibrosis (IPF) enrolled in the Australian IPF Registry (AIPFR). In IPF, automated quantification of total airway volume predicts mortality independently of total fibrosis extent on HRCT and can be used to identify patients at risk of progression at 12 months.
Felder, F., Nan, Y., Yang, G., Mackintosh, J., Calandriello, L., Silva, M., Glaspole, I., Goh, N., Cooper, W., Grainge, C., Hopkins, P., Moodley, Y., Vidya, N., Reynolds, P., Wells, A., Corte, T., Walsh, S., Deep learning-based quantification of traction bronchiectasis severity for predicting outcome in idiopathic pulmonary fibrosis, Abstract de <<ERS (European Respiratory Society) congress 2023>>, (Milano, 09-12 September 2023 ), <<EUROPEAN RESPIRATORY JOURNAL>>, 2023; 62 (Supplement 67): N/A-N/A. 10.1183/13993003.congress-2023.OA4853 [https://hdl.handle.net/10807/324337]
Deep learning-based quantification of traction bronchiectasis severity for predicting outcome in idiopathic pulmonary fibrosis
Calandriello, Lucio;Silva, Matteo;
2023
Abstract
We investigated the prognostic utility of a novel deep learning algorithm for quantifying severity of traction bronchiectasis in patients with idiopathic pulmonary fibrosis (IPF) enrolled in the Australian IPF Registry (AIPFR). In IPF, automated quantification of total airway volume predicts mortality independently of total fibrosis extent on HRCT and can be used to identify patients at risk of progression at 12 months.
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
