Deep learning-based quantification of traction bronchiectasis severity for predicting outcome in idiopathic pulmonary fibrosis

F Felder; Y Nan; G Yang; J Mackintosh; Lucio Calandriello; M Silva; I Glaspole; N Goh; W Cooper; C Grainge; P Hopkins; Y Moodley; N Vidya; P Reynolds; A Wells; T Corte; S Walsh

doi:10.1183/13993003.congress-2023.OA4853

Deep learning-based quantification of traction bronchiectasis severity for predicting outcome in idiopathic pulmonary fibrosis

F Felder
, Y Nan
, G Yang
, J Mackintosh
, Lucio Calandriello
, M Silva
, I Glaspole
, N Goh
, W Cooper
, C Grainge
, P Hopkins
, Y Moodley
, N Vidya
, P Reynolds
, A Wells
, T Corte
, S Walsh

Risultato della ricerca: Contributo in rivista › Abstract di Conferenza

Abstract

We investigated the prognostic utility 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.

Lingua originale	Inglese
pagine (da-a)	1-1
Numero di pagine	1
Rivista	European Respiratory Journal
Volume	62
Numero di pubblicazione	Supplement 67
DOI	https://doi.org/10.1183/13993003.congress-2023.OA4853
Stato di pubblicazione	Pubblicato - 2023

Keywords

Artificial intelligence

Accesso al documento

10.1183/13993003.congress-2023.OA4853

Altri file e collegamenti

Link a IRIS PubliCatt

Cita questo

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. (2023). Deep learning-based quantification of traction bronchiectasis severity for predicting outcome in idiopathic pulmonary fibrosis. European Respiratory Journal, 62(Supplement 67), 1-1. https://doi.org/10.1183/13993003.congress-2023.OA4853

@article{01acf7c0c1674acc87eef1b6e51d9ced,

title = "Deep learning-based quantification of traction bronchiectasis severity for predicting outcome in idiopathic pulmonary fibrosis",

abstract = "We investigated the prognostic utility 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.",

keywords = "Artificial intelligence, Artificial intelligence",

author = "F Felder and Y Nan and G Yang and J Mackintosh and Lucio Calandriello and M Silva and I Glaspole and N Goh and W Cooper and C Grainge and P Hopkins and Y Moodley and N Vidya and P Reynolds and A Wells and T Corte and S Walsh",

year = "2023",

doi = "10.1183/13993003.congress-2023.OA4853",

language = "English",

volume = "62",

pages = "1--1",

journal = "European Respiratory Journal",

issn = "0903-1936",

publisher = "European Respiratory Society",

number = "Supplement 67",

}

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 2023, 'Deep learning-based quantification of traction bronchiectasis severity for predicting outcome in idiopathic pulmonary fibrosis', European Respiratory Journal, vol. 62, n. Supplement 67, pagg. 1-1. https://doi.org/10.1183/13993003.congress-2023.OA4853

TY - JOUR

T1 - Deep learning-based quantification of traction bronchiectasis severity for predicting outcome in idiopathic pulmonary fibrosis

AU - Felder, F

AU - Nan, Y

AU - Yang, G

AU - Mackintosh, J

AU - Calandriello, Lucio

AU - Silva, M

AU - Glaspole, I

AU - Goh, N

AU - Cooper, W

AU - Grainge, C

AU - Hopkins, P

AU - Moodley, Y

AU - Vidya, N

AU - Reynolds, P

AU - Wells, A

AU - Corte, T

AU - Walsh, S

PY - 2023

Y1 - 2023

N2 - We investigated the prognostic utility 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.

AB - We investigated the prognostic utility 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.

KW - Artificial intelligence

UR - https://publicatt.unicatt.it/handle/10807/324468

U2 - 10.1183/13993003.congress-2023.OA4853

DO - 10.1183/13993003.congress-2023.OA4853

M3 - Meeting Abstract

SN - 0903-1936

VL - 62

SP - 1

EP - 1

JO - European Respiratory Journal

JF - European Respiratory Journal

IS - Supplement 67

ER -

Deep learning-based quantification of traction bronchiectasis severity for predicting outcome in idiopathic pulmonary fibrosis

Abstract

Keywords

Accesso al documento

Altri file e collegamenti

Fingerprint

Cita questo