Pulmonary fibrosis is a progressive lung disease that occurs when lung tissues get scarred and damaged. Although this condition cannot be completely treated, early identification and prediction of its progression can assist to keep it under control. Since this disease can occur without any cause it is termed “Idiopathic”. This disease can cause shortness of breath, fatigue, a dry cough, etc., and lead to death if left uncared. The objective of this paper is to use the patient’s HRCT images from the CT scanner, forced vital capacity (FVC) assessed with a spirometer, and other patient information like sex, smoking status, and so on to predict the severity of idiopathic pulmonary fibrosis progression in the lungs. Nowadays, Machine Learning plays a significant part in the healthcare sector for predicting and diagnosing various diseases, image segmentation, drug discovery, etc. The LSTM (Long Short Term Memory) model is utilized in this work to predict disease progression. The LSTM is a kind of RNN (Recurrent neural network) that is effectively used for predicting time series data and for sequence prediction problems. This model predicts the future values of FVC measurements through which we can know the patient’s severity of the decline.

1.
Shi
,
Yu
&
Wong
,
Weng
&
Goldin
,
Jonathan
&
Brown
,
Matthew
&
Kim
,
Grace
. (
2019
), “
Prediction of Progression in Idiopathic Pulmonary Fibrosis using CT Scans at Baseline: A Quantum Particle Swarm Optimization - Random Forest Approach
.
Artificial Intelligence in Medicine
”,
2019
Sep;
100
:
101709
. doi . Epub 2019 Aug 28. PMID: 31607341.
2.
Kim
,
G.H.J.
,
Weigt
,
S.S.
,
Belperio
,
J.A.
 et al, “
Prediction of idiopathic pulmonary fibrosis progression using early quantitative changes on CT imaging for a short term of clinical 18-24-month follow-ups
”.
EurRadiol
30
,
726
734
(
2020
).
3.
Trusculescu
,
A.A.
,
Manolescu
,
D.
,
Tudorache
,
E.
 et al, “
Deep learning in interstitial lung disease-how long until daily practice
”,
EurRadiol
30
,
6285
6292
(
2020
). .
4.
S. F.
Gaudencio
 et al, “
Three-Dimensional Multiscale Fuzzy Entropy: Validation and Application to Idiopathic Pulmonary Fibrosis
,” in
IEEE Journal of Biomedical and Health Informatics
, vol.
25
, no.
1
, pp.
100
107
, Jan.
2021
, doi: .
5.
Botchkarev
,
A.
(
2018
), “
Performance Metrics (Error Measures) in Machine Learning Regression, Forecasting and Prognostics: Properties and Typology
”, ArXiv, abs/1809.03006.
6.
OSIC organization
, “
OSIC Pulmonary Fibrosis Progression
”, [Online]. Available: https://www.kaggle.com
7.
Sak
,
Haşim
/ Senior, Andrew / Beaufays, Françoise (
2014
): “
Long short-term memory recurrent neural network architectures for large scale acoustic modeling
”, In
INTERSPEECH-2014
,
338
342
.
8.
Fatima
,
Meherwar
&
Pasha
,
Maruf
. (
2017
), “
Survey of Machine Learning Algorithms for Disease Diagnostic
.
Journal of Intelligent Learning Systems and Applications
”,
09
.
1
16
.
9.
F.
Mento
,
G.
Soldati
,
R.
Prediletto
,
M.
Demi
and
L.
Demi
, “
Quantitative Lung Ultrasound Spectroscopy Applied to the Diagnosis of Pulmonary Fibrosis: The First Clinical Study
,” in
IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control
, vol.
67
, no.
11
, pp.
2265
2273
, Nov.
2020
, doi: .
10.
Hutchinson
J.
,
Fogarty
A.
,
Hubbard
R.
,
McKeever T.
Global
Incidence and mortality of idiopathic pulmonary fibrosis: A systematic review
”,
Eur.Respir. J.
2015
;
46
:
795
806
. doi: .
11.
Alex
Sherstinsky
, “
Fundamentals of Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) network
”,
Physica D: Nonlinear Phenomena
, Volume
404
,
2020
,
132306
, ISSN 0167-2789, . (https://www.sciencedirect.com/science/article/pii/S0167278919305974).
This content is only available via PDF.
You do not currently have access to this content.