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Automatic diagnostic support for diagnosis of pulmonary fibrosis

Automatic diagnostic support for diagnosis of pulmonary fibrosis
Automatic diagnostic support for diagnosis of pulmonary fibrosis
Patients with pulmonary fibrosis (PF) often experience long waits before getting a correct diagnosis, and this delay in reaching specialized care is associated with increased mortality, regardless of the severity of the disease. Early diagnosis and timely treatment of PF can potentially extend life expectancy and maintain a better quality of life. Crackles present in the recorded lung sounds may be crucial for the early diagnosis of PF. This paper describes an automated system for differentiating lung sounds related to PF from other pathological lung conditions using the average number of crackles per breath cycle (NOC/BC). The system is divided into four main parts: (1) preprocessing, (2) separation of crackles from normal breath sounds, (3) crackle verification and counting, and (4) estimating NOC/BC. The system was tested on a dataset consisting of 48 (24 fibrotic and 24 non-fibrotic) subjects and the results were compared with an assessment by two expert respiratory physicians. The set of HRCT images, reviewed by two expert radiologists for the presence or absence of pulmonary fibrosis, was used as the ground truth for evaluating the PF and non-PF classification performance of the system. The overall performance of the automatic classifier based on receiver operating curve-derived cut-off value for average NOC/BC of 18.65 (AUC=0.845, 95 % CI 0.739-0.952, p<0.001; sensitivity=91.7 %; specificity=59.3 %) compares favorably with the averaged performance of the physicians (sensitivity=83.3 %; specificity=56.25 %). Although radiological assessment should remain the gold standard for diagnosis of fibrotic interstitial lung disease, the automatic classification system has strong potential for diagnostic support, especially in assisting general practitioners in the auscultatory assessment of lung sounds to prompt further diagnostic work up of patients with suspect of interstitial lung disease.

medRxiv
Pal, Ravi
a4973d64-eac7-47db-ad19-e79e6c64abd0
Barney, Anna
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Sgalla, Giacomo
a827a8c4-77f9-4d0e-bc08-a1aa01ffdf5a
Walsh, Simon L.F.
229a3dbd-04d7-4f46-9efa-aa8315293019
Sverzellati, Nicola
03bddd86-a6f5-44e6-898f-3cfad8e2390c
Fletcher, Sophie
71599088-9df7-4d4a-8570-aef773ead0fe
Cerri, Stefania
7ea9bc28-eaf2-46ce-8433-b9e732484b5f
Cannesson, Maxime
6f6c4da6-cd22-4d1d-b6e1-ed799d019bbd
Richeldi, Luca
540f0654-88a1-4ce1-8141-fb1b1ec1071d
Pal, Ravi
a4973d64-eac7-47db-ad19-e79e6c64abd0
Barney, Anna
bc0ee7f7-517a-4154-ab7d-57270de3e815
Sgalla, Giacomo
a827a8c4-77f9-4d0e-bc08-a1aa01ffdf5a
Walsh, Simon L.F.
229a3dbd-04d7-4f46-9efa-aa8315293019
Sverzellati, Nicola
03bddd86-a6f5-44e6-898f-3cfad8e2390c
Fletcher, Sophie
71599088-9df7-4d4a-8570-aef773ead0fe
Cerri, Stefania
7ea9bc28-eaf2-46ce-8433-b9e732484b5f
Cannesson, Maxime
6f6c4da6-cd22-4d1d-b6e1-ed799d019bbd
Richeldi, Luca
540f0654-88a1-4ce1-8141-fb1b1ec1071d

[Unknown type: UNSPECIFIED]

Record type: UNSPECIFIED

Abstract

Patients with pulmonary fibrosis (PF) often experience long waits before getting a correct diagnosis, and this delay in reaching specialized care is associated with increased mortality, regardless of the severity of the disease. Early diagnosis and timely treatment of PF can potentially extend life expectancy and maintain a better quality of life. Crackles present in the recorded lung sounds may be crucial for the early diagnosis of PF. This paper describes an automated system for differentiating lung sounds related to PF from other pathological lung conditions using the average number of crackles per breath cycle (NOC/BC). The system is divided into four main parts: (1) preprocessing, (2) separation of crackles from normal breath sounds, (3) crackle verification and counting, and (4) estimating NOC/BC. The system was tested on a dataset consisting of 48 (24 fibrotic and 24 non-fibrotic) subjects and the results were compared with an assessment by two expert respiratory physicians. The set of HRCT images, reviewed by two expert radiologists for the presence or absence of pulmonary fibrosis, was used as the ground truth for evaluating the PF and non-PF classification performance of the system. The overall performance of the automatic classifier based on receiver operating curve-derived cut-off value for average NOC/BC of 18.65 (AUC=0.845, 95 % CI 0.739-0.952, p<0.001; sensitivity=91.7 %; specificity=59.3 %) compares favorably with the averaged performance of the physicians (sensitivity=83.3 %; specificity=56.25 %). Although radiological assessment should remain the gold standard for diagnosis of fibrotic interstitial lung disease, the automatic classification system has strong potential for diagnostic support, especially in assisting general practitioners in the auscultatory assessment of lung sounds to prompt further diagnostic work up of patients with suspect of interstitial lung disease.

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lung sounds 2024 - Author's Original
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Published date: 20 August 2024

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Local EPrints ID: 494809
URI: http://eprints.soton.ac.uk/id/eprint/494809
PURE UUID: 14d20931-748b-4cf1-8ef7-0b2eb6abdfa5
ORCID for Anna Barney: ORCID iD orcid.org/0000-0002-6034-1478
ORCID for Sophie Fletcher: ORCID iD orcid.org/0000-0002-5633-905X

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Date deposited: 15 Oct 2024 17:06
Last modified: 19 Oct 2024 02:14

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Contributors

Author: Ravi Pal
Author: Anna Barney ORCID iD
Author: Giacomo Sgalla
Author: Simon L.F. Walsh
Author: Nicola Sverzellati
Author: Sophie Fletcher ORCID iD
Author: Stefania Cerri
Author: Maxime Cannesson
Author: Luca Richeldi

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