Deep neural network analyses of spirometry for structural phenotyping of chronic obstructive pulmonary disease

Sandeep Bodduluri; Arie Nakhmani; Joseph M Reinhardt; Carla G Wilson; Merry-Lynn McDonald; Ramaraju Rudraraju; Byron C Jaeger; Nirav R Bhakta; Peter J Castaldi; Frank C Sciurba; Chengcui Zhang; Purushotham V Bangalore; Surya P Bhatt

doi:10.1172/jci.insight.132781

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Deep neural network analyses of spirometry for structural phenotyping of chronic obstructive pulmonary disease

Journal article

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Deep neural network analyses of spirometry for structural phenotyping of chronic obstructive pulmonary disease

Sandeep Bodduluri, Arie Nakhmani, Joseph M Reinhardt, Carla G Wilson, Merry-Lynn McDonald, Ramaraju Rudraraju, Byron C Jaeger, Nirav R Bhakta, Peter J Castaldi, Frank C Sciurba, …

JCI insight, Vol.5(13), e132781

07/09/2020

DOI: 10.1172/jci.insight.132781

PMCID: PMC7406302

PMID: 32554922

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Abstract

BACKGROUNDCurrently recommended traditional spirometry outputs do not reflect the relative contributions of emphysema and airway disease to airflow obstruction. We hypothesized that machine-learning algorithms can be trained on spirometry data to identify these structural phenotypes.METHODSParticipants enrolled in a large multicenter study (COPDGene) were included. The data points from expiratory flow-volume curves were trained using a deep-learning model to predict structural phenotypes of chronic obstructive pulmonary disease (COPD) on CT, and results were compared with traditional spirometry metrics and an optimized random forest classifier. Area under the receiver operating characteristic curve (AUC) and weighted F-score were used to measure the discriminative accuracy of a fully convolutional neural network, random forest, and traditional spirometry metrics to phenotype CT as normal, emphysema-predominant (>5% emphysema), airway-predominant (Pi10 > median), and mixed phenotypes. Similar comparisons were made for the detection of functional small airway disease phenotype (>20% on parametric response mapping).RESULTSAmong 8980 individuals, the neural network was more accurate in discriminating predominant emphysema/airway phenotypes (AUC 0.80, 95%CI 0.79-0.81) compared with traditional measures of spirometry, FEV1/FVC (AUC 0.71, 95%CI 0.69-0.71), FEV1% predicted (AUC 0.70, 95%CI 0.68-0.71), and random forest classifier (AUC 0.78, 95%CI 0.77-0.79). The neural network was also more accurate in discriminating predominant emphysema/small airway phenotypes (AUC 0.91, 95%CI 0.90-0.92) compared with FEV1/FVC (AUC 0.80, 95%CI 0.78-0.82), FEV1% predicted (AUC 0.83, 95%CI 0.80-0.84), and with comparable accuracy with random forest classifier (AUC 0.90, 95%CI 0.88-0.91).CONCLUSIONSStructural phenotypes of COPD can be identified from spirometry using deep-learning and machine-learning approaches, demonstrating their potential to identify individuals for targeted therapies.TRIAL REGISTRATIONClinicalTrials.gov NCT00608764.FUNDINGThis study was supported by NIH grants K23 HL133438 and R21EB027891 and an American Thoracic Foundation 2018 Unrestricted Research Grant. The COPDGene study is supported by NIH grants NHLBI U01 HL089897 and U01 HL089856. The COPDGene study (NCT00608764) is also supported by the COPD Foundation through contributions made to an Industry Advisory Committee comprising AstraZeneca, Boehringer-Ingelheim, GlaxoSmithKline, Novartis, and Sunovion.

Pulmonology

COPD

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Title: Subtitle: Deep neural network analyses of spirometry for structural phenotyping of chronic obstructive pulmonary disease
Creators: Sandeep Bodduluri - Division of Pulmonary, Allergy and Critical Care Medicine, and
Arie Nakhmani - Department of Electrical and Computer Engineering, University of Alabama at Birmingham, Birmingham, Alabama, USA
Joseph M Reinhardt - Department of Biomedical Engineering, University of Iowa, Iowa City, Iowa, USA
Carla G Wilson - Department of Biostatistics and Bioinformatics, National Jewish Health, Denver, Colorado, USA
Merry-Lynn McDonald - Division of Pulmonary, Allergy and Critical Care Medicine, and
Ramaraju Rudraraju - Division of Cardiothoracic Surgery and
Byron C Jaeger - Department of Biostatistics, University of Alabama at Birmingham, Birmingham, Alabama, USA
Nirav R Bhakta - Division of Pulmonary, Critical Care, Allergy and Sleep Medicine, University California, San Francisco, San Francisco, California, USA
Peter J Castaldi - Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, Massachusetts, USA
Frank C Sciurba - Division of Pulmonary, Allergy and Critical Care Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
Chengcui Zhang - Department of Computer Science, University of Alabama at Birmingham, Birmingham, Alabama, USA
Purushotham V Bangalore - Department of Computer Science, University of Alabama at Birmingham, Birmingham, Alabama, USA
Surya P Bhatt - Division of Pulmonary, Allergy and Critical Care Medicine, and
Resource Type: Journal article
Publication Details: JCI insight, Vol.5(13), e132781
DOI: 10.1172/jci.insight.132781
PMID: 32554922
PMCID: PMC7406302
NLM abbreviation: JCI Insight
ISSN: 2379-3708
eISSN: 2379-3708
Publisher: United States
Grant note: U01 HL089856 / NHLBI NIH HHS U01 HL089897 / NHLBI NIH HHS K23 HL133438 / NHLBI NIH HHS R21 EB027891 / NIBIB NIH HHS P30 DK054759 / NIDDK NIH HHS R01 HL151421 / NHLBI NIH HHS
Language: English
Date published: 07/09/2020
Academic Unit: Roy J. Carver Department of Biomedical Engineering; Radiology
Record Identifier: 9984066114302771

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