ArticleBMJ open respiratory research2022
Deep learning using multilayer perception improves the diagnostic acumen of spirometry: a single-centre Canadian study.
Article in BMJ open respiratory research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed, 12 citations in OpenAlex.
- Classification of pulmonary diseases using machine learning and deep learning models on GLI-2012 standardized spirometry features.BMC medical informatics and decision making · 2026Article
- Artificial Intelligence-Powered Chronic Obstructive Pulmonary Disease Detection Techniques-A Review.Diagnostics (Basel, Switzerland) · 2025Review
- Innovation in technologies for monitoring lung function in patients with respiratory disease.Breathe (Sheffield, England) · 2025Article
- Article
- Deep Learning-Based Chronic Obstructive Pulmonary Disease Exacerbation Prediction Using Flow-Volume and Volume-Time Curve Imaging: Retrospective Cohort Study.Journal of medical Internet research · 2025Article
- Development and External Validation of a Machine Learning Model to Predict Restriction from Spirometry.medRxiv : the preprint server for health sciences · 2025Article
- Prediction of xerostomia in elderly based on clinical characteristics and salivary flow rate with machine learning.Scientific reports · 2024Article
- nPCA: a linear dimensionality reduction method using a multilayer perceptron.Frontiers in genetics · 2023Article
- The Role and Diagnostic Accuracy of Artificial Intelligence in Pulmonary Function Tests: A Systematic Review.Current health sciences journalReview
Corrections and comments
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Authors and funding
10 authors at 2 institutions in 1 country.
Funding
Abstract
rationaleSpirometry and plethysmography are the gold standard pulmonary function tests (PFT) for diagnosis and management of lung disease. Due to the inaccessibility of plethysmography, spirometry is often used alone but this leads to missed or misdiagnoses as spirometry cannot identify restrictive disease without plethysmography. We aimed to develop a deep learning model to improve interpretation of spirometry alone.
methodsWe built a multilayer perceptron model using full PFTs from 748 patients, interpreted according to international guidelines. Inputs included spirometry (forced vital capacity, forced expiratory volume in 1 s, forced mid-expiratory flow
resultsAccuracies from the first 477 patients were similar when inputs included biometrics+spirometry+plethysmography (95%±3%) vs biometrics+spirometry (90%±2%). Model refinement with the next 271 patients improved accuracies with biometrics+pirometry (95%±2%) but no change for biometrics+spirometry+plethysmography (95%±2%). The final model significantly outperformed (94.67%±2.63%, p<0.01 for both) interpretation of 82 spirometry tests by the decision tree (75.61%±0.00%) and pulmonologists (66.67%±14.63%).
conclusionsDeep learning improves the diagnostic acumen of spirometry and classifies lung physiology better than pulmonologists with accuracies comparable to full PFTs.
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