ArticleNature genetics2023
Inference of chronic obstructive pulmonary disease with deep learning on raw spirograms identifies new genetic loci and improves risk models.
Article in Nature genetics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers, 2 of them syntheses that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
40 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Accuracy of Deep Learning in Diagnosing Chronic Obstructive Pulmonary Disease: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2026Pooled it
- Newborn blood DNA methylation and childhood asthma: findings from the ECHO program.International journal of epidemiology · 2025Pooled it
- Polygenic Risk Scores Show Distinct Genetic Architectures of IBD-Associated Comorbidities.Digestive diseases and sciences · 2026Article
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- SpiroLLM: Finetuning pretrained LLMs to understand spirogram time series with clinical validation in COPD reporting.PLOS digital health · 2026Article
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- Computer-aided drug discovery: historical foundations, practical AI tools, and emerging ethical considerations.Frontiers in pharmacology · 2026Review
- Genome-wide Association Study IdentifiesAllergy, asthma & immunology research · 2026Article
- Molecular determinants of lung function decline: a multi-level analysis of gene expression.Respiratory research · 2025Article
- Article
- Artificial Intelligence-Powered Chronic Obstructive Pulmonary Disease Detection Techniques-A Review.Diagnostics (Basel, Switzerland) · 2025Review
- Improving AI coaching with Gemini using real-world Fitbit data.Nature medicine · 2025Article
- Lung NR3C1Communications biology · 2025Article
- Genetic analyses of eight complex diseases using predicted continuous representations of disease.Cell reports methods · 2025Article
- Applying multimodal AI to physiological waveforms improves genetic prediction of cardiovascular traits.American journal of human genetics · 2025Article
- Deep Learning of Suboptimal Spirometry to Predict Respiratory Outcomes and Mortality.Research square · 2025Article
- The effect of a post-bronchodilator FEVRespiratory research · 2025Article
- Identifying cross-tissue molecular targets of lung function by multi-omics integration analysis from DNA methylation and gene expression of diverse human tissues.BMC genomics · 2025Article
- Trans-ancestral rare variant association study with machine learning-based phenotyping for metabolic dysfunction-associated steatotic liver disease.Genome biology · 2025Article
Corrections and comments
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Authors and funding
12 authors.
Funding
Abstract
Chronic obstructive pulmonary disease (COPD), the third leading cause of death worldwide, is highly heritable. While COPD is clinically defined by applying thresholds to summary measures of lung function, a quantitative liability score has more power to identify genetic signals. Here we train a deep convolutional neural network on noisy self-reported and International Classification of Diseases labels to predict COPD case-control status from high-dimensional raw spirograms and use the model's predictions as a liability score. The machine-learning-based (ML-based) liability score accurately discriminates COPD cases and controls, and predicts COPD-related hospitalization without any domain-specific knowledge. Moreover, the ML-based liability score is associated with overall survival and exacerbation events. A genome-wide association study on the ML-based liability score replicates existing COPD and lung function loci and also identifies 67 new loci. Lastly, our method provides a general framework to use ML methods and medical-record-based labels that does not require domain knowledge or expert curation to improve disease prediction and genomic discovery for drug design.
Indexed as
Identifiers
37069358What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.