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Related Concept Videos

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Pulmonary Function Tests01:25

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Related Experiment Video

Updated: Mar 27, 2026

Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections
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Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections

Published on: September 19, 2025

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SpiroLLM: Finetuning pretrained LLMs to understand spirogram time series with clinical validation in COPD reporting.

Shuhao Mei1,2,3, Yongchao Long2, Xiaoyu Xiao1

  • 1Guangzhou Institute of Technology, Xidian University, Xi'an, China.

PLOS Digital Health
|March 24, 2026
PubMed
Summary

SpiroLLM, a new AI model, interprets respiratory spirograms for Chronic Obstructive Pulmonary Disease (COPD) diagnosis. This multimodal approach enhances clinical trust by providing diagnostic reports, improving upon text-only models.

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Author Spotlight: Enhancing Diagnostic Strategies and Biomarker Development for Comprehensive Lung Function Analysis
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Area of Science:

  • Artificial Intelligence in Medicine
  • Respiratory Medicine
  • Medical Diagnostics

Background:

  • Chronic Obstructive Pulmonary Disease (COPD) is a significant global health burden.
  • Pulmonary Function Tests (PFTs) generate vital spirogram data for lung health monitoring.
  • Current AI diagnostic tools lack interpretability, hindering clinical adoption.

Purpose of the Study:

  • To develop the first multimodal large language model (LLM) capable of understanding spirograms for COPD diagnosis.
  • To enhance the interpretability and clinical trust of AI-driven diagnostic tools.

Main Methods:

  • Leveraged UK Biobank data from 234,028 individuals.
  • Developed SpiroLLM, integrating a SpiroEncoder and SpiroProjector to process spirogram morphology and PFT values.
  • Utilized a unified latent space for feature alignment.

Main Results:

  • SpiroLLM achieved an Area Under the Receiver Operating Characteristic Curve (AUROC) of 0.8977.
  • Demonstrated superior robustness with a 100% valid response rate on incomplete data, compared to 13.4% for text-only models.
  • Successfully generated comprehensive diagnostic reports from spirogram data.

Conclusions:

  • SpiroLLM represents a novel paradigm for interpretable clinical decision support.
  • Deep fusion of physiological signals with LLMs holds substantial potential for advancing respiratory disease diagnostics.
  • The multimodal approach significantly improves AI reliability and clinical applicability in PFT analysis.