ArticlePulmonary medicine2021
A Machine Learning Approach to the Interpretation of Cardiopulmonary Exercise Tests: Development and Validation.
Article in Pulmonary medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- The Narrative Review: Advancements in Heart Failure Diagnosis and Management using Artificial Intelligence: A New Era of Patient Care.Current cardiology reviews · 2026Review
- On the development of diagnostic support algorithms based on CPET biosignals dataPeerJ. Computer science · 2025Article
- Unveiling the Influence of AI on Advancements in Respiratory Care: Narrative Review.Interactive journal of medical research · 2024Review
- Improving cardiovascular risk stratification through multivariate time-series analysis of cardiopulmonary exercise test data.iScience · 2024Article
- Review
- Integrative Interpretation of Cardiopulmonary Exercise Tests for Cardiovascular Outcome Prediction: A Machine Learning Approach.Diagnostics (Basel, Switzerland) · 2023Article
- Machine Learning Models for the Automatic Detection of Exercise Thresholds in Cardiopulmonary Exercising Tests: From Regression to Generation to Explanation.Sensors (Basel, Switzerland) · 2023Article
- Functional Data Analysis for Predicting Pediatric Failure to Complete Ten Brief Exercise Bouts.IEEE journal of biomedical and health informatics · 2022Article
- Automatic Cardiopulmonary Endurance Assessment: A Machine Learning Approach Based on GA-XGBOOST.Diagnostics (Basel, Switzerland) · 2022Article
- Neural network methods for diagnosing patient conditions from cardiopulmonary exercise testing data.BioData mining · 2022Article
- Using Machine Learning to Identify Organ System Specific Limitations to Exercise via Cardiopulmonary Exercise Testing.IEEE journal of biomedical and health informatics · 2022Article
- Encoding Cardiopulmonary Exercise Testing Time Series as Images for Classification using Convolutional Neural Network.Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference · 2022Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveAt present, there is no consensus on the best strategy for interpreting the cardiopulmonary exercise test's (CPET) results. This study is aimed at assessing the potential of using computer-aided algorithms to evaluate CPET data for identifying chronic heart failure (CHF) and chronic obstructive pulmonary disease (COPD).
methodsData from 234 CPET files from the Pulmonary Institute, at Sheba Medical Center, and the Givat-Washington College, both in Israel, were selected for this study. The selected CPET files included patients with confirmed primary CHF (
resultsThe disease classification results show that the overall predictive power of the proposed interpretive model ranged from 96% to 100%, indicating very high predictive power. Furthermore, the sensitivity, specificity, and overall precision of the proposed interpretive module were 99%, 99%, and 99%, respectively.
conclusionsThe proposed new computer-aided CPET interpretive module was found to be highly sensitive and specific in classifying patients with CHF or COPD, or healthy. Comparable modules may well be applied to additional and larger populations (pathologies and exercise limitations), thereby making this tool powerful and clinically applicable.
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