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Using Machine Learning-Based Algorithms to Identify and Quantify Exercise Limitations in Clinical Practice: Are We

Fabian Schwendinger¹, Ann-Kathrin Biehler, Monika Nagy-Huber²

¹Division of Sports and Exercise Medicine, Department of Sport, Exercise and Health, University of Basel, Basel, SWITZERLAND.

Medicine and Science in Sports and Exercise

|September 13, 2023

Summary

This summary is machine-generated.

Machine learning algorithms can accurately classify exercise limitations from cardiopulmonary exercise tests (CPET), matching expert performance. This technology may aid clinical decision-making and standardize CPET interpretation.

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Area of Science:

Cardiopulmonary physiology
Artificial intelligence in medicine
Clinical decision support

Background:

Interpreting cardiopulmonary exercise tests (CPET) requires specialized staff.
Machine learning (ML) offers potential for automating CPET interpretation.

Purpose of the Study:

To evaluate the accuracy of ML algorithms in classifying exercise limitations and their severity.
To compare ML algorithm performance against expert consensus in a clinical setting.

Main Methods:

Analysis of 200 historical CPET data sets from patients over 40.
Independent expert rating of limitations (pulmonary-vascular, mechanical-ventilatory, cardiocirculatory, muscular) using a visual analog scale.
Application of decision trees and random forests for data analysis.

Main Results:

Random forests identified key parameters for specific limitations (e.g., ventilatory efficiency for pulmonary-vascular).
Decision tree accuracy was comparable to expert ratings.
A combined decision tree was developed to quantify multiple system limitations.

Conclusions:

ML algorithms show promise for facilitating CPET interpretation and identifying exercise limitations.
These findings support clinical decision-making and the development of standardized CPET rating tools.