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Usefulness of Heat Map Explanations for Deep-Learning-Based Electrocardiogram Analysis
Deep neural networks are complex machine learning models that have shown promising results in analyzing high-dimensional data such as those collected from medical examinations. Such models have the potential to provide fast and accurate medical diagnoses. However, the high complexity makes deep neur...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10378376/ https://www.ncbi.nlm.nih.gov/pubmed/37510089 http://dx.doi.org/10.3390/diagnostics13142345 |
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author | Storås, Andrea M. Andersen, Ole Emil Lockhart, Sam Thielemann, Roman Gnesin, Filip Thambawita, Vajira Hicks, Steven A. Kanters, Jørgen K. Strümke, Inga Halvorsen, Pål Riegler, Michael A. |
author_facet | Storås, Andrea M. Andersen, Ole Emil Lockhart, Sam Thielemann, Roman Gnesin, Filip Thambawita, Vajira Hicks, Steven A. Kanters, Jørgen K. Strümke, Inga Halvorsen, Pål Riegler, Michael A. |
author_sort | Storås, Andrea M. |
collection | PubMed |
description | Deep neural networks are complex machine learning models that have shown promising results in analyzing high-dimensional data such as those collected from medical examinations. Such models have the potential to provide fast and accurate medical diagnoses. However, the high complexity makes deep neural networks and their predictions difficult to understand. Providing model explanations can be a way of increasing the understanding of “black box” models and building trust. In this work, we applied transfer learning to develop a deep neural network to predict sex from electrocardiograms. Using the visual explanation method Grad-CAM, heat maps were generated from the model in order to understand how it makes predictions. To evaluate the usefulness of the heat maps and determine if the heat maps identified electrocardiogram features that could be recognized to discriminate sex, medical doctors provided feedback. Based on the feedback, we concluded that, in our setting, this mode of explainable artificial intelligence does not provide meaningful information to medical doctors and is not useful in the clinic. Our results indicate that improved explanation techniques that are tailored to medical data should be developed before deep neural networks can be applied in the clinic for diagnostic purposes. |
format | Online Article Text |
id | pubmed-10378376 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-103783762023-07-29 Usefulness of Heat Map Explanations for Deep-Learning-Based Electrocardiogram Analysis Storås, Andrea M. Andersen, Ole Emil Lockhart, Sam Thielemann, Roman Gnesin, Filip Thambawita, Vajira Hicks, Steven A. Kanters, Jørgen K. Strümke, Inga Halvorsen, Pål Riegler, Michael A. Diagnostics (Basel) Article Deep neural networks are complex machine learning models that have shown promising results in analyzing high-dimensional data such as those collected from medical examinations. Such models have the potential to provide fast and accurate medical diagnoses. However, the high complexity makes deep neural networks and their predictions difficult to understand. Providing model explanations can be a way of increasing the understanding of “black box” models and building trust. In this work, we applied transfer learning to develop a deep neural network to predict sex from electrocardiograms. Using the visual explanation method Grad-CAM, heat maps were generated from the model in order to understand how it makes predictions. To evaluate the usefulness of the heat maps and determine if the heat maps identified electrocardiogram features that could be recognized to discriminate sex, medical doctors provided feedback. Based on the feedback, we concluded that, in our setting, this mode of explainable artificial intelligence does not provide meaningful information to medical doctors and is not useful in the clinic. Our results indicate that improved explanation techniques that are tailored to medical data should be developed before deep neural networks can be applied in the clinic for diagnostic purposes. MDPI 2023-07-11 /pmc/articles/PMC10378376/ /pubmed/37510089 http://dx.doi.org/10.3390/diagnostics13142345 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Storås, Andrea M. Andersen, Ole Emil Lockhart, Sam Thielemann, Roman Gnesin, Filip Thambawita, Vajira Hicks, Steven A. Kanters, Jørgen K. Strümke, Inga Halvorsen, Pål Riegler, Michael A. Usefulness of Heat Map Explanations for Deep-Learning-Based Electrocardiogram Analysis |
title | Usefulness of Heat Map Explanations for Deep-Learning-Based Electrocardiogram Analysis |
title_full | Usefulness of Heat Map Explanations for Deep-Learning-Based Electrocardiogram Analysis |
title_fullStr | Usefulness of Heat Map Explanations for Deep-Learning-Based Electrocardiogram Analysis |
title_full_unstemmed | Usefulness of Heat Map Explanations for Deep-Learning-Based Electrocardiogram Analysis |
title_short | Usefulness of Heat Map Explanations for Deep-Learning-Based Electrocardiogram Analysis |
title_sort | usefulness of heat map explanations for deep-learning-based electrocardiogram analysis |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10378376/ https://www.ncbi.nlm.nih.gov/pubmed/37510089 http://dx.doi.org/10.3390/diagnostics13142345 |
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