Usefulness of Heat Map Explanations for Deep-Learning-Based Electrocardiogram Analysis

Andrea M. Storås*, Ole Emil Andersen, Sam Lockhart, Roman Thielemann, Filip Gnesin, Vajira Thambawita, Steven A. Hicks, Jørgen K. Kanters, Inga Strümke, Pål Halvorsen, Michael A. Riegler

*Corresponding author af dette arbejde

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

2 Citationer (Scopus)
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Abstract

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.

OriginalsprogEngelsk
Artikelnummer2345
TidsskriftDiagnostics
Vol/bind13
Udgave nummer14
Antal sider11
ISSN2075-4418
DOI
StatusUdgivet - 2023

Bibliografisk note

Funding Information:
The authors would like to thank the Danish Diabetes and Endocrine Academy and the Danish Data Science Academy for organizing the Data Science Spring School & Challenge for early career researchers and professionals. The research presented in this paper has benefited from the Experimental Infrastructure for Exploration of Exascale Computing (eX3), which is financially supported by the Research Council of Norway under Contract No. 270053.

Funding Information:
Sam Lockhart is supported by a Wellcome Trust Clinical PhD Fellowship (225479/Z/22/Z).

Publisher Copyright:
© 2023 by the authors.

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