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A method for AI assisted human interpretation of neonatal EEG

The study proposes a novel method to empower healthcare professionals to interact and leverage AI decision support in an intuitive manner using auditory senses. The method’s suitability is assessed through acoustic detection of the presence of neonatal seizures in electroencephalography (EEG). Neuro...

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Autores principales: Gomez-Quintana, Sergi, O’Shea, Alison, Factor, Andreea, Popovici, Emanuel, Temko, Andriy
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9243143/
https://www.ncbi.nlm.nih.gov/pubmed/35768501
http://dx.doi.org/10.1038/s41598-022-14894-4
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author Gomez-Quintana, Sergi
O’Shea, Alison
Factor, Andreea
Popovici, Emanuel
Temko, Andriy
author_facet Gomez-Quintana, Sergi
O’Shea, Alison
Factor, Andreea
Popovici, Emanuel
Temko, Andriy
author_sort Gomez-Quintana, Sergi
collection PubMed
description The study proposes a novel method to empower healthcare professionals to interact and leverage AI decision support in an intuitive manner using auditory senses. The method’s suitability is assessed through acoustic detection of the presence of neonatal seizures in electroencephalography (EEG). Neurophysiologists use EEG recordings to identify seizures visually. However, neurophysiological expertise is expensive and not available 24/7, even in tertiary hospitals. Other neonatal and pediatric medical professionals (nurses, doctors, etc.) can make erroneous interpretations of highly complex EEG signals. While artificial intelligence (AI) has been widely used to provide objective decision support for EEG analysis, AI decisions are not always explainable. This work developed a solution to combine AI algorithms with a human-centric intuitive EEG interpretation method. Specifically, EEG is converted to sound using an AI-driven attention mechanism. The perceptual characteristics of seizure events can be heard using this method, and an hour of EEG can be analysed in five seconds. A survey that has been conducted among targeted end-users on a publicly available dataset has demonstrated that not only does it drastically reduce the burden of reviewing the EEG data, but also the obtained accuracy is on par with experienced neurophysiologists trained to interpret neonatal EEG. It is also shown that the proposed communion of a medical professional and AI outperforms AI alone by empowering the human with little or no experience to leverage AI attention mechanisms to enhance the perceptual characteristics of seizure events.
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spelling pubmed-92431432022-06-30 A method for AI assisted human interpretation of neonatal EEG Gomez-Quintana, Sergi O’Shea, Alison Factor, Andreea Popovici, Emanuel Temko, Andriy Sci Rep Article The study proposes a novel method to empower healthcare professionals to interact and leverage AI decision support in an intuitive manner using auditory senses. The method’s suitability is assessed through acoustic detection of the presence of neonatal seizures in electroencephalography (EEG). Neurophysiologists use EEG recordings to identify seizures visually. However, neurophysiological expertise is expensive and not available 24/7, even in tertiary hospitals. Other neonatal and pediatric medical professionals (nurses, doctors, etc.) can make erroneous interpretations of highly complex EEG signals. While artificial intelligence (AI) has been widely used to provide objective decision support for EEG analysis, AI decisions are not always explainable. This work developed a solution to combine AI algorithms with a human-centric intuitive EEG interpretation method. Specifically, EEG is converted to sound using an AI-driven attention mechanism. The perceptual characteristics of seizure events can be heard using this method, and an hour of EEG can be analysed in five seconds. A survey that has been conducted among targeted end-users on a publicly available dataset has demonstrated that not only does it drastically reduce the burden of reviewing the EEG data, but also the obtained accuracy is on par with experienced neurophysiologists trained to interpret neonatal EEG. It is also shown that the proposed communion of a medical professional and AI outperforms AI alone by empowering the human with little or no experience to leverage AI attention mechanisms to enhance the perceptual characteristics of seizure events. Nature Publishing Group UK 2022-06-29 /pmc/articles/PMC9243143/ /pubmed/35768501 http://dx.doi.org/10.1038/s41598-022-14894-4 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Gomez-Quintana, Sergi
O’Shea, Alison
Factor, Andreea
Popovici, Emanuel
Temko, Andriy
A method for AI assisted human interpretation of neonatal EEG
title A method for AI assisted human interpretation of neonatal EEG
title_full A method for AI assisted human interpretation of neonatal EEG
title_fullStr A method for AI assisted human interpretation of neonatal EEG
title_full_unstemmed A method for AI assisted human interpretation of neonatal EEG
title_short A method for AI assisted human interpretation of neonatal EEG
title_sort method for ai assisted human interpretation of neonatal eeg
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9243143/
https://www.ncbi.nlm.nih.gov/pubmed/35768501
http://dx.doi.org/10.1038/s41598-022-14894-4
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