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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...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2022
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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. |
format | Online Article Text |
id | pubmed-9243143 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
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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