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The use of artificial intelligence systems in diagnosis of pneumonia via signs and symptoms : a systematic review
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Stokes, Katy, Castaldo, Rossana, Federici, Carlo, Pagliara, Silvio, Maccaro, Alessia, Cappuccio, Francesco, Fico, Giuseppe, Salvatore, Marco, Franzese, Monica and Pecchia, Leandro (2022) The use of artificial intelligence systems in diagnosis of pneumonia via signs and symptoms : a systematic review. Biomedical Signal Processing and Control, 72 (Part A). 103325. doi:10.1016/j.bspc.2021.103325 ISSN 1746-8094.
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Official URL: https://doi.org/10.1016/j.bspc.2021.103325
Abstract
Artificial Intelligence (AI) systems using symptoms/signs to detect respiratory diseases may improve diagnosis especially in limited resource settings. Heterogeneity in such AI systems creates an ongoing need to analyse performance to inform future research. This systematic literature review aimed to investigate performance and reporting of diagnostic AI systems using machine learning (ML) for pneumonia detection based on symptoms and signs, and to provide recommendations on best practices for designing and implementing predictive ML algorithms. This article was conducted following the PRISMA protocol, 876 articles were identified by searching PubMed, Scopus, and OvidSP databases (last search 5th May 2021). For inclusion, studies must have differentiated clinically diagnosed pneumonia from controls or other diseases using AI. Risk of Bias was evaluated using The STARD 2015 tool. Information was extracted from 16 included studies regarding study characteristics, ML-model features, reference tests, study population, accuracy measures and ethical aspects. All included studies were highly heterogenous concerning the study design, setting of diagnosis, study population and ML algorithm. Study reporting quality in methodology and results was low. Ethical issues surrounding design and implementation of the AI algorithms were not well explored. Although no single performance measure was used in all studies, most reported an accuracy measure over 90%. There is strong evidence to support further investigations of ML to automatically detect pneumonia based on easily recognisable symptoms and signs. To help improve the efficacy of future research, recommendations for designing and implementing AI tools based on the findings of this study are provided.
Item Type: | Journal Article | ||||||||
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Divisions: | Faculty of Science, Engineering and Medicine > Medicine > Warwick Medical School | ||||||||
Journal or Publication Title: | Biomedical Signal Processing and Control | ||||||||
Publisher: | Elsevier BV | ||||||||
ISSN: | 1746-8094 | ||||||||
Official Date: | February 2022 | ||||||||
Dates: |
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Volume: | 72 | ||||||||
Number: | Part A | ||||||||
Article Number: | 103325 | ||||||||
DOI: | 10.1016/j.bspc.2021.103325 | ||||||||
Status: | Peer Reviewed | ||||||||
Publication Status: | Published | ||||||||
Access rights to Published version: | Open Access (Creative Commons) | ||||||||
Date of first compliant deposit: | 9 November 2021 | ||||||||
Date of first compliant Open Access: | 22 November 2021 | ||||||||
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