Artificial Intelligence for Detecting and Quantifying Fatty Liver in Ultrasound Images: A Systematic Review
<div><p>Background: Non-alcoholic Fatty Liver Disease (NAFLD) is growing more prevalent worldwide. Although non-invasive diagnostic approaches such as conventional ultrasonography and clinical scoring systems have been proposed as alternatives to liver biopsy, their efficacy has been cal...
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2022
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| _version_ | 1864513518554841088 |
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| author | Fahad Muflih Alshagathrh (18427950) |
| author2 | Mowafa Said Househ (18427953) |
| author2_role | author |
| author_facet | Fahad Muflih Alshagathrh (18427950) Mowafa Said Househ (18427953) |
| author_role | author |
| dc.creator.none.fl_str_mv | Fahad Muflih Alshagathrh (18427950) Mowafa Said Househ (18427953) |
| dc.date.none.fl_str_mv | 2022-12-01T00:00:00Z |
| dc.identifier.none.fl_str_mv | 10.3390/bioengineering9120748 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Artificial_Intelligence_for_Detecting_and_Quantifying_Fatty_Liver_in_Ultrasound_Images_A_Systematic_Review/25672548 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Information and computing sciences Artificial intelligence Machine learning artificial intelligence deep learning machine learning fatty liver NAFLD ultrasound |
| dc.title.none.fl_str_mv | Artificial Intelligence for Detecting and Quantifying Fatty Liver in Ultrasound Images: A Systematic Review |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <div><p>Background: Non-alcoholic Fatty Liver Disease (NAFLD) is growing more prevalent worldwide. Although non-invasive diagnostic approaches such as conventional ultrasonography and clinical scoring systems have been proposed as alternatives to liver biopsy, their efficacy has been called into doubt. Artificial Intelligence (AI) is now combined with traditional diagnostic processes to improve the performance of non-invasive approaches. Objective: This study explores how well various AI methods function and perform on ultrasound (US) images to diagnose and quantify non-alcoholic fatty liver disease. Methodology: A systematic review was conducted to achieve this objective. Five science bibliographic databases were searched, including PubMed, Association for Computing Machinery ACM Digital Library, Institute of Electrical and Electronics Engineers IEEE Xplore, Scopus, and Google Scholar. Only peer-reviewed English articles, conferences, theses, and book chapters were included. Data from studies were synthesized using narrative methodologies per Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) criteria. Results: Forty-nine studies were included in the systematic review. According to the qualitative analysis, AI significantly enhanced the diagnosis of NAFLD, Non-Alcoholic Steatohepatitis (NASH), and liver fibrosis. In addition, modalities, image acquisition, feature extraction and selection, data management, and classifiers were assessed and compared in terms of performance measures (i.e., accuracy, sensitivity, and specificity). Conclusion: AI-supported systems show potential performance increases in detecting and quantifying steatosis, NASH, and liver fibrosis in NAFLD patients. Before real-world implementation, prospective studies with direct comparisons of AI-assisted modalities and conventional techniques are necessary.</p><p> </p></div><h2>Other Information</h2> <p> Published in: Bioengineering<br> License: <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.3390/bioengineering9120748" target="_blank">https://dx.doi.org/10.3390/bioengineering9120748</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_cd4aac3bb65a1579e957343ea0612405 |
| identifier_str_mv | 10.3390/bioengineering9120748 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/25672548 |
| publishDate | 2022 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Artificial Intelligence for Detecting and Quantifying Fatty Liver in Ultrasound Images: A Systematic ReviewFahad Muflih Alshagathrh (18427950)Mowafa Said Househ (18427953)Information and computing sciencesArtificial intelligenceMachine learningartificial intelligencedeep learningmachine learningfatty liverNAFLDultrasound<div><p>Background: Non-alcoholic Fatty Liver Disease (NAFLD) is growing more prevalent worldwide. Although non-invasive diagnostic approaches such as conventional ultrasonography and clinical scoring systems have been proposed as alternatives to liver biopsy, their efficacy has been called into doubt. Artificial Intelligence (AI) is now combined with traditional diagnostic processes to improve the performance of non-invasive approaches. Objective: This study explores how well various AI methods function and perform on ultrasound (US) images to diagnose and quantify non-alcoholic fatty liver disease. Methodology: A systematic review was conducted to achieve this objective. Five science bibliographic databases were searched, including PubMed, Association for Computing Machinery ACM Digital Library, Institute of Electrical and Electronics Engineers IEEE Xplore, Scopus, and Google Scholar. Only peer-reviewed English articles, conferences, theses, and book chapters were included. Data from studies were synthesized using narrative methodologies per Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) criteria. Results: Forty-nine studies were included in the systematic review. According to the qualitative analysis, AI significantly enhanced the diagnosis of NAFLD, Non-Alcoholic Steatohepatitis (NASH), and liver fibrosis. In addition, modalities, image acquisition, feature extraction and selection, data management, and classifiers were assessed and compared in terms of performance measures (i.e., accuracy, sensitivity, and specificity). Conclusion: AI-supported systems show potential performance increases in detecting and quantifying steatosis, NASH, and liver fibrosis in NAFLD patients. Before real-world implementation, prospective studies with direct comparisons of AI-assisted modalities and conventional techniques are necessary.</p><p> </p></div><h2>Other Information</h2> <p> Published in: Bioengineering<br> License: <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.3390/bioengineering9120748" target="_blank">https://dx.doi.org/10.3390/bioengineering9120748</a></p>2022-12-01T00:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.3390/bioengineering9120748https://figshare.com/articles/journal_contribution/Artificial_Intelligence_for_Detecting_and_Quantifying_Fatty_Liver_in_Ultrasound_Images_A_Systematic_Review/25672548CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/256725482022-12-01T00:00:00Z |
| spellingShingle | Artificial Intelligence for Detecting and Quantifying Fatty Liver in Ultrasound Images: A Systematic Review Fahad Muflih Alshagathrh (18427950) Information and computing sciences Artificial intelligence Machine learning artificial intelligence deep learning machine learning fatty liver NAFLD ultrasound |
| status_str | publishedVersion |
| title | Artificial Intelligence for Detecting and Quantifying Fatty Liver in Ultrasound Images: A Systematic Review |
| title_full | Artificial Intelligence for Detecting and Quantifying Fatty Liver in Ultrasound Images: A Systematic Review |
| title_fullStr | Artificial Intelligence for Detecting and Quantifying Fatty Liver in Ultrasound Images: A Systematic Review |
| title_full_unstemmed | Artificial Intelligence for Detecting and Quantifying Fatty Liver in Ultrasound Images: A Systematic Review |
| title_short | Artificial Intelligence for Detecting and Quantifying Fatty Liver in Ultrasound Images: A Systematic Review |
| title_sort | Artificial Intelligence for Detecting and Quantifying Fatty Liver in Ultrasound Images: A Systematic Review |
| topic | Information and computing sciences Artificial intelligence Machine learning artificial intelligence deep learning machine learning fatty liver NAFLD ultrasound |