Deep Learning in Coeliac Disease: A Systematic Review on Novel Diagnostic Approaches to Disease Diagnosis
Background: Coeliac disease affects approximately 1% of the global population with the diagnosis often relying on invasive and time-demanding methods. Deep learning, a powerful tool in medical science, shows potential for non-invasive, accurate coeliac disease diagnosis, though challenges remain. Ob...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-11-01
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| Series: | Journal of Clinical Medicine |
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| Online Access: | https://www.mdpi.com/2077-0383/12/23/7386 |
| _version_ | 1797399973560582144 |
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| author | Kassem Sharif Paula David Mahmud Omar Yousra Sharif Yonatan Shneor Patt Eyal Klang Adi Lahat |
| author_facet | Kassem Sharif Paula David Mahmud Omar Yousra Sharif Yonatan Shneor Patt Eyal Klang Adi Lahat |
| author_sort | Kassem Sharif |
| collection | DOAJ |
| description | Background: Coeliac disease affects approximately 1% of the global population with the diagnosis often relying on invasive and time-demanding methods. Deep learning, a powerful tool in medical science, shows potential for non-invasive, accurate coeliac disease diagnosis, though challenges remain. Objective: This systematic review aimed to evaluate the current state of deep-learning applications in coeliac disease diagnosis and identify potential areas for future research that could enhance diagnostic accuracy, sensitivity, and specificity. Methods: A systematic review was conducted using the following databases: PubMed, Embase, Web of Science, and Scopus. PRISMA guidelines were applied. Two independent reviewers identified research articles using deep learning for coeliac disease diagnosis and severity assessment. Only original research articles with performance metrics data were included. The quality of the diagnostic accuracy studies was assessed using the QUADAS-2 tool, categorizing studies based on risk of bias and concerns about applicability. Due to heterogeneity, a narrative synthesis was conducted to describe the applications and efficacy of the deep-learning techniques (DLT) in coeliac disease diagnosis. Results: The initial search across four databases yielded 417 studies with 195 being removed due to duplicity. Finally, eight studies were found to be suitable for inclusion after rigorous evaluation. They were all published between 2017 and 2023 and focused on using DLT for coeliac disease diagnosis or assessing disease severity. Different deep-learning architectures were applied. Accuracy levels ranged from 84% to 95.94% with the GoogLeNet model achieving 100% sensitivity and specificity for video capsule endoscopy images. Conclusions: DLT hold substantial potential in coeliac disease diagnosis. They offer improved accuracy and the prospect of mitigating clinician bias. However, key challenges persist, notably the requirement for more extensive and diverse datasets, especially to detect milder forms of coeliac disease. These methods are in their nascent stages, underscoring the need of integrating multiple data sources to achieve comprehensive coeliac disease diagnosis. |
| first_indexed | 2024-03-09T01:47:50Z |
| format | Article |
| id | doaj.art-591592134be44fe98877eaef1e94849a |
| institution | Directory Open Access Journal |
| issn | 2077-0383 |
| language | English |
| last_indexed | 2024-03-09T01:47:50Z |
| publishDate | 2023-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Clinical Medicine |
| spelling | doaj.art-591592134be44fe98877eaef1e94849a2023-12-08T15:19:43ZengMDPI AGJournal of Clinical Medicine2077-03832023-11-011223738610.3390/jcm12237386Deep Learning in Coeliac Disease: A Systematic Review on Novel Diagnostic Approaches to Disease DiagnosisKassem Sharif0Paula David1Mahmud Omar2Yousra Sharif3Yonatan Shneor Patt4Eyal Klang5Adi Lahat6Department of Gastroenterology, Sheba Medical Centre, Ramat Gan 52621, IsraelDepartment of Internal Medicine B, Sheba Medical Centre, Ramat Gan 52621, IsraelFaculty of Medicine, Tel Aviv University, Tel Aviv 69978, IsraelDepartment of Internal Medicine C, Haddasah Medical Centre, Hebrew University of Jerusalem, Jerusalem 9112102, IsraelDepartment of Internal Medicine B, Sheba Medical Centre, Ramat Gan 52621, IsraelFaculty of Medicine, Tel Aviv University, Tel Aviv 69978, IsraelDepartment of Gastroenterology, Sheba Medical Centre, Ramat Gan 52621, IsraelBackground: Coeliac disease affects approximately 1% of the global population with the diagnosis often relying on invasive and time-demanding methods. Deep learning, a powerful tool in medical science, shows potential for non-invasive, accurate coeliac disease diagnosis, though challenges remain. Objective: This systematic review aimed to evaluate the current state of deep-learning applications in coeliac disease diagnosis and identify potential areas for future research that could enhance diagnostic accuracy, sensitivity, and specificity. Methods: A systematic review was conducted using the following databases: PubMed, Embase, Web of Science, and Scopus. PRISMA guidelines were applied. Two independent reviewers identified research articles using deep learning for coeliac disease diagnosis and severity assessment. Only original research articles with performance metrics data were included. The quality of the diagnostic accuracy studies was assessed using the QUADAS-2 tool, categorizing studies based on risk of bias and concerns about applicability. Due to heterogeneity, a narrative synthesis was conducted to describe the applications and efficacy of the deep-learning techniques (DLT) in coeliac disease diagnosis. Results: The initial search across four databases yielded 417 studies with 195 being removed due to duplicity. Finally, eight studies were found to be suitable for inclusion after rigorous evaluation. They were all published between 2017 and 2023 and focused on using DLT for coeliac disease diagnosis or assessing disease severity. Different deep-learning architectures were applied. Accuracy levels ranged from 84% to 95.94% with the GoogLeNet model achieving 100% sensitivity and specificity for video capsule endoscopy images. Conclusions: DLT hold substantial potential in coeliac disease diagnosis. They offer improved accuracy and the prospect of mitigating clinician bias. However, key challenges persist, notably the requirement for more extensive and diverse datasets, especially to detect milder forms of coeliac disease. These methods are in their nascent stages, underscoring the need of integrating multiple data sources to achieve comprehensive coeliac disease diagnosis.https://www.mdpi.com/2077-0383/12/23/7386coeliac diseasedeep learningmachine learninginnovationdiagnosis |
| spellingShingle | Kassem Sharif Paula David Mahmud Omar Yousra Sharif Yonatan Shneor Patt Eyal Klang Adi Lahat Deep Learning in Coeliac Disease: A Systematic Review on Novel Diagnostic Approaches to Disease Diagnosis Journal of Clinical Medicine coeliac disease deep learning machine learning innovation diagnosis |
| title | Deep Learning in Coeliac Disease: A Systematic Review on Novel Diagnostic Approaches to Disease Diagnosis |
| title_full | Deep Learning in Coeliac Disease: A Systematic Review on Novel Diagnostic Approaches to Disease Diagnosis |
| title_fullStr | Deep Learning in Coeliac Disease: A Systematic Review on Novel Diagnostic Approaches to Disease Diagnosis |
| title_full_unstemmed | Deep Learning in Coeliac Disease: A Systematic Review on Novel Diagnostic Approaches to Disease Diagnosis |
| title_short | Deep Learning in Coeliac Disease: A Systematic Review on Novel Diagnostic Approaches to Disease Diagnosis |
| title_sort | deep learning in coeliac disease a systematic review on novel diagnostic approaches to disease diagnosis |
| topic | coeliac disease deep learning machine learning innovation diagnosis |
| url | https://www.mdpi.com/2077-0383/12/23/7386 |
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