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| Format: | Artículo Open Access |
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Wiley
2026
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| Online Access: | https://onlinelibrary.wiley.com/doi/10.1111/nmo.70310 |
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| author | Dylan Muggleston Greg O'Grady Chris Varghese Christopher Andrews |
| author_facet | Dylan Muggleston Greg O'Grady Chris Varghese Christopher Andrews Dylan Muggleston Greg O'Grady Chris Varghese Christopher Andrews |
| collection | Wiley Open Access |
| contents | Artificial Intelligence in Gastrointestinal Motility Diagnostics: A Systematic Review Dylan Muggleston Greg O'Grady Chris Varghese Christopher Andrews Neurogastroenterology & Motility ABSTRACT Background The assessment of gastrointestinal (GI) motility disorders is limited by the invasive, resource‐intensive, and subjective nature of current testing. Artificial Intelligence (AI) is being applied to address these challenges. This review aimed to comprehensively identify, appraise, and synthesize the spectrum of AI applications in GI motility diagnostics. Methods A systematic search of PubMed, Embase, Scopus, Web of Science, and Medline for original studies was conducted in March 2025. Results were narratively synthesized and grouped by anatomic region. Key Results Of 1383 articles, 90 primary studies met the inclusion criteria. In the esophagus ( n = 31), the primary focus was automation of High‐Resolution Esophageal Manometry (HREM), pH impedance, and Functional Luminal Imaging Probe (FLIP) panometry analysis. In the gastroduodenum ( n = 21), studies looked to improve diagnostic accuracy and interpretability of legacy electrogastrography (EGG). In the small and large intestine ( n = 19), AI was used to determine transit time and predict post‐operative ileus. In the anorectum ( n = 9), tools focused on standardizing manometry interpretation. Review of pan‐GI applications ( n = 10) highlighted AI's application to wireless capsule endoscopy and bowel sounds analysis. While promising, all areas need more rigorous research and external validation before widespread deployment. Conclusions and Inferences AI will play an essential role in improving and automating the interpretation of GI motility diagnostics. However, even the most promising models require large‐scale prospective validation before clinical implementation. Trial Registration PROSPERO (ID: 1128662) 10.1111/nmo.70310 http://creativecommons.org/licenses/by-nc/4.0/ |
| doi_str_mv | 10.1111/nmo.70310 |
| format | Artículo Open Access |
| id | wiley_oa_10_1111_nmo_70310 |
| institution | Wiley Open Access |
| license_str_mv | http://creativecommons.org/licenses/by-nc/4.0/ |
| publishDate | 2026 |
| publisher | Wiley |
| record_format | wiley_oa |
| spellingShingle | Artificial Intelligence in Gastrointestinal Motility Diagnostics: A Systematic Review Dylan Muggleston Greg O'Grady Chris Varghese Christopher Andrews Neurogastroenterology & Motility Artificial Intelligence in Gastrointestinal Motility Diagnostics: A Systematic Review Dylan Muggleston Greg O'Grady Chris Varghese Christopher Andrews Neurogastroenterology & Motility ABSTRACT Background The assessment of gastrointestinal (GI) motility disorders is limited by the invasive, resource‐intensive, and subjective nature of current testing. Artificial Intelligence (AI) is being applied to address these challenges. This review aimed to comprehensively identify, appraise, and synthesize the spectrum of AI applications in GI motility diagnostics. Methods A systematic search of PubMed, Embase, Scopus, Web of Science, and Medline for original studies was conducted in March 2025. Results were narratively synthesized and grouped by anatomic region. Key Results Of 1383 articles, 90 primary studies met the inclusion criteria. In the esophagus ( n = 31), the primary focus was automation of High‐Resolution Esophageal Manometry (HREM), pH impedance, and Functional Luminal Imaging Probe (FLIP) panometry analysis. In the gastroduodenum ( n = 21), studies looked to improve diagnostic accuracy and interpretability of legacy electrogastrography (EGG). In the small and large intestine ( n = 19), AI was used to determine transit time and predict post‐operative ileus. In the anorectum ( n = 9), tools focused on standardizing manometry interpretation. Review of pan‐GI applications ( n = 10) highlighted AI's application to wireless capsule endoscopy and bowel sounds analysis. While promising, all areas need more rigorous research and external validation before widespread deployment. Conclusions and Inferences AI will play an essential role in improving and automating the interpretation of GI motility diagnostics. However, even the most promising models require large‐scale prospective validation before clinical implementation. Trial Registration PROSPERO (ID: 1128662) 10.1111/nmo.70310 http://creativecommons.org/licenses/by-nc/4.0/ |
| title | Artificial Intelligence in Gastrointestinal Motility Diagnostics: A Systematic Review |
| topic | Neurogastroenterology & Motility |
| url | https://onlinelibrary.wiley.com/doi/10.1111/nmo.70310 |