AI-Powered Detection of Functional Gastrointestinal Disorders in Adults Visiting Tertiary Hospitals

Muhammad Essa Khan; Mahnoor Naeem Rana; Khalid Bilal Khan; Laiba Mushtaq; Turfa Asghar; Wajeeha Ahmadani; Muhammad Zohaib Wahid

doi:10.61919/m0nr1z88

Authors

Muhammad Essa Khan Senior Registrar, Department of Gastroenterology, Sandeman Provincial Hospital, Quetta, Pakistan Author
Mahnoor Naeem Rana MBBS Graduate, CMH Lahore Medical College, Lahore, Pakistan Author
Khalid Bilal Khan Consultant Gastroenterologist and Hepatologist, POF Hospital, Wah Cantt, Rawalpindi, Pakistan Author https://orcid.org/0009-0005-8222-7122
Laiba Mushtaq Research Associate, National Institute for Biotechnology and Genetic Engineering (NIBGE-C PIEAS), Faisalabad, Pakistan Author
Turfa Asghar MScN, BScN, NL, RN, Shifa Tameer-e-Millat University, Islamabad, Pakistan Author
Wajeeha Ahmadani Final Year MBBS Student, People's University of Medical and Health Sciences, Nawabshah, Pakistan Author
Muhammad Zohaib Wahid Second Year MBBS Student, Multan Medical and Dental College, Multan, Pakistan Author

DOI:

https://doi.org/10.61919/m0nr1z88

Keywords:

Artificial intelligence, functional gastrointestinal disorders, irritable bowel syndrome, functional dyspepsia, functional constipation, machine learning, natural language processing, diagnostic accuracy

Abstract

Background: Functional gastrointestinal disorders are common in tertiary gastroenterology practice and remain difficult to diagnose because they are defined largely by symptom patterns rather than structural or biochemical abnormalities. In busy referral settings, overlap among irritable bowel syndrome, functional dyspepsia, functional constipation, and non-functional gastrointestinal conditions can reduce diagnostic consistency. Artificial intelligence offers a potential solution by integrating structured clinical variables with free-text symptom narratives to support more standardized classification. Objective: To evaluate the diagnostic accuracy of an artificial intelligence–based system for detecting functional gastrointestinal disorders among adults attending tertiary hospitals. Methods: This multicenter cross-sectional diagnostic accuracy study was conducted over eight months in three tertiary hospitals in Lahore, Pakistan. A total of 420 adults aged 18–65 years presenting with gastrointestinal symptoms were enrolled through consecutive sampling. After exclusion of relevant organic pathology, Rome IV–based clinician diagnosis served as the reference standard. The artificial intelligence system analyzed structured clinical variables and unstructured consultation notes using supervised machine learning and natural language processing to classify irritable bowel syndrome, functional dyspepsia, functional constipation, and non-functional gastrointestinal disorder status. Diagnostic performance was assessed using sensitivity, specificity, predictive values, area under the receiver operating characteristic curve, and Cohen’s kappa. Results: The mean age was 37.4 ± 10.8 years, and 54.8% of participants were male. Clinically, 33.3% had irritable bowel syndrome, 23.8% functional dyspepsia, 14.3% functional constipation, and 28.6% non-functional gastrointestinal disorders. The artificial intelligence model achieved a sensitivity of 88.2%, specificity of 84.5%, positive predictive value of 86.9%, negative predictive value of 86.0%, overall accuracy of 87.1%, and an overall area under the curve of 0.912. Agreement with clinician diagnosis was strong (κ = 0.83, p < 0.001). Conclusion: The artificial intelligence system showed high diagnostic performance and strong concordance with Rome IV–based clinical diagnosis, supporting its potential role as an adjunctive decision-support tool for functional gastrointestinal disorder classification in tertiary care.

References

1. Wang Q, Xu J, Wang A, Chen Y, Wang T, Chen D, et al. Systematic review of machine learning-based radiomics approach for predicting microsatellite instability status in colorectal cancer. Radiol Med. 2023;128(2):136-148.

2. Stanzione A, Verde F, Romeo V, Boccadifuoco F, Mainenti PP, Maurea S. Radiomics and machine learning applications in rectal cancer: current update and future perspectives. World J Gastroenterol. 2021;27(32):5306-5321.

3. Xie X, Xiao YF, Yang H, Peng X, Li JJ, Zhou YY, et al. A new artificial intelligence system for both stomach and small-bowel capsule endoscopy. Gastrointest Endosc. 2024;100(5):878.e1-878.e14.

4. Tunali V, Arslan N, Ermiş BH, Derviş Hakim G, Gündoğdu A, Hora M, et al. A multicenter randomized controlled trial of microbiome-based artificial intelligence-assisted personalized diet vs low-fermentable oligosaccharides, disaccharides, monosaccharides, and polyols diet: a novel approach for the management of irritable bowel syndrome. Am J Gastroenterol. 2024;119(9):1901-1912.

5. Baumgartner M, Lang M, Holley H, Crepaz D, Hausmann B, Pjevac P, et al. Mucosal biofilms are an endoscopic feature of irritable bowel syndrome and ulcerative colitis. Gastroenterology. 2021;161(4):1245-1256.e20.

6. Jiang X, Zhao H, Saldanha OL, Nebelung S, Kuhl C, Amygdalos I, et al. An MRI deep learning model predicts outcome in rectal cancer. Radiology. 2023;307(5):e222223.

7. Nguyen NH, Picetti D, Dulai PS, Jairath V, Sandborn WJ, Ohno-Machado L, et al. Machine learning-based prediction models for diagnosis and prognosis in inflammatory bowel diseases: a systematic review. J Crohns Colitis. 2022;16(3):398-413.

8. Javaid A, Shahab O, Adorno W, Fernandes P, May E, Syed S. Machine learning predictive outcomes modeling in inflammatory bowel diseases. Inflamm Bowel Dis. 2022;28(6):819-829.

9. Jung JO, Crnovrsanin N, Wirsik NM, Nienhüser H, Peters L, Popp F, et al. Machine learning for optimized individual survival prediction in resectable upper gastrointestinal cancer. J Cancer Res Clin Oncol. 2023;149(5):1691-1702.

10. Tariq R, Dilmaghani S. Machine learning and radiomics: changing the horizon of Crohn's disease assessment. Inflamm Bowel Dis. 2024;30(10):1919-1921.

11. Kuntz S, Krieghoff-Henning E, Kather JN, Jutzi T, Höhn J, Kiehl L, et al. Gastrointestinal cancer classification and prognostication from histology using deep learning: systematic review. Eur J Cancer. 2021;155:200-215.

12. Nehme F, Feldman K. Evolving role and future directions of natural language processing in gastroenterology. Dig Dis Sci. 2021;66(1):29-40.

13. Wu L, Shang R, Sharma P, Zhou W, Liu J, Yao L, et al. Effect of a deep learning-based system on the miss rate of gastric neoplasms during upper gastrointestinal endoscopy: a single-centre, tandem, randomised controlled trial. Lancet Gastroenterol Hepatol. 2021;6(9):700-708.

14. Naz J, Sharif M, Yasmin M, Raza M, Khan MA. Detection and classification of gastrointestinal diseases using machine learning. Curr Med Imaging. 2021;17(4):479-490.

15. Gong EJ, Bang CS, Lee JJ, Baik GH, Lim H, Jeong JH, et al. Deep learning-based clinical decision support system for gastric neoplasms in real-time endoscopy: development and validation study. Endoscopy. 2023;55(8):701-708.

16. Yan T, Wong PK, Qin YY. Deep learning for diagnosis of precancerous lesions in upper gastrointestinal endoscopy: a review. World J Gastroenterol. 2021;27(20):2531-2544.

17. Wong PK, Chan IN, Yan HM, Gao S, Wong CH, Yan T, et al. Deep learning based radiomics for gastrointestinal cancer diagnosis and treatment: a minireview. World J Gastroenterol. 2022;28(45):6363-6379.

18. Zhang Y, Zhang Z, Wei L, Wei S. Construction and validation of nomograms combined with novel machine learning algorithms to predict early death of patients with metastatic colorectal cancer. Front Public Health. 2022;10:1008137.

19. Shi Q, Hao Y, Liu H, Liu X, Yan W, Mao J, et al. Computed tomography enterography radiomics and machine learning for identification of Crohn's disease. BMC Med Imaging. 2024;24(1):302.

20. Zhou S, Xie Y, Feng X, Li Y, Shen L, Chen Y. Artificial intelligence in gastrointestinal cancer research: image learning advances and applications. Cancer Lett. 2025;614:217555.

21. Ramoni D, Scuricini A, Carbone F, Liberale L, Montecucco F. Artificial intelligence in gastroenterology: ethical and diagnostic challenges in clinical practice. World J Gastroenterol. 2025;31(10):102725.

22. Matsubayashi CO, Cheng S, Hulchafo I, Zhang Y, Tada T, Buxbaum JL, et al. Artificial intelligence for gastric cancer in endoscopy: from diagnostic reasoning to market. Dig Liver Dis. 2024;56(7):1156-1163.

23. Issaiy M, Zarei D, Saghazadeh A. Artificial intelligence and acute appendicitis: a systematic review of diagnostic and prognostic models. World J Emerg Surg. 2023;18(1):59.