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Diagnostics
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Applications of Artificial Intelligence in Gastrointestinal Diseases
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Applications of Artificial Intelligence in Gastrointestinal Diseases

A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Machine Learning and Artificial Intelligence in Diagnostics".

Deadline for manuscript submissions: 31 August 2025 | Viewed by 3413

Special Issue Editors

Dr. Dimitra D. Dionysiou

E-Mail Website
Guest Editor
Health Technologies Division, i-SENSE Group, Institute of Communication and Computer Systems, National Technical University of Athens, Athens, Greece
Interests: mechanistic and machine learning models in healthcare; decision support systems for personalized medicine and public health
Dr. Ioannis Gallos

E-Mail Website
Guest Editor
Health Technologies Division, i-SENSE Group, Institute of Communication and Computer Systems, National Technical University of Athens, Athens, Greece
Interests: system dynamics, time series analysis, machine, manifold and deep learning applications for medical diagnosis and public health

Special Issue Information

Dear Colleagues,

In recent years, significant progress in gastroenterology has been fueled by the integration of artificial intelligence (AI) technologies. From AI-powered diagnosis and prognosis to personalized treatment, AI holds significant potential in revolutionizing the screening, diagnosis, management, and prevention of gastrointestinal diseases.

This Special Issue aims to explore the latest developments, challenges, and opportunities in relevant AI applications, including the integration of multiple heterogeneous data sources such as multi-omics; the identification of complex patterns and interactions among multiple biomarkers; the exploration of causal inference and intricate relationships between multiple factors and health outcomes; the provision of deeper insights into the disease through explainable AI; and the prediction of the risk of disease manifestation or relapse.

We invite experts to contribute original research articles, reviews, and perspectives on various aspects of AI applications in gastrointestinal diseases. The scope of interest for this Special Issue includes, but is not limited to, the following:

  • AI-driven diagnostic tools for the early detection of gastrointestinal conditions;
  • Non- or minimally invasive screening methodologies;
  • Machine learning algorithms for predicting disease progression and treatment outcomes;
  • Comparative effectiveness research using AI to evaluate different diagnostic and therapeutic approaches in gastrointestinal disease;
  • AI applications incorporating the gut microbiome and gut–brain axis for the assessment and management of functional gastrointestinal disease;
  • AI-powered exposomics and management of gastrointestinal diseases;
  • Explainable AI in gastrointestinal pathology;
  • Virtual reality and augmented reality applications in gastrointestinal endoscopy and training;
  • Ethical considerations and challenges in the integration of AI into gastroenterology clinical practice;

We look forward to receiving your valuable contributions.

Dr. Dimitra D. Dionysiou
Dr. Ioannis Gallos
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Diagnostics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • gastrointestinal disease
  • artificial intelligence
  • biomarkers
  • screening/diagnosis
  • personalized medicine
  • treatment outcome
  • pathogenesis
  • risk management

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Published Papers (3 papers)

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Research

13 pages, 449 KiB  
Article
PolyDeep Advance 1: Clinical Validation of a Computer-Aided Detection System for Colorectal Polyp Detection with a Second Observer Design
by Pedro Davila-Piñón, Teresa Pedrido, Astrid Irene Díez-Martín, Jesús Herrero, Manuel Puga, Laura Rivas, Eloy Sánchez, Sara Zarraquiños, Noel Pin, Pablo Vega, Santiago Soto, David Remedios, Rubén Domínguez-Carbajales, Florentino Fdez-Riverola, Alba Nogueira-Rodríguez, Daniel Glez-Peña, Miguel Reboiro-Jato, Hugo López-Fernández and Joaquín Cubiella
Diagnostics 2025, 15(4), 458; https://doi.org/10.3390/diagnostics15040458 - 13 Feb 2025
Viewed by 588
Abstract
Background: PolyDeep is a computer-aided detection and characterization system that has demonstrated a high diagnostic yield for in vitro detection of colorectal polyps. Our objective is to compare the diagnostic performance of expert endoscopists and PolyDeep for colorectal polyp detection. Methods: PolyDeep Advance [...] Read more.
Background: PolyDeep is a computer-aided detection and characterization system that has demonstrated a high diagnostic yield for in vitro detection of colorectal polyps. Our objective is to compare the diagnostic performance of expert endoscopists and PolyDeep for colorectal polyp detection. Methods: PolyDeep Advance 1 (NCT05514301) is an unicentric diagnostic test study with a second observer design. Endoscopists performed colonoscopy blinded to PolyDeep’s detection results. The main endpoint was the sensitivity for colorectal polyp (adenoma, serrated or hyperplastic lesion) detection. The secondary endpoints were the diagnostic performance for diminutive lesions (≤5 mm), neoplasia (adenoma, serrated lesion) and adenoma detection. Results: We included 205 patients (55.1% male, 63.0 ± 6.2 years of age) referred to colonoscopy (positive faecal immunochemical occult blood test = 60.5%, surveillance colonoscopy = 39.5%). We excluded eight patients due to incomplete colonoscopy. Endoscopists detected 384 lesions, of which 39 were not detected by PolyDeep. In contrast, PolyDeep predicted 410 possible additional lesions, 26 of these predictions confirmed by endoscopists as lesions, resulting in a potential 6.8% detection increase with respect to the 384 lesions detected by the endoscopists. In total, 410 lesions were detected, 20 were not retrieved, five were colorectal adenocarcinoma, 343 were colorectal polyps (231 adenomas, 39 serrated and 73 hyperplastic polyps), 42 were normal mucosa and 289 were ≤5 mm. We did not find statistically significant differences between endoscopists and PolyDeep for colorectal polyp detection (Sensitivity = 94.2%, 91.5%, p = 0.2; Specificity = 9.5%, 14.3%, p = 0.7), diminutive lesions (Sensitivity = 92.3%, 89.5%, p = 0.4; Specificity = 9.8%, 14.6%, p = 0.7), neoplasia (Sensitivity = 95.2%, 92.9%, p = 0.3; Specificity = 9.6%, 13.9%, p = 0.4) and adenoma detection (Sensitivity = 94.4%, 92.6%, p = 0.5; Specificity = 7.2%, 11.8%, p = 0.2). Conclusions: Expert endoscopists and PolyDeep have similar diagnostic performance for colorectal polyp detection. Full article
(This article belongs to the Special Issue Applications of Artificial Intelligence in Gastrointestinal Diseases)
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12 pages, 4157 KiB  
Article
Validation of Artificial Intelligence Computer-Aided Detection of Colonic Neoplasm in Colonoscopy
by Hannah Lee, Jun-Won Chung, Kyoung Oh Kim, Kwang An Kwon, Jung Ho Kim, Sung-Cheol Yun, Sung Woo Jung, Ahmad Sheeraz, Yeong Jun Yoon, Ji Hee Kim and Mohd Azzam Kayasseh
Diagnostics 2024, 14(23), 2762; https://doi.org/10.3390/diagnostics14232762 - 8 Dec 2024
Viewed by 856
Abstract
Background/Objectives: Controlling colonoscopic quality is important in the detection of colon polyps during colonoscopy as it reduces the overall long-term colorectal cancer risk. Artificial intelligence has recently been introduced in various medical fields. In this study, we aimed to validate a previously developed [...] Read more.
Background/Objectives: Controlling colonoscopic quality is important in the detection of colon polyps during colonoscopy as it reduces the overall long-term colorectal cancer risk. Artificial intelligence has recently been introduced in various medical fields. In this study, we aimed to validate a previously developed artificial intelligence (AI) computer-aided detection (CADe) algorithm called ALPHAON® and compare outcomes with previous studies that showed that AI outperformed and assisted endoscopists of diverse levels of expertise in detecting colon polyps. Methods: We used the retrospective data of 500 still images, including 100 polyp images and 400 healthy colon images. In addition, we validated the CADe algorithm and compared its diagnostic performance with that of two expert endoscopists and six trainees from Gachon University Gil Medical Center. After a washing-out period of over 2 weeks, endoscopists performed polyp detection on the same dataset with the assistance of ALPHAON®. Results: The CADe algorithm presented a high capability in detecting colon polyps, with an accuracy of 0.97 (95% CI: 0.96 to 0.99), sensitivity of 0.91 (95% CI: 0.85 to 0.97), specificity of 0.99 (95% CI: 0.97 to 0.99), and AUC of 0.967. When evaluating and comparing the polyp detection ability of ALPHAON® with that of endoscopists with different levels of expertise (regarding years of endoscopic experience), it was found that ALPHAON® outperformed the experts in accuracy (0.97, 95% CI: 0.96 to 0.99), sensitivity (0.91, 95% CI: 0.85 to 0.97), and specificity (0.99, 95% CI: 0.97 to 0.99). After a washing-out period of over 2 weeks, the overall capability significantly improved for both experts and trainees with the assistance of ALPHAON®. Conclusions: The high performance of the CADe algorithm system in colon polyp detection during colonoscopy was verified. The sensitivity of ALPHAON® led to it outperforming the experts, and it demonstrated the ability to enhance the polyp detection ability of both experts and trainees, which suggests a significant possibility of ALPHAON® being able to provide endoscopic assistance. Full article
(This article belongs to the Special Issue Applications of Artificial Intelligence in Gastrointestinal Diseases)
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10 pages, 1413 KiB  
Article
Machine Learning Models for Predicting Mortality in Patients with Cirrhosis and Acute Upper Gastrointestinal Bleeding at an Emergency Department: A Retrospective Cohort Study
by Shih-Chien Tsai, Ching-Heng Lin, Cheng-C. J. Chu, Hsiang-Yun Lo, Chip-Jin Ng, Chun-Chuan Hsu and Shou-Yen Chen
Diagnostics 2024, 14(17), 1919; https://doi.org/10.3390/diagnostics14171919 - 30 Aug 2024
Cited by 1 | Viewed by 1361
Abstract
Background: Cirrhosis is a major global cause of mortality, and upper gastrointestinal (GI) bleeding significantly increases the mortality risk in these patients. Although scoring systems such as the Child–Pugh score and the Model for End-stage Liver Disease evaluate the severity of cirrhosis, none [...] Read more.
Background: Cirrhosis is a major global cause of mortality, and upper gastrointestinal (GI) bleeding significantly increases the mortality risk in these patients. Although scoring systems such as the Child–Pugh score and the Model for End-stage Liver Disease evaluate the severity of cirrhosis, none of these systems specifically target the risk of mortality in patients with upper GI bleeding. In this study, we constructed machine learning (ML) models for predicting mortality in patients with cirrhosis and upper GI bleeding, particularly in emergency settings, to achieve early intervention and improve outcomes. Methods: In this retrospective study, we analyzed the electronic health records of adult patients with cirrhosis who presented at an emergency department (ED) with GI bleeding between 2001 and 2019. Data were divided into training and testing sets at a ratio of 90:10. The ability of three ML models—a linear regression model, an XGBoost (XGB) model, and a three-layer neural network model—to predict mortality in the patients was evaluated. Results: A total of 16,025 patients with cirrhosis and 32,826 ED visits for upper GI bleeding were included in the study. The in-hospital and ED mortality rates were 11.2% and 2.2%, respectively. The XGB model exhibited the highest performance in predicting both in-hospital and ED mortality (area under the receiver operating characteristic curve: 0.866 and 0.861, respectively). International normalized ratio, renal function, red blood cell distribution width, age, and white blood cell count were the strongest predictors in all the ML models. The median ED length of stay for the ED mortality group was 17.54 h (7.16–40.01 h). Conclusions: ML models can be used to predict mortality in patients with cirrhosis and upper GI bleeding. Of the three models, the XGB model exhibits the highest performance. Further research is required to determine the actual efficacy of our ML models in clinical settings. Full article
(This article belongs to the Special Issue Applications of Artificial Intelligence in Gastrointestinal Diseases)
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