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Diagnostics
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Application of Artificial Intelligence in Gastrointestinal Disease
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Application of Artificial Intelligence in Gastrointestinal Disease

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 4383

Special Issue Editor

Dr. Kai Zhao

E-Mail Website
Guest Editor
Department of Radiological Sciences, University of California, Los Angeles, CA 90024, USA
Interests: machine learning; medical image analysis; dynamic-contrast-enhanced MRI

Special Issue Information

Dear Colleagues,

In recent years, the synergy between computer vision and machine learning has emerged as a transformative force in the realm of medicine, revolutionizing the way that healthcare professionals diagnose, treat, and manage various medical conditions.

This Special Issue on the “Application of Artificial Intelligence in Gastrointestinal Disease” serves as a comprehensive platform with which to showcase the latest advances in AI-based gastrointestinal disease diagnosis, including, but not limited to, AI-based disease detection/segmentation, medical image enhancement, disease management and prognosis, radiomics and texture analyses, and 3D reconstruction as well as visualization.

Dr. Kai Zhao
Guest Editor

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

  • artificial intelligence
  • machine learning
  • gastrointestinal disease

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Published Papers (6 papers)

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Research

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14 pages, 13972 KiB  
Article
Rapid and Efficient Screening of Helicobacter pylori in Gastric Samples Stained with Warthin–Starry Using Deep Learning
by José Aneiros-Fernández, Pedro Montero Pavón, Natalia García Gómez, Rosa María Palo Prian, Ismael Sánchez García, Ana Isabel Romero Ortiz, Rodrigo López Castro, César Casado-Sánchez, Víctor Sánchez Turrión, Antonio Luna and Manuel Álvaro Berbís
Diagnostics 2025, 15(9), 1085; https://doi.org/10.3390/diagnostics15091085 - 24 Apr 2025
Viewed by 211
Abstract
Background/Objectives: Helicobacter pylori is a major risk factor for gastric cancer. The incidence and prevalence of the pathogen are increasing worldwide, urging novel approaches to reduce detection turnaround times. H. pylori diagnosis relies on histological examination of gastric biopsies, but interobserver variability considerably [...] Read more.
Background/Objectives: Helicobacter pylori is a major risk factor for gastric cancer. The incidence and prevalence of the pathogen are increasing worldwide, urging novel approaches to reduce detection turnaround times. H. pylori diagnosis relies on histological examination of gastric biopsies, but interobserver variability considerably impacts its identification. We present an algorithm combining a feature pyramid network and a ResNet architecture for automatic and rapid H. pylori detection in digitized Warthin–Starry-stained gastric biopsies. Methods: Whole-slide images were segmented into manually annotated smaller patches and segments containing stomach tissue were analyzed for the presence of Gram-negative bacteria. Patches classified as positive were examined to confirm the presence/absence of bacteria in contact with the gastric epithelial surface (H. pylori). Results: The algorithm exhibited 0.923 average precision and 0.982 average recall. The conducted efficiency study demonstrated that algorithm utilization significantly decreased (p < 0.001) diagnostic turnaround times for all participants (two pathologists, a pathology resident, a pathology technician, and a biotechnologist), observing an 88.13–91.76% time reduction. Implementation of the algorithm also improved diagnostic accuracy for the resident, technician, and biotechnologist, indicating that the tool remarkably supports less experienced personnel. Conclusions: We believe that the incorporation of our algorithm into pathology workflows will help standardize diagnostic protocols and drastically reduce H. pylori diagnostic turnaround times. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence in Gastrointestinal Disease)
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10 pages, 11728 KiB  
Article
Real-World Colonoscopy Video Integration to Improve Artificial Intelligence Polyp Detection Performance and Reduce Manual Annotation Labor
by Yuna Kim, Ji-Soo Keum, Jie-Hyun Kim, Jaeyoung Chun, Sang-Il Oh, Kyung-Nam Kim, Young-Hoon Yoon and Hyojin Park
Diagnostics 2025, 15(7), 901; https://doi.org/10.3390/diagnostics15070901 - 1 Apr 2025
Viewed by 345
Abstract
Background/Objectives: Artificial intelligence (AI) integration in colon polyp detection often exhibits high sensitivity but notably low specificity in real-world settings, primarily due to reliance on publicly available datasets alone. To address this limitation, we proposed a semi-automatic annotation method using real colonoscopy [...] Read more.
Background/Objectives: Artificial intelligence (AI) integration in colon polyp detection often exhibits high sensitivity but notably low specificity in real-world settings, primarily due to reliance on publicly available datasets alone. To address this limitation, we proposed a semi-automatic annotation method using real colonoscopy videos to enhance AI model performance and reduce manual labeling labor. Methods: An integrated AI model was trained and validated on 86,258 training images and 17,616 validation images. Model 1 utilized only publicly available datasets, while Model 2 additionally incorporated images obtained from real colonoscopy videos of patients through a semi-automatic annotation process, significantly reducing the labeling burden on expert endoscopists. Results: The integrated AI model (Model 2) significantly outperformed the public-dataset-only model (Model 1). At epoch 35, Model 2 achieved a sensitivity of 90.6%, a specificity of 96.0%, an overall accuracy of 94.5%, and an F1 score of 89.9%. All polyps in the test videos were successfully detected, demonstrating considerable enhancement in detection performance compared to the public-dataset-only model. Conclusions: Integrating real-world colonoscopy video data using semi-automatic annotation markedly improved diagnostic accuracy while potentially reducing the need for extensive manual annotation typically performed by expert endoscopists. However, the findings need validation through multicenter external datasets to ensure generalizability. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence in Gastrointestinal Disease)
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45 pages, 9440 KiB  
Article
Brain Morphometry and Cognitive Features in the Prediction of Irritable Bowel Syndrome
by Arvid Lundervold, Ben René Bjørsvik , Julie Billing , Birgitte Berentsen , Gülen Arslan Lied , Elisabeth K. Steinsvik , Trygve Hausken , Daniela M. Pfabigan  and Astri J. Lundervold 
Diagnostics 2025, 15(4), 470; https://doi.org/10.3390/diagnostics15040470 - 14 Feb 2025
Viewed by 606
Abstract
Background/Objectives: Irritable bowel syndrome (IBS) is a gut–brain disorder characterized by abdominal pain, altered bowel habits, and psychological distress. While brain–gut interactions are recognized in IBS pathophysiology, the relationship between brain morphometry, cognitive function, and clinical features remains poorly understood. The study aims [...] Read more.
Background/Objectives: Irritable bowel syndrome (IBS) is a gut–brain disorder characterized by abdominal pain, altered bowel habits, and psychological distress. While brain–gut interactions are recognized in IBS pathophysiology, the relationship between brain morphometry, cognitive function, and clinical features remains poorly understood. The study aims to conduct the following: (i) to replicate previous univariate morphometric findings in IBS patients and conduct software comparisons; (ii) to investigate whether multivariate analysis of brain morphometric measures and cognitive performance can distinguish IBS patients from healthy controls (HCs), and evaluate the importance of structural and cognitive features in this discrimination. Methods: We studied 49 IBS patients and 29 HCs using structural brain magnetic resonance images (MRIs) and the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS). Brain morphometry was analyzed using FreeSurfer v6.0.1 and v7.4.1, with IBS severity assessed via the IBS-Severity Scoring System. We employed univariate, multivariate, and machine learning approaches with cross-validation. Results: The FreeSurfer version comparison revealed substantial variations in morphometric measurements, while morphometric measures alone showed limited discrimination between groups; combining morphometric and cognitive measures achieved 93% sensitivity in identifying IBS patients (22% specificity). The feature importance analysis highlighted the role of subcortical structures (the hippocampus, caudate, and putamen) and cognitive domains (recall and verbal skills) in group discrimination. Conclusions: Our comprehensive open-source framework suggests that combining brain morphometry and cognitive measures improves IBS-HC discrimination compared to morphometric measures alone. The importance of subcortical structures and specific cognitive domains supports complex brain–gut interaction in IBS, emphasizing the need for multimodal approaches and rigorous methodological considerations. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence in Gastrointestinal Disease)
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15 pages, 7781 KiB  
Article
Validation of Artificial Intelligence Computer-Aided Detection on Gastric Neoplasm in Upper Gastrointestinal Endoscopy
by Hannah Lee, Jun-Won Chung, Sung-Cheol Yun, Sung Woo Jung, Yeong Jun Yoon, Ji Hee Kim, Boram Cha, Mohd Azzam Kayasseh and Kyoung Oh Kim
Diagnostics 2024, 14(23), 2706; https://doi.org/10.3390/diagnostics14232706 - 30 Nov 2024
Cited by 1 | Viewed by 935
Abstract
Background/Objectives: Gastric cancer ranks fifth for incidence and fourth in the leading causes of mortality worldwide. In this study, we aimed to validate previously developed artificial intelligence (AI) computer-aided detection (CADe) algorithm, called ALPHAON® in detecting gastric neoplasm. Methods: We used the [...] Read more.
Background/Objectives: Gastric cancer ranks fifth for incidence and fourth in the leading causes of mortality worldwide. In this study, we aimed to validate previously developed artificial intelligence (AI) computer-aided detection (CADe) algorithm, called ALPHAON® in detecting gastric neoplasm. Methods: We used the retrospective data of 500 still images, including 5 benign gastric ulcers, 95 with gastric cancer, and 400 normal images. Thereby we validated the CADe algorithm measuring accuracy, sensitivity, and specificity with the result of receiver operating characteristic curves (ROC) and area under curve (AUC) in addition to comparing the diagnostic performance status of four expert endoscopists, four trainees, and four beginners from two university-affiliated hospitals with CADe algorithm. After a washing-out period of over 2 weeks, endoscopists performed gastric detection on the same dataset of the 500 endoscopic images again marked by ALPHAON®. Results: The CADe algorithm presented high validity in detecting gastric neoplasm with accuracy (0.88, 95% CI: 0.85 to 0.91), sensitivity (0.93, 95% CI: 0.88 to 0.98), specificity (0.87, 95% CI: 0.84 to 0.90), and AUC (0.962). After a washing-out period of over 2 weeks, overall validity improved in the trainee and beginner groups with the assistance of ALPHAON®. Significant improvement was present, especially in the beginner group (accuracy 0.94 (0.93 to 0.96) p < 0.001, sensitivity 0.87 (0.82 to 0.92) p < 0.001, specificity 0.96 (0.95 to 0.97) p < 0.001). Conclusions: The high validation performance state of the CADe algorithm system was verified. Also, ALPHAON® has demonstrated its potential to serve as an endoscopic educator for beginners improving and making progress in sensitivity and specificity. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence in Gastrointestinal Disease)
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13 pages, 3224 KiB  
Article
Prediction of Bone Marrow Metastases Using Computed Tomography (CT) Radiomics in Patients with Gastric Cancer: Uncovering Invisible Metastases
by Jiwoo Park, Minkyu Jung, Sang Kyum Kim and Young Han Lee
Diagnostics 2024, 14(15), 1689; https://doi.org/10.3390/diagnostics14151689 - 5 Aug 2024
Cited by 1 | Viewed by 1686
Abstract
We investigated whether radiomics of computed tomography (CT) image data enables the differentiation of bone metastases not visible on CT from unaffected bone, using pathologically confirmed bone metastasis as the reference standard, in patients with gastric cancer. In this retrospective study, 96 patients [...] Read more.
We investigated whether radiomics of computed tomography (CT) image data enables the differentiation of bone metastases not visible on CT from unaffected bone, using pathologically confirmed bone metastasis as the reference standard, in patients with gastric cancer. In this retrospective study, 96 patients (mean age, 58.4 ± 13.3 years; range, 28–85 years) with pathologically confirmed bone metastasis in iliac bones were included. The dataset was categorized into three feature sets: (1) mean and standard deviation values of attenuation in the region of interest (ROI), (2) radiomic features extracted from the same ROI, and (3) combined features of (1) and (2). Five machine learning models were developed and evaluated using these feature sets, and their predictive performance was assessed. The predictive performance of the best-performing model in the test set (based on the area under the curve [AUC] value) was validated in the external validation group. A Random Forest classifier applied to the combined radiomics and attenuation dataset achieved the highest performance in predicting bone marrow metastasis in patients with gastric cancer (AUC, 0.96), outperforming models using only radiomics or attenuation datasets. Even in the pathology-positive CT-negative group, the model demonstrated the best performance (AUC, 0.93). The model’s performance was validated both internally and with an external validation cohort, consistently demonstrating excellent predictive accuracy. Radiomic features derived from CT images can serve as effective imaging biomarkers for predicting bone marrow metastasis in patients with gastric cancer. These findings indicate promising potential for their clinical utility in diagnosing and predicting bone marrow metastasis through routine evaluation of abdominopelvic CT images during follow-up. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence in Gastrointestinal Disease)
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Review

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19 pages, 643 KiB  
Review
Advancing Colorectal Cancer Diagnostics from Barium Enema to AI-Assisted Colonoscopy
by Dumitru-Dragos Chitca, Valentin Popescu, Anca Dumitrescu, Cristian Botezatu and Bogdan Mastalier
Diagnostics 2025, 15(8), 974; https://doi.org/10.3390/diagnostics15080974 - 11 Apr 2025
Viewed by 337
Abstract
Colorectal cancer (CRC) remains a major global health burden, necessitating continuous advancements in diagnostic methodologies. Traditional screening techniques, including barium enema and fecal occult blood tests, have been progressively replaced by more precise modalities, such as colonoscopy, liquid biopsy, and artificial intelligence (AI)-assisted [...] Read more.
Colorectal cancer (CRC) remains a major global health burden, necessitating continuous advancements in diagnostic methodologies. Traditional screening techniques, including barium enema and fecal occult blood tests, have been progressively replaced by more precise modalities, such as colonoscopy, liquid biopsy, and artificial intelligence (AI)-assisted imaging. Objective: This review explores the evolution of CRC diagnostic tools, from conventional imaging methods to cutting-edge AI-driven approaches, emphasizing their clinical utility, cost-effectiveness, and integration into multidisciplinary healthcare settings. Methods: A comprehensive literature search was conducted using the PubMed, Medline, and Scopus databases, selecting studies that evaluate various CRC diagnostic tools, including endoscopic advancements, liquid biopsy applications, and AI-assisted imaging techniques. Key inclusion criteria include studies on diagnostic accuracy, sensitivity, specificity, clinical outcomes, and economic feasibility. Results: AI-assisted colonoscopy has demonstrated superior adenoma detection rates (ADR), reduced interobserver variability, and enhanced real-time lesion classification, offering a cost-effective alternative to liquid biopsy, particularly in high-volume healthcare institutions. While liquid biopsy provides a non-invasive means of molecular profiling, it remains cost-intensive and requires frequent testing, making it more suitable for post-treatment surveillance and high-risk patient monitoring. Conclusions: The future of CRC diagnostics lies in a hybrid model, leveraging AI-assisted endoscopic precision with molecular insights from liquid biopsy. This integration is expected to revolutionize early detection, risk stratification, and personalized treatment approaches, ultimately improving patient outcomes and healthcare efficiency. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence in Gastrointestinal Disease)
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