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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: 30 November 2024 | Viewed by 994

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 (1 paper)

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Research

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
Viewed by 773
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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