Special Issue "Applications of Artificial Intelligence for the Diagnosis of 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: closed (31 October 2022) | Viewed by 830

Special Issue Editors

Dr. Silvia Pecere
E-Mail Website
Guest Editor
Fondazione Policlinico Universitario Agostino Gemelli IRCCS, Rome, Italy
Interests: gastroenterology; digestive endoscopy
Dr. Debesh Jha
E-Mail Website
Co-Guest Editor
Department of Radiology, Northwestern University, Chicago, IL, USA
Interests: medical image analysis; deep learning; computer vision; colonoscopy; gastrointestinal endoscopy; wireless capsule endoscopy; surgical data science; radiation oncology; prostrate and lung caner; generalizable machine learning models; robust and trustworthy AI systems; transparent system

Special Issue Information

Dear Colleagues,

In recent years, the development of convolutional neural networks (CNN) has achieved impressive advances within the field of machine learning, leading to an increase in the use of artificial intelligence (AI) in the field of gastrointestinal (GI) diseases. AI networks have been trained for the detection of gastrointestinal lesions, but also for their characterization, obtaining excellent results and having a high overall diagnostic accuracy. Moreover, in recent decades, a great deal of attention has been focused on the development of AI systems for application in radiology and pathology for the improvement of the diagnosis, treatment, and prognosis of many gastrointestinal diseases. Nevertheless, the real usefulness and application of AI systems in the gastroenterology field and high-quality studies comparing the performance of AI networks to health care professionals are still limited. This collection focuses on novel techniques and study reports of data analysis in the application of AI for the diagnosis of GI disease in real-time clinical settings, assessing the role of AI in daily clinical practice.

Dr. Silvia Pecere
Dr. Debesh Jha
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 monthly 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 1800 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.

Published Papers (1 paper)

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Research

Article
An Improved Endoscopic Automatic Classification Model for Gastroesophageal Reflux Disease Using Deep Learning Integrated Machine Learning
Diagnostics 2022, 12(11), 2827; https://doi.org/10.3390/diagnostics12112827 - 17 Nov 2022
Viewed by 300
Abstract
Gastroesophageal reflux disease (GERD) is a common digestive tract disease, and most physicians use the Los Angeles classification and diagnose the severity of the disease to provide appropriate treatment. With the advancement of artificial intelligence, deep learning models have been used successfully to [...] Read more.
Gastroesophageal reflux disease (GERD) is a common digestive tract disease, and most physicians use the Los Angeles classification and diagnose the severity of the disease to provide appropriate treatment. With the advancement of artificial intelligence, deep learning models have been used successfully to help physicians with clinical diagnosis. This study combines deep learning and machine learning techniques and proposes a two-stage process for endoscopic classification in GERD, including transfer learning techniques applied to the target dataset to extract more precise image features and machine learning algorithms to build the best classification model. The experimental results demonstrate that the performance of the GerdNet-RF model proposed in this work is better than that of previous studies. Test accuracy can be improved from 78.8% ± 8.5% to 92.5% ± 2.1%. By enhancing the automated diagnostic capabilities of AI models, patient health care will be more assured. Full article
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