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Artificial Intelligence and Radiomics Applications in Gastrointestinal Diseases

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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 (1 November 2021) | Viewed by 16864

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Special Issue Editors

Dr. Luca Boldrini

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Guest Editor

Fondazione Policlinico Universitario “A. Gemelli” IRCCS, Rome, Italy
Interests: radiotherapy; MRI-guided radiotherapy; artificial intelligence; hybrid imaging; radiomics

Dr. Jorge D. Machicado

E-Mail Website
Co-Guest Editor

Mayo Clinic Health System, Eau Claire, WI, USA
Interests: advanced endoscopy; pancreatobiliary disorders; advanced imaging modalities; artificial intelligence; radiomics

Special Issue Information

Dear Colleagues,

Artificial intelligence and radiomics represent potential paradigm-shifting approaches in the most modern management of gastrointestinal diseases. These innovative applications may effectively support clinicians in their choices, governing an unprecedented amount of data for an increasingly effective decision-making process.

Gastrointestinal diseases, whether benign or oncological, represent a particularly promising field of application for these investigation techniques, thanks to the numerous involved data sources (lab tests, imaging, functional exams, digital pathology, nutritional, etc.) and the potentialities of their integration.

This Special Issue “Artificial Intelligence and Radiomics Applications in Gastrointestinal Diseases” will include literature reviews and primary research studies focusing on the implementation and scale up of artificial intelligence and radiomics in gastrointestinal diseases.

Dr. Luca Boldrini
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

Radiomics
Big data
Artificial intelligence
Surgical data science
Enhanced reality
Gastrointestinal disease
Gastrointestinal cancer
Quantitative imaging
Omics-guided therapy

Published Papers (6 papers)

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Research

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16 pages, 1741 KiB

Open AccessArticle

A Promising Approach: Artificial Intelligence Applied to Small Intestinal Bacterial Overgrowth (SIBO) Diagnosis Using Cluster Analysis

by Rong Hao, Lun Zhang, Jiashuang Liu, Yajun Liu, Jun Yi and Xiaowei Liu

Diagnostics 2021, 11(8), 1445; https://doi.org/10.3390/diagnostics11081445 - 10 Aug 2021

Cited by 1 | Viewed by 2092

Abstract

Small intestinal bacterial overgrowth (SIBO) is characterized by abnormal and excessive amounts of bacteria in the small intestine. Since symptoms and lab tests are non-specific, the diagnosis of SIBO is highly dependent on breath testing. There is a lack of a universally accepted cut-off point for breath testing to diagnose SIBO, and the dilemma of defining “SIBO patients” has made it more difficult to explore the gold standard for SIBO diagnosis. How to validate the gold standard for breath testing without defining “SIBO patients” has become an imperious demand in clinic. Breath-testing datasets from 1071 patients were collected from Xiangya Hospital in the past 3 years and analyzed with an artificial intelligence method using cluster analysis. K-means and DBSCAN algorithms were applied to the dataset after the clustering tendency was confirmed with Hopkins Statistic. Satisfying the clustering effect was evaluated with a Silhouette score, and patterns of each group were described. Advantages of artificial intelligence application in adaptive breath-testing diagnosis criteria with SIBO were discussed from the aspects of high dimensional analysis, and data-driven and regional specific dietary influence. This research work implied a promising application of artificial intelligence for SIBO diagnosis, which would benefit clinical practice and scientific research. Full article

(This article belongs to the Special Issue Artificial Intelligence and Radiomics Applications in Gastrointestinal Diseases)

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13 pages, 1230 KiB

Open AccessArticle

Predicting the Local Response of Esophageal Squamous Cell Carcinoma to Neoadjuvant Chemoradiotherapy by Radiomics with a Machine Learning Method Using ¹⁸F-FDG PET Images

by Yuji Murakami, Daisuke Kawahara, Shigeyuki Tani, Katsumaro Kubo, Tsuyoshi Katsuta, Nobuki Imano, Yuki Takeuchi, Ikuno Nishibuchi, Akito Saito and Yasushi Nagata

Diagnostics 2021, 11(6), 1049; https://doi.org/10.3390/diagnostics11061049 - 7 Jun 2021

Cited by 13 | Viewed by 2549

Abstract

Background: This study aimed to propose a machine learning model to predict the local response of resectable locally advanced esophageal squamous cell carcinoma (LA-ESCC) treated by neoadjuvant chemoradiotherapy (NCRT) using pretreatment 18-fluorodeoxyglucose positron emission tomography (FDG PET) images. Methods: The local responses of 98 patients were categorized into two groups (complete response and noncomplete response). We performed a radiomics analysis using five segmentations created on FDG PET images, resulting in 4250 features per patient. To construct a machine learning model, we used the least absolute shrinkage and selection operator (LASSO) regression to extract radiomics features optimal for the prediction. Then, a prediction model was constructed by using a neural network classifier. The training model was evaluated with 5-fold cross-validation. Results: By the LASSO analysis of the training data, 22 radiomics features were extracted. In the testing data, the average accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve score of the five prediction models were 89.6%, 92.7%, 89.5%, and 0.95, respectively. Conclusions: The proposed machine learning model using radiomics showed promising predictive accuracy of the local response of LA-ESCC treated by NCRT. Full article

(This article belongs to the Special Issue Artificial Intelligence and Radiomics Applications in Gastrointestinal Diseases)

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17 pages, 6491 KiB

Open AccessArticle

Feature Point Tracking-Based Localization of Colon Capsule Endoscope

by Jürgen Herp, Ulrik Deding, Maria M. Buijs, Rasmus Kroijer, Gunnar Baatrup and Esmaeil S. Nadimi

Diagnostics 2021, 11(2), 193; https://doi.org/10.3390/diagnostics11020193 - 28 Jan 2021

Cited by 12 | Viewed by 2714

Abstract

In large bowel investigations using endoscopic capsules and upon detection of significant findings, physicians require the location of those findings for a follow-up therapeutic colonoscopy. To cater to this need, we propose a model based on tracking feature points in consecutive frames of videos retrieved from colon capsule endoscopy investigations. By locally approximating the colon as a cylinder, we obtained both the displacement and the orientation of the capsule using geometrical assumptions and by setting priors on both physical properties of the intestine and the image sample frequency of the endoscopic capsule. Our proposed model tracks a colon capsule endoscope through the large intestine for different prior selections. A discussion on validating the findings in terms of intra and inter capsule and expert panel validation is provided. The performance of the model is evaluated based on the average difference in multiple reconstructed capsule’s paths through the large intestine. The path difference averaged over all videos was as low as

4 \pm 0.7

cm, with min and max error corresponding to 1.2 and 6.0 cm, respectively. The inter comparison addresses frame classification for the rectum, descending and sigmoid, splenic flexure, transverse, hepatic, and ascending, with an average accuracy of 86%. Full article

(This article belongs to the Special Issue Artificial Intelligence and Radiomics Applications in Gastrointestinal Diseases)

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11 pages, 594 KiB

Open AccessArticle

Delta Radiomics Analysis for Local Control Prediction in Pancreatic Cancer Patients Treated Using Magnetic Resonance Guided Radiotherapy

by Davide Cusumano, Luca Boldrini, Poonam Yadav, Calogero Casà, Sangjune Laurence Lee, Angela Romano, Antonio Piras, Giuditta Chiloiro, Lorenzo Placidi, Francesco Catucci, Claudio Votta, Gian Carlo Mattiucci, Luca Indovina, Maria Antonietta Gambacorta, Michael Bassetti and Vincenzo Valentini

Diagnostics 2021, 11(1), 72; https://doi.org/10.3390/diagnostics11010072 - 5 Jan 2021

Cited by 28 | Viewed by 3487

Abstract

The aim of this study is to investigate the role of Delta Radiomics analysis in the prediction of one-year local control (1yLC) in patients affected by locally advanced pancreatic cancer (LAPC) and treated using Magnetic Resonance guided Radiotherapy (MRgRT). A total of 35 patients from two institutions were enrolled: A 0.35 Tesla T2*/T1 MR image was acquired for each case during simulation and on each treatment fraction. Physical dose was converted in biologically effective dose (BED) to compensate for different radiotherapy schemes. Delta Radiomics analysis was performed considering the gross tumour volume (GTV) delineated on MR images acquired at BED of 20, 40, and 60 Gy. The performance of the delta features in predicting 1yLC was investigated in terms of Wilcoxon Mann–Whitney test and area under receiver operating characteristic (ROC) curve (AUC). The most significant feature in predicting 1yLC was the variation of cluster shade calculated at BED = 40 Gy, with a p-value of 0.005 and an AUC of 0.78 (0.61–0.94). Delta Radiomics analysis on low-field MR images might play a promising role in 1yLC prediction for LAPC patients: further studies including an external validation dataset and a larger cohort of patients are recommended to confirm the validity of this preliminary experience. Full article

(This article belongs to the Special Issue Artificial Intelligence and Radiomics Applications in Gastrointestinal Diseases)

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Review

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13 pages, 288 KiB

Open AccessReview

Applications of Artificial Intelligence for the Diagnosis of Gastrointestinal Diseases

by Silvia Pecere, Sebastian Manuel Milluzzo, Gianluca Esposito, Emanuele Dilaghi, Andrea Telese and Leonardo Henry Eusebi

Diagnostics 2021, 11(9), 1575; https://doi.org/10.3390/diagnostics11091575 - 30 Aug 2021

Cited by 14 | Viewed by 2700

Abstract

The development of convolutional neural networks has achieved impressive advances of machine learning in recent years, leading to an increasing use of artificial intelligence (AI) in the field of gastrointestinal (GI) diseases. AI networks have been trained to differentiate benign from malignant lesions, analyze endoscopic and radiological GI images, and assess histological diagnoses, obtaining excellent results and high overall diagnostic accuracy. Nevertheless, there data are lacking on side effects of AI in the gastroenterology field, and high-quality studies comparing the performance of AI networks to health care professionals are still limited. Thus, large, controlled trials in real-time clinical settings are warranted to assess the role of AI in daily clinical practice. This narrative review gives an overview of some of the most relevant potential applications of AI for gastrointestinal diseases, highlighting advantages and main limitations and providing considerations for future development. Full article

(This article belongs to the Special Issue Artificial Intelligence and Radiomics Applications in Gastrointestinal Diseases)

11 pages, 594 KiB

Open AccessReview

Computer-Aided Detection False Positives in Colonoscopy

by Yu-Hsi Hsieh, Chia-Pei Tang, Chih-Wei Tseng, Tu-Liang Lin and Felix W. Leung

Diagnostics 2021, 11(6), 1113; https://doi.org/10.3390/diagnostics11061113 - 18 Jun 2021

Cited by 6 | Viewed by 2155

Abstract

Randomized control trials and meta-analyses comparing colonoscopies with and without computer-aided detection (CADe) assistance showed significant increases in adenoma detection rates (ADRs) with CADe. A major limitation of CADe is its false positives (FPs), ranked 3rd in importance among 59 research questions in a modified Delphi consensus review. The definition of FPs varies. One commonly used definition defines an FP as an activation of the CADe system, irrespective of the number of frames or duration of time, not due to any polypoid or nonpolypoid lesions. Although only 0.07 to 0.2 FPs were observed per colonoscopy, video analysis studies using FPs as the primary outcome showed much higher numbers of 26 to 27 per colonoscopy. Most FPs were of short duration (91% < 0.5 s). A higher number of FPs was also associated with suboptimal bowel preparation. The appearance of FPs can lead to user fatigue. The polypectomy of FPs results in increased procedure time and added use of resources. Re-training the CADe algorithms is one way to reduce FPs but is not practical in the clinical setting during colonoscopy. Water exchange (WE) is an emerging method that the colonoscopist can use to provide salvage cleaning during insertion. We discuss the potential of WE for reducing FPs as well as the augmentation of ADRs through CADe. Full article

(This article belongs to the Special Issue Artificial Intelligence and Radiomics Applications in Gastrointestinal Diseases)

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