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Medicina
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Artificial Intelligence in Gastrointestinal Endoscopy: Disrupting Landscape in GI Practice
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Artificial Intelligence in Gastrointestinal Endoscopy: Disrupting Landscape in GI Practice

A special issue of Medicina (ISSN 1648-9144). This special issue belongs to the section "Gastroenterology & Hepatology".

Deadline for manuscript submissions: closed (30 November 2022) | Viewed by 11180

Special Issue Editors

Prof. Dr. Miguel Mascarenhas

E-Mail Website
Guest Editor
1. Gastroenterology Department, Centro Hospitalar Universitário de São João, Porto, Portugal
2. Faculty of Medicine, University of Porto, Porto, Portugal
Interests: inflammatory bowel disease; applied artificial intelligence; capsule endoscopy; neurogastroenterology; coloproctology
Special Issues, Collections and Topics in MDPI journals
Dr. Hélder Cardoso

E-Mail Website
Co-Guest Editor
1. Gastroenterology Department, Centro Hospitalar Universitário de São João, Porto, Portugal
2. Faculty of Medicine, University of Porto, Porto, Portugal
3. World Gastroenterology Organization Porto Training Center, Porto, Portugal
Interests: gastroenterology; hepatology; endoscopy, capsule endoscopy; enteroscopy; applied artificial intelligence; liver cancer; inflammatory bowel diseases
Special Issues, Collections and Topics in MDPI journals
Dr. Ana Patricia Andrade

E-Mail Website
Co-Guest Editor
Gastroenterology Department, São João University Hospital, Porto, Portugal
Interests: inflammatory bowel disease; liver diseases; colonoscopy; hepatology; gastrointestinal diseases
Prof. Dr. Guilherme Macedo

E-Mail Website
Co-Guest Editor
1. Gastroenterology Department, Centro Hospitalar Universitário de São João, Porto, Portugal
2. Faculty of Medicine, University of Porto, Porto, Portugal
Interests: gastroenterology; hepatology; liver transplantation; hepatocellular carcinoma; gastrointestinal diseases; biliary tract diseases; inflammatory bowel diseases
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The applicability of artificial intelligence (AI) in digestive healthcare is a hot topic because of its disruptive nature. Indeed, the potential of AI has applications over a range of different medical specialties, while specialties with a strong imaging and diagnostic component have assumed a leading position in the implementation of this technology. With the advent of the big data era, the accumulation of a gigantic number of digital images and medical records created an unparalleled set of resources for developing proficuous AI tools.

Gastrointestinal endoscopy (GI) plays a crucial role in several areas of digestive pathology, being an invaluable diagnostic and therapeutic tool. Therefore, there is growing interest in the use of AI in GI endoscopy. Several studies have demonstrated the enormous potential of using convolutional neural networks in various areas of GI endoscopy. The exponential development of the usefulness of AI in GI endoscopy requires consideration of its medium- and long-term impacts on clinical practice. Indeed, the advent of deep learning in the field of GI endoscopy, with its evolutionary character, could lead to a paradigm shift in clinical activity in this setting. In this Special Issue, we aim to showcase the state of the art of AI in the field of GI endoscopy, with examples of cutting-edge research being carried out in this particular field.

Given the high importance of AI in gastrointestinal endoscopy and research, the journal Medicina is launching this Special Issue.

We encourage you and your co-workers to submit your articles reporting on this topic. Reviews or original articles dealing with the clinical usefulness of AI tools in GI endoscopy, namely, in the prevention and the early detection of GI cancer (premalignant and malignant lesions), are welcome. Further topics for this Special Issue should include AI's role in capsule endoscopy, pancreatobiliary endoscopy, endoscopic ultrasound, and chromoendoscopy. In addition, we warmly invite you to submit articles reporting on the role of AI in GI endoscopy in the clinical scope of inflammatory bowel disease and hepatology.

We want to reinforce that given your specific expertise, the editorial team targeted your contribution as being essential for the Special Issue's success and soundness.

Dr. Miguel Mascarenhas Saraiva
Dr. Hélder Cardoso
Dr. Ana Patricia Andrade
Dr. Guilherme Macedo
Prof. Dr. Guilherme Macedo
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. Medicina 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.

Keywords

  • artificial intelligence
  • gastrointestinal endoscopy
  • deep learning
  • machine learning
  • colonoscopy
  • esophagogastroduodenoscopy
  • capsule endoscopy
  • digestive healthcare
  • gastroenterology
  • coloproctology

Published Papers (5 papers)

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Research

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11 pages, 1375 KiB  
Article
Design of a Convolutional Neural Network as a Deep Learning Tool for the Automatic Classification of Small-Bowel Cleansing in Capsule Endoscopy
by Tiago Ribeiro, Miguel José Mascarenhas Saraiva, João Afonso, Pedro Cardoso, Francisco Mendes, Miguel Martins, Ana Patrícia Andrade, Hélder Cardoso, Miguel Mascarenhas Saraiva, João Ferreira and Guilherme Macedo
Medicina 2023, 59(4), 810; https://doi.org/10.3390/medicina59040810 - 21 Apr 2023
Cited by 3 | Viewed by 1439
Abstract
Background and objectives: Capsule endoscopy (CE) is a non-invasive method to inspect the small bowel that, like other enteroscopy methods, requires adequate small-bowel cleansing to obtain conclusive results. Artificial intelligence (AI) algorithms have been seen to offer important benefits in the field [...] Read more.
Background and objectives: Capsule endoscopy (CE) is a non-invasive method to inspect the small bowel that, like other enteroscopy methods, requires adequate small-bowel cleansing to obtain conclusive results. Artificial intelligence (AI) algorithms have been seen to offer important benefits in the field of medical imaging over recent years, particularly through the adaptation of convolutional neural networks (CNNs) to achieve more efficient image analysis. Here, we aimed to develop a deep learning model that uses a CNN to automatically classify the quality of intestinal preparation in CE. Methods: A CNN was designed based on 12,950 CE images obtained at two clinical centers in Porto (Portugal). The quality of the intestinal preparation was classified for each image as: excellent, ≥90% of the image surface with visible mucosa; satisfactory, 50–90% of the mucosa visible; and unsatisfactory, <50% of the mucosa visible. The total set of images was divided in an 80:20 ratio to establish training and validation datasets, respectively. The CNN prediction was compared with the classification established by consensus of a group of three experts in CE, currently considered the gold standard to evaluate cleanliness. Subsequently, how the CNN performed in diagnostic terms was evaluated using an independent validation dataset. Results: Among the images obtained, 3633 were designated as unsatisfactory preparation, 6005 satisfactory preparation, and 3312 with excellent preparation. When differentiating the classes of small-bowel preparation, the algorithm developed here achieved an overall accuracy of 92.1%, with a sensitivity of 88.4%, a specificity of 93.6%, a positive predictive value of 88.5%, and a negative predictive value of 93.4%. The area under the curve for the detection of excellent, satisfactory, and unsatisfactory classes was 0.98, 0.95, and 0.99, respectively. Conclusions: A CNN-based tool was developed to automatically classify small-bowel preparation for CE, and it was seen to accurately classify intestinal preparation for CE. The development of such a system could enhance the reproducibility of the scales used for such purposes. Full article
(This article belongs to the Special Issue Artificial Intelligence in Gastrointestinal Endoscopy: Disrupting Landscape in GI Practice)
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8 pages, 1413 KiB  
Article
Deep-Learning and Device-Assisted Enteroscopy: Automatic Panendoscopic Detection of Ulcers and Erosions
by Miguel Martins, Miguel Mascarenhas, João Afonso, Tiago Ribeiro, Pedro Cardoso, Francisco Mendes, Hélder Cardoso, Patrícia Andrade, João Ferreira and Guilherme Macedo
Medicina 2023, 59(1), 172; https://doi.org/10.3390/medicina59010172 - 15 Jan 2023
Cited by 7 | Viewed by 1811
Abstract
Background and Objectives: Device-assisted enteroscopy (DAE) has a significant role in approaching enteric lesions. Endoscopic observation of ulcers or erosions is frequent and can be associated with many nosological entities, namely Crohn’s disease. Although the application of artificial intelligence (AI) is growing [...] Read more.
Background and Objectives: Device-assisted enteroscopy (DAE) has a significant role in approaching enteric lesions. Endoscopic observation of ulcers or erosions is frequent and can be associated with many nosological entities, namely Crohn’s disease. Although the application of artificial intelligence (AI) is growing exponentially in various imaged-based gastroenterology procedures, there is still a lack of evidence of the AI technical feasibility and clinical applicability of DAE. This study aimed to develop and test a multi-brand convolutional neural network (CNN)-based algorithm for automatically detecting ulcers and erosions in DAE. Materials and Methods: A unicentric retrospective study was conducted for the development of a CNN, based on a total of 250 DAE exams. A total of 6772 images were used, of which 678 were considered ulcers or erosions after double-validation. Data were divided into a training and a validation set, the latter being used for the performance assessment of the model. Our primary outcome measures were sensitivity, specificity, accuracy, positive predictive value (PPV), negative predictive value (NPV), and an area under the curve precision–recall curve (AUC-PR). Results: Sensitivity, specificity, PPV, and NPV were respectively 88.5%, 99.7%, 96.4%, and 98.9%. The algorithm’s accuracy was 98.7%. The AUC-PR was 1.00. The CNN processed 293.6 frames per second, enabling AI live application in a real-life clinical setting in DAE. Conclusion: To the best of our knowledge, this is the first study regarding the automatic multi-brand panendoscopic detection of ulcers and erosions throughout the digestive tract during DAE, overcoming a relevant interoperability challenge. Our results highlight that using a CNN to detect this type of lesion is associated with high overall accuracy. The development of binary CNN for automatically detecting clinically relevant endoscopic findings and assessing endoscopic inflammatory activity are relevant steps toward AI application in digestive endoscopy, particularly for panendoscopic evaluation. Full article
(This article belongs to the Special Issue Artificial Intelligence in Gastrointestinal Endoscopy: Disrupting Landscape in GI Practice)
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12 pages, 28192 KiB  
Article
Semantic Segmentation Dataset for AI-Based Quantification of Clean Mucosa in Capsule Endoscopy
by Jeong-Woo Ju, Heechul Jung, Yeoun Joo Lee, Sang-Wook Mun and Jong-Hyuck Lee
Medicina 2022, 58(3), 397; https://doi.org/10.3390/medicina58030397 - 07 Mar 2022
Cited by 6 | Viewed by 2471
Abstract
Background and Objectives: Capsule endoscopy (CE) for bowel cleanliness evaluation primarily depends on subjective methods. To objectively evaluate bowel cleanliness, we focused on artificial intelligence (AI)-based assessments. We aimed to generate a large segmentation dataset from CE images and verify its quality [...] Read more.
Background and Objectives: Capsule endoscopy (CE) for bowel cleanliness evaluation primarily depends on subjective methods. To objectively evaluate bowel cleanliness, we focused on artificial intelligence (AI)-based assessments. We aimed to generate a large segmentation dataset from CE images and verify its quality using a convolutional neural network (CNN)-based algorithm. Materials and Methods: Images were extracted and divided into 10 stages according to the clean regions in a CE video. Each image was classified into three classes (clean, dark, and floats/bubbles) or two classes (clean and non-clean). Using this semantic segmentation dataset, a CNN training was performed with 169 videos, and a clean region (visualization scale (VS)) formula was developed. Then, measuring mean intersection over union (mIoU), Dice index, and clean mucosal predictions were performed. The VS performance was tested using 10 videos. Results: A total of 10,033 frames of the semantic segmentation dataset were constructed from 179 patients. The 3-class and 2-class semantic segmentation’s testing performance was 0.7716 mIoU (range: 0.7031–0.8071), 0.8627 Dice index (range: 0.7846–0.8891), and 0.8927 mIoU (range: 0.8562–0.9330), 0.9457 Dice index (range: 0.9225–0.9654), respectively. In addition, the 3-class and 2-class clean mucosal prediction accuracy was 94.4% and 95.7%, respectively. The VS prediction performance for both 3-class and 2-class segmentation was almost identical to the ground truth. Conclusions: We established a semantic segmentation dataset spanning 10 stages uniformly from 179 patients. The prediction accuracy for clean mucosa was significantly high (above 94%). Our VS equation can approximately measure the region of clean mucosa. These results confirmed our dataset to be ideal for an accurate and quantitative assessment of AI-based bowel cleanliness. Full article
(This article belongs to the Special Issue Artificial Intelligence in Gastrointestinal Endoscopy: Disrupting Landscape in GI Practice)
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9 pages, 1527 KiB  
Article
Deep Learning and Device-Assisted Enteroscopy: Automatic Detection of Gastrointestinal Angioectasia
by Miguel Mascarenhas Saraiva, Tiago Ribeiro, João Afonso, Patrícia Andrade, Pedro Cardoso, João Ferreira, Hélder Cardoso and Guilherme Macedo
Medicina 2021, 57(12), 1378; https://doi.org/10.3390/medicina57121378 - 18 Dec 2021
Cited by 10 | Viewed by 2229
Abstract
Background and Objectives: Device-assisted enteroscopy (DAE) allows deep exploration of the small bowel and combines diagnostic and therapeutic capacities. Suspected mid-gastrointestinal bleeding is the most frequent indication for DAE, and vascular lesions, particularly angioectasia, are the most common etiology. Nevertheless, the diagnostic [...] Read more.
Background and Objectives: Device-assisted enteroscopy (DAE) allows deep exploration of the small bowel and combines diagnostic and therapeutic capacities. Suspected mid-gastrointestinal bleeding is the most frequent indication for DAE, and vascular lesions, particularly angioectasia, are the most common etiology. Nevertheless, the diagnostic yield of DAE for the detection of these lesions is suboptimal. Deep learning algorithms have shown great potential for automatic detection of lesions in endoscopy. We aimed to develop an artificial intelligence (AI) model for the automatic detection of angioectasia DAE images. Materials and Methods: A convolutional neural network (CNN) was developed using DAE images. Each frame was labeled as normal/mucosa or angioectasia. The image dataset was split for the constitution of training and validation datasets. The latter was used for assessing the performance of the CNN. Results: A total of 72 DAE exams were included, and 6740 images were extracted (5345 of normal mucosa and 1395 of angioectasia). The model had a sensitivity of 88.5%, a specificity of 97.1% and an AUC of 0.988. The image processing speed was 6.4 ms/frame. Conclusions: The application of AI to DAE may have a significant impact on the management of patients with suspected mid-gastrointestinal bleeding. Full article
(This article belongs to the Special Issue Artificial Intelligence in Gastrointestinal Endoscopy: Disrupting Landscape in GI Practice)
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Review

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13 pages, 941 KiB  
Review
The Promise of Artificial Intelligence in Digestive Healthcare and the Bioethics Challenges It Presents
by Miguel Mascarenhas, João Afonso, Tiago Ribeiro, Patrícia Andrade, Hélder Cardoso and Guilherme Macedo
Medicina 2023, 59(4), 790; https://doi.org/10.3390/medicina59040790 - 18 Apr 2023
Cited by 6 | Viewed by 2357
Abstract
With modern society well entrenched in the digital area, the use of Artificial Intelligence (AI) to extract useful information from big data has become more commonplace in our daily lives than we perhaps realize. Medical specialties that rely heavily on imaging techniques have [...] Read more.
With modern society well entrenched in the digital area, the use of Artificial Intelligence (AI) to extract useful information from big data has become more commonplace in our daily lives than we perhaps realize. Medical specialties that rely heavily on imaging techniques have become a strong focus for the incorporation of AI tools to aid disease diagnosis and monitoring, yet AI-based tools that can be employed in the clinic are only now beginning to become a reality. However, the potential introduction of these applications raises a number of ethical issues that must be addressed before they can be implemented, among the most important of which are issues related to privacy, data protection, data bias, explainability and responsibility. In this short review, we aim to highlight some of the most important bioethical issues that will have to be addressed if AI solutions are to be successfully incorporated into healthcare protocols, and ideally, before they are put in place. In particular, we contemplate the use of these aids in the field of gastroenterology, focusing particularly on capsule endoscopy and highlighting efforts aimed at resolving the issues associated with their use when available. Full article
(This article belongs to the Special Issue Artificial Intelligence in Gastrointestinal Endoscopy: Disrupting Landscape in GI Practice)
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