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Guo F, Meng H. Application of artificial intelligence in gastrointestinal endoscopy. Arab J Gastroenterol 2024; 25:93-96. [PMID: 38228443 DOI: 10.1016/j.ajg.2023.12.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/24/2023] [Revised: 09/06/2023] [Accepted: 12/30/2023] [Indexed: 01/18/2024]
Abstract
Endoscopy is an important method for diagnosing gastrointestinal (GI) diseases. In this study, we provide an overview of the advances in artificial intelligence (AI) technology in the field of GI endoscopy over recent years, including esophagus, stomach, large intestine, and capsule endoscopy (small intestine). AI-assisted endoscopy shows high accuracy, sensitivity, and specificity in the detection and diagnosis of GI diseases at all levels. Hence, AI will make a breakthrough in the field of GI endoscopy in the near future. However, AI technology currently has some limitations and is still in the preclinical stages.
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Affiliation(s)
- Fujia Guo
- The first Affiliated Hospital, Dalian Medical University, Dalian 116044, China
| | - Hua Meng
- The first Affiliated Hospital, Dalian Medical University, Dalian 116044, China.
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Brzeski A, Dziubich T, Krawczyk H. Visual Features for Improving Endoscopic Bleeding Detection Using Convolutional Neural Networks. SENSORS (BASEL, SWITZERLAND) 2023; 23:9717. [PMID: 38139563 PMCID: PMC10748269 DOI: 10.3390/s23249717] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/16/2023] [Revised: 11/19/2023] [Accepted: 12/04/2023] [Indexed: 12/24/2023]
Abstract
The presented paper investigates the problem of endoscopic bleeding detection in endoscopic videos in the form of a binary image classification task. A set of definitions of high-level visual features of endoscopic bleeding is introduced, which incorporates domain knowledge from the field. The high-level features are coupled with respective feature descriptors, enabling automatic capture of the features using image processing methods. Each of the proposed feature descriptors outputs a feature activation map in the form of a grayscale image. Acquired feature maps can be appended in a straightforward way to the original color channels of the input image and passed to the input of a convolutional neural network during the training and inference steps. An experimental evaluation is conducted to compare the classification ROC AUC of feature-extended convolutional neural network models with baseline models using regular color image inputs. The advantage of feature-extended models is demonstrated for the Resnet and VGG convolutional neural network architectures.
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Affiliation(s)
- Adam Brzeski
- Faculty of Electronics, Telecommunications and Informatics, Gdańsk University of Technology, 80-233 Gdańsk, Poland; (T.D.); (H.K.)
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Musha A, Hasnat R, Mamun AA, Ping EP, Ghosh T. Computer-Aided Bleeding Detection Algorithms for Capsule Endoscopy: A Systematic Review. SENSORS (BASEL, SWITZERLAND) 2023; 23:7170. [PMID: 37631707 PMCID: PMC10459126 DOI: 10.3390/s23167170] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/27/2023] [Revised: 08/08/2023] [Accepted: 08/10/2023] [Indexed: 08/27/2023]
Abstract
Capsule endoscopy (CE) is a widely used medical imaging tool for the diagnosis of gastrointestinal tract abnormalities like bleeding. However, CE captures a huge number of image frames, constituting a time-consuming and tedious task for medical experts to manually inspect. To address this issue, researchers have focused on computer-aided bleeding detection systems to automatically identify bleeding in real time. This paper presents a systematic review of the available state-of-the-art computer-aided bleeding detection algorithms for capsule endoscopy. The review was carried out by searching five different repositories (Scopus, PubMed, IEEE Xplore, ACM Digital Library, and ScienceDirect) for all original publications on computer-aided bleeding detection published between 2001 and 2023. The Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) methodology was used to perform the review, and 147 full texts of scientific papers were reviewed. The contributions of this paper are: (I) a taxonomy for computer-aided bleeding detection algorithms for capsule endoscopy is identified; (II) the available state-of-the-art computer-aided bleeding detection algorithms, including various color spaces (RGB, HSV, etc.), feature extraction techniques, and classifiers, are discussed; and (III) the most effective algorithms for practical use are identified. Finally, the paper is concluded by providing future direction for computer-aided bleeding detection research.
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Affiliation(s)
- Ahmmad Musha
- Department of Electrical and Electronic Engineering, Pabna University of Science and Technology, Pabna 6600, Bangladesh; (A.M.); (R.H.)
| | - Rehnuma Hasnat
- Department of Electrical and Electronic Engineering, Pabna University of Science and Technology, Pabna 6600, Bangladesh; (A.M.); (R.H.)
| | - Abdullah Al Mamun
- Faculty of Engineering and Technology, Multimedia University, Melaka 75450, Malaysia;
| | - Em Poh Ping
- Faculty of Engineering and Technology, Multimedia University, Melaka 75450, Malaysia;
| | - Tonmoy Ghosh
- Department of Electrical and Computer Engineering, The University of Alabama, Tuscaloosa, AL 35487, USA;
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Ribeiro T, Mascarenhas Saraiva MJ, Afonso J, Cardoso P, Mendes F, Martins M, Andrade AP, Cardoso H, Mascarenhas Saraiva M, Ferreira J, Macedo G. Design of a Convolutional Neural Network as a Deep Learning Tool for the Automatic Classification of Small-Bowel Cleansing in Capsule Endoscopy. MEDICINA (KAUNAS, LITHUANIA) 2023; 59:medicina59040810. [PMID: 37109768 PMCID: PMC10145655 DOI: 10.3390/medicina59040810] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/05/2023] [Revised: 04/03/2023] [Accepted: 04/06/2023] [Indexed: 04/29/2023]
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 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.
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Affiliation(s)
- Tiago Ribeiro
- Department of Gasteroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
- Gastroenterology and Hepatology, WGO Gastroenterology and Hepatology Training Centre, 4050-345 Porto, Portugal
| | - Miguel José Mascarenhas Saraiva
- Department of Gasteroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
- Gastroenterology and Hepatology, WGO Gastroenterology and Hepatology Training Centre, 4050-345 Porto, Portugal
- Faculty of Medicine, University of Porto, 4200-319 Porto, Portugal
| | - João Afonso
- Department of Gasteroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
- Gastroenterology and Hepatology, WGO Gastroenterology and Hepatology Training Centre, 4050-345 Porto, Portugal
| | - Pedro Cardoso
- Department of Gasteroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
- Gastroenterology and Hepatology, WGO Gastroenterology and Hepatology Training Centre, 4050-345 Porto, Portugal
| | - Francisco Mendes
- Department of Gasteroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
- Gastroenterology and Hepatology, WGO Gastroenterology and Hepatology Training Centre, 4050-345 Porto, Portugal
| | - Miguel Martins
- Department of Gasteroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
- Gastroenterology and Hepatology, WGO Gastroenterology and Hepatology Training Centre, 4050-345 Porto, Portugal
| | - Ana Patrícia Andrade
- Department of Gasteroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
- Gastroenterology and Hepatology, WGO Gastroenterology and Hepatology Training Centre, 4050-345 Porto, Portugal
- Faculty of Medicine, University of Porto, 4200-319 Porto, Portugal
| | - Hélder Cardoso
- Department of Gasteroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
- Gastroenterology and Hepatology, WGO Gastroenterology and Hepatology Training Centre, 4050-345 Porto, Portugal
- Faculty of Medicine, University of Porto, 4200-319 Porto, Portugal
| | | | - João Ferreira
- Department of Mechanical Engineering, Faculty of Engineering, University of Porto, Rua Dr. Roberto Frias, 4200-465 Porto, Portugal
- INEGI-Institute of Science and Innovation in Mechanical and Industrial Engineering, 4200-465 Porto, Portugal
| | - Guilherme Macedo
- Department of Gasteroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, 4200-427 Porto, Portugal
- Gastroenterology and Hepatology, WGO Gastroenterology and Hepatology Training Centre, 4050-345 Porto, Portugal
- Faculty of Medicine, University of Porto, 4200-319 Porto, Portugal
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Mascarenhas M, Afonso J, Ribeiro T, Andrade P, Cardoso H, Macedo G. The Promise of Artificial Intelligence in Digestive Healthcare and the Bioethics Challenges It Presents. MEDICINA (KAUNAS, LITHUANIA) 2023; 59:medicina59040790. [PMID: 37109748 PMCID: PMC10145124 DOI: 10.3390/medicina59040790] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/19/2023] [Revised: 03/27/2023] [Accepted: 04/02/2023] [Indexed: 04/29/2023]
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 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.
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Affiliation(s)
- Miguel Mascarenhas
- Faculty of Medicine, University of Porto, 4200-437 Porto, Portugal
- Precision Medicine Unit, Department of Gastroenterology, Hospital São João, 4200-437 Porto, Portugal
- WGO Training Center, 4200-437 Porto, Portugal
| | - João Afonso
- Precision Medicine Unit, Department of Gastroenterology, Hospital São João, 4200-437 Porto, Portugal
- WGO Training Center, 4200-437 Porto, Portugal
| | - Tiago Ribeiro
- Precision Medicine Unit, Department of Gastroenterology, Hospital São João, 4200-437 Porto, Portugal
- WGO Training Center, 4200-437 Porto, Portugal
| | - Patrícia Andrade
- Faculty of Medicine, University of Porto, 4200-437 Porto, Portugal
- Precision Medicine Unit, Department of Gastroenterology, Hospital São João, 4200-437 Porto, Portugal
- WGO Training Center, 4200-437 Porto, Portugal
| | - Hélder Cardoso
- Faculty of Medicine, University of Porto, 4200-437 Porto, Portugal
- Precision Medicine Unit, Department of Gastroenterology, Hospital São João, 4200-437 Porto, Portugal
- WGO Training Center, 4200-437 Porto, Portugal
| | - Guilherme Macedo
- Faculty of Medicine, University of Porto, 4200-437 Porto, Portugal
- Precision Medicine Unit, Department of Gastroenterology, Hospital São João, 4200-437 Porto, Portugal
- WGO Training Center, 4200-437 Porto, Portugal
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