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Tawheed A, Ismail A, Amer MS, Elnahas O, Mowafy T. Capsule endoscopy: Do we still need it after 24 years of clinical use? World J Gastroenterol 2025; 31:102692. [PMID: 39926220 PMCID: PMC11718605 DOI: 10.3748/wjg.v31.i5.102692] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/28/2024] [Revised: 11/20/2024] [Accepted: 12/02/2024] [Indexed: 12/30/2024] Open
Abstract
In this letter, we comment on a recent article published in the World Journal of Gastroenterology by Xiao et al, where the authors aimed to use a deep learning model to automatically detect gastrointestinal lesions during capsule endoscopy (CE). CE was first presented in 2000 and was approved by the Food and Drug Administration in 2001. The indications of CE overlap with those of regular diagnostic endoscopy. However, in clinical practice, CE is usually used to detect lesions in areas inaccessible to standard endoscopies or in cases of bleeding that might be missed during conventional endoscopy. Since the emergence of CE, many physiological and technical challenges have been faced and addressed. In this letter, we summarize the current challenges and briefly mention the proposed methods to overcome these challenges to answer a central question: Do we still need CE?
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Affiliation(s)
- Ahmed Tawheed
- Department of Endemic Medicine, Faculty of Medicine, Helwan University, Cairo 11795, Egypt
| | - Alaa Ismail
- Faculty of Medicine, Helwan University, Cairo 11795, Egypt
| | - Mohab S Amer
- Faculty of Medicine, Helwan University, Cairo 11795, Egypt
- Department of Research, SMART Company for Research Services, Cairo 11795, Egypt
| | - Osama Elnahas
- Faculty of Medicine, Helwan University, Cairo 11795, Egypt
| | - Tawhid Mowafy
- Department of Internal Medicine, Gardenia Medical Center, Doha 0000, Qatar
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2
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Rey JF. As how artificial intelligence is revolutionizing endoscopy. Clin Endosc 2024; 57:302-308. [PMID: 38454543 PMCID: PMC11133999 DOI: 10.5946/ce.2023.230] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/13/2023] [Revised: 10/11/2023] [Accepted: 10/15/2023] [Indexed: 03/09/2024] Open
Abstract
With incessant advances in information technology and its implications in all domains of our lives, artificial intelligence (AI) has emerged as a requirement for improved machine performance. This brings forth the query of how this can benefit endoscopists and improve both diagnostic and therapeutic endoscopy in each part of the gastrointestinal tract. Additionally, it also raises the question of the recent benefits and clinical usefulness of this new technology in daily endoscopic practice. There are two main categories of AI systems: computer-assisted detection (CADe) for lesion detection and computer-assisted diagnosis (CADx) for optical biopsy and lesion characterization. Quality assurance is the next step in the complete monitoring of high-quality colonoscopies. In all cases, computer-aided endoscopy is used, as the overall results rely on the physician. Video capsule endoscopy is a unique example in which a computer operates a device, stores multiple images, and performs an accurate diagnosis. While there are many expectations, we need to standardize and assess various software packages. It is important for healthcare providers to support this new development and make its use an obligation in daily clinical practice. In summary, AI represents a breakthrough in digestive endoscopy. Screening for gastric and colonic cancer detection should be improved, particularly outside expert centers. Prospective and multicenter trials are mandatory before introducing new software into clinical practice.
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Affiliation(s)
- Jean-Francois Rey
- Institut Arnaut Tzanck Gastrointestinal Unt, Saint Laurent du Var, France
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3
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George AA, Tan JL, Kovoor JG, Lee A, Stretton B, Gupta AK, Bacchi S, George B, Singh R. Artificial intelligence in capsule endoscopy: development status and future expectations. MINI-INVASIVE SURGERY 2024. [DOI: 10.20517/2574-1225.2023.102] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/24/2025]
Abstract
In this review, we aim to illustrate the state-of-the-art artificial intelligence (AI) applications in the field of capsule endoscopy. AI has made significant strides in gastrointestinal imaging, particularly in capsule endoscopy - a non-invasive procedure for capturing gastrointestinal tract images. However, manual analysis of capsule endoscopy videos is labour-intensive and error-prone, prompting the development of automated computational algorithms and AI models. While currently serving as a supplementary observer, AI has the capacity to evolve into an autonomous, integrated reading system, potentially significantly reducing capsule reading time while surpassing human accuracy. We searched Embase, Pubmed, Medline, and Cochrane databases from inception to 06 Jul 2023 for studies investigating the use of AI for capsule endoscopy and screened retrieved records for eligibility. Quantitative and qualitative data were extracted and synthesised to identify current themes. In the search, 824 articles were collected, and 291 duplicates and 31 abstracts were deleted. After a double-screening process and full-text review, 106 publications were included in the review. Themes pertaining to AI for capsule endoscopy included active gastrointestinal bleeding, erosions and ulcers, vascular lesions and angiodysplasias, polyps and tumours, inflammatory bowel disease, coeliac disease, hookworms, bowel prep assessment, and multiple lesion detection. This review provides current insights into the impact of AI on capsule endoscopy as of 2023. AI holds the potential for faster and precise readings and the prospect of autonomous image analysis. However, careful consideration of diagnostic requirements and potential challenges is crucial. The untapped potential within vision transformer technology hints at further evolution and even greater patient benefit.
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Juneja D. Artificial intelligence: Applications in critical care gastroenterology. Artif Intell Gastrointest Endosc 2024; 5:89138. [DOI: 10.37126/aige.v5.i1.89138] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/21/2023] [Revised: 12/07/2023] [Accepted: 12/26/2023] [Indexed: 02/20/2024] Open
Abstract
Gastrointestinal (GI) complications frequently necessitate intensive care unit (ICU) admission. Additionally, critically ill patients also develop GI complications requiring further diagnostic and therapeutic interventions. However, these patients form a vulnerable group, who are at risk for developing side effects and complications. Every effort must be made to reduce invasiveness and ensure safety of interventions in ICU patients. Artificial intelligence (AI) is a rapidly evolving technology with several potential applications in healthcare settings. ICUs produce a large amount of data, which may be employed for creation of AI algorithms, and provide a lucrative opportunity for application of AI. However, the current role of AI in these patients remains limited due to lack of large-scale trials comparing the efficacy of AI with the accepted standards of care.
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Affiliation(s)
- Deven Juneja
- Department of Critical Care Medicine, Max Super Speciality Hospital, New Delhi 110017, India
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Krishnan G, Singh S, Pathania M, Gosavi S, Abhishek S, Parchani A, Dhar M. Artificial intelligence in clinical medicine: catalyzing a sustainable global healthcare paradigm. Front Artif Intell 2023; 6:1227091. [PMID: 37705603 PMCID: PMC10497111 DOI: 10.3389/frai.2023.1227091] [Citation(s) in RCA: 84] [Impact Index Per Article: 42.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2023] [Accepted: 08/09/2023] [Indexed: 09/15/2023] Open
Abstract
As the demand for quality healthcare increases, healthcare systems worldwide are grappling with time constraints and excessive workloads, which can compromise the quality of patient care. Artificial intelligence (AI) has emerged as a powerful tool in clinical medicine, revolutionizing various aspects of patient care and medical research. The integration of AI in clinical medicine has not only improved diagnostic accuracy and treatment outcomes, but also contributed to more efficient healthcare delivery, reduced costs, and facilitated better patient experiences. This review article provides an extensive overview of AI applications in history taking, clinical examination, imaging, therapeutics, prognosis and research. Furthermore, it highlights the critical role AI has played in transforming healthcare in developing nations.
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Affiliation(s)
- Gokul Krishnan
- Department of Internal Medicine, Kasturba Medical College, Manipal, India
| | - Shiana Singh
- Department of Emergency Medicine, All India Institute of Medical Sciences, Rishikesh, India
| | - Monika Pathania
- Department of Geriatric Medicine, All India Institute of Medical Sciences, Rishikesh, India
| | - Siddharth Gosavi
- Department of Internal Medicine, Kasturba Medical College, Manipal, India
| | - Shuchi Abhishek
- Department of Internal Medicine, Kasturba Medical College, Manipal, India
| | - Ashwin Parchani
- Department of Geriatric Medicine, All India Institute of Medical Sciences, Rishikesh, India
| | - Minakshi Dhar
- Department of Geriatric Medicine, All India Institute of Medical Sciences, Rishikesh, India
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6
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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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Shen T, Li X. Automatic polyp image segmentation and cancer prediction based on deep learning. Front Oncol 2023; 12:1087438. [PMID: 36713495 PMCID: PMC9878560 DOI: 10.3389/fonc.2022.1087438] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2022] [Accepted: 12/22/2022] [Indexed: 01/15/2023] Open
Abstract
The similar shape and texture of colonic polyps and normal mucosal tissues lead to low accuracy of medical image segmentation algorithms. To solve these problems, we proposed a polyp image segmentation algorithm based on deep learning technology, which combines a HarDNet module, attention module, and multi-scale coding module with the U-Net network as the basic framework, including two stages of coding and decoding. In the encoder stage, HarDNet68 is used as the main backbone network to extract features using four null space convolutional pooling pyramids while improving the inference speed and computational efficiency; the attention mechanism module is added to the encoding and decoding network; then the model can learn the global and local feature information of the polyp image, thus having the ability to process information in both spatial and channel dimensions, to solve the problem of information loss in the encoding stage of the network and improving the performance of the segmentation network. Through comparative analysis with other algorithms, we can find that the network of this paper has a certain degree of improvement in segmentation accuracy and operation speed, which can effectively assist physicians in removing abnormal colorectal tissues and thus reduce the probability of polyp cancer, and improve the survival rate and quality of life of patients. Also, it has good generalization ability, which can provide technical support and prevention for colon cancer.
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Affiliation(s)
- Tongping Shen
- School of Information Engineering, Anhui University of Chinese Medicine, Hefei, China,Graduate School, Angeles University Foundation, Angeles, Philippines,*Correspondence: Tongping Shen,
| | - Xueguang Li
- School of Computer Science and Technology, Henan Institute of Technology, Xinxiang, China
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8
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Dahiya DS, Al-Haddad M, Chandan S, Gangwani MK, Aziz M, Mohan BP, Ramai D, Canakis A, Bapaye J, Sharma N. Artificial Intelligence in Endoscopic Ultrasound for Pancreatic Cancer: Where Are We Now and What Does the Future Entail? J Clin Med 2022; 11:7476. [PMID: 36556092 PMCID: PMC9786876 DOI: 10.3390/jcm11247476] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Revised: 12/14/2022] [Accepted: 12/15/2022] [Indexed: 12/23/2022] Open
Abstract
Pancreatic cancer is a highly lethal disease associated with significant morbidity and mortality. In the United States (US), the overall 5-year relative survival rate for pancreatic cancer during the 2012-2018 period was 11.5%. However, the cancer stage at diagnosis strongly influences relative survival in these patients. Per the National Cancer Institute (NCI) statistics for 2012-2018, the 5-year relative survival rate for patients with localized disease was 43.9%, while it was 3.1% for patients with distant metastasis. The poor survival rates are primarily due to the late development of clinical signs and symptoms. Hence, early diagnosis is critical in improving treatment outcomes. In recent years, artificial intelligence (AI) has gained immense popularity in gastroenterology. AI-assisted endoscopic ultrasound (EUS) models have been touted as a breakthrough in the early detection of pancreatic cancer. These models may also accurately differentiate pancreatic cancer from chronic pancreatitis and autoimmune pancreatitis, which mimics pancreatic cancer on radiological imaging. In this review, we detail the application of AI-assisted EUS models for pancreatic cancer detection. We also highlight the utility of AI-assisted EUS models in differentiating pancreatic cancer from radiological mimickers. Furthermore, we discuss the current limitations and future applications of AI technology in EUS for pancreatic cancers.
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Affiliation(s)
- Dushyant Singh Dahiya
- Department of Internal Medicine, Central Michigan University College of Medicine, Saginaw, MI 48601, USA
| | - Mohammad Al-Haddad
- Division of Gastroenterology and Hepatology, Indiana University School of Medicine, Indianapolis, IN 46202, USA
| | - Saurabh Chandan
- Division of Gastroenterology and Hepatology, CHI Creighton University Medical Center, Omaha, NE 68131, USA
| | - Manesh Kumar Gangwani
- Department of Internal Medicine, The University of Toledo Medical Center, Toledo, OH 43614, USA
| | - Muhammad Aziz
- Department of Gastroenterology, The University of Toledo Medical Center, Toledo, OH 43614, USA
| | - Babu P. Mohan
- Division of Gastroenterology and Hepatology, University of Utah School of Medicine, Salt Lake City, UT 84132, USA
| | - Daryl Ramai
- Division of Gastroenterology and Hepatology, University of Utah School of Medicine, Salt Lake City, UT 84132, USA
| | - Andrew Canakis
- Division of Gastroenterology and Hepatology, University of Maryland School of Medicine, Baltimore, MD 21201, USA
| | - Jay Bapaye
- Department of Internal Medicine, Rochester General Hospital, Rochester, NY 14621, USA
| | - Neil Sharma
- Division of Gastroenterology and Hepatology, Indiana University School of Medicine, Indianapolis, IN 46202, USA
- Parkview Cancer Institute, Fort Wayne, IN 46845, USA
- Interventional Oncology & Surgical Endoscopy Programs (IOSE), Parkview Health, Fort Wayne, IN 46845, USA
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9
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Ramamurthy K, George TT, Shah Y, Sasidhar P. A Novel Multi-Feature Fusion Method for Classification of Gastrointestinal Diseases Using Endoscopy Images. Diagnostics (Basel) 2022; 12:2316. [PMID: 36292006 PMCID: PMC9600128 DOI: 10.3390/diagnostics12102316] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2022] [Revised: 09/02/2022] [Accepted: 09/06/2022] [Indexed: 11/17/2022] Open
Abstract
The first step in the diagnosis of gastric abnormalities is the detection of various abnormalities in the human gastrointestinal tract. Manual examination of endoscopy images relies on a medical practitioner's expertise to identify inflammatory regions on the inner surface of the gastrointestinal tract. The length of the alimentary canal and the large volume of images obtained from endoscopic procedures make traditional detection methods time consuming and laborious. Recently, deep learning architectures have achieved better results in the classification of endoscopy images. However, visual similarities between different portions of the gastrointestinal tract pose a challenge for effective disease detection. This work proposes a novel system for the classification of endoscopy images by focusing on feature mining through convolutional neural networks (CNN). The model presented is built by combining a state-of-the-art architecture (i.e., EfficientNet B0) with a custom-built CNN architecture named Effimix. The proposed Effimix model employs a combination of squeeze and excitation layers and self-normalising activation layers for precise classification of gastrointestinal diseases. Experimental observations on the HyperKvasir dataset confirm the effectiveness of the proposed architecture for the classification of endoscopy images. The proposed model yields an accuracy of 97.99%, with an F1 score, precision, and recall of 97%, 97%, and 98%, respectively, which is significantly higher compared to the existing works.
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Affiliation(s)
- Karthik Ramamurthy
- Centre for Cyber Physical Systems, School of Electronics Engineering, Vellore Institute of Technology, Chennai 600127, India
| | - Timothy Thomas George
- School of Computer Science and Engineering, Vellore Institute of Technology, Chennai 600127, India
| | - Yash Shah
- School of Computer Science and Engineering, Vellore Institute of Technology, Chennai 600127, India
| | - Parasa Sasidhar
- School of Electronics Engineering, Vellore Institute of Technology, Chennai 600127, India
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Abstract
Artificial intelligence (AI) is rapidly developing in various medical fields, and there is an increase in research performed in the field of gastrointestinal (GI) endoscopy. In particular, the advent of convolutional neural network, which is a class of deep learning method, has the potential to revolutionize the field of GI endoscopy, including esophagogastroduodenoscopy (EGD), capsule endoscopy (CE), and colonoscopy. A total of 149 original articles pertaining to AI (27 articles in esophagus, 30 articles in stomach, 29 articles in CE, and 63 articles in colon) were identified in this review. The main focuses of AI in EGD are cancer detection, identifying the depth of cancer invasion, prediction of pathological diagnosis, and prediction of Helicobacter pylori infection. In the field of CE, automated detection of bleeding sites, ulcers, tumors, and various small bowel diseases is being investigated. AI in colonoscopy has advanced with several patient-based prospective studies being conducted on the automated detection and classification of colon polyps. Furthermore, research on inflammatory bowel disease has also been recently reported. Most studies of AI in the field of GI endoscopy are still in the preclinical stages because of the retrospective design using still images. Video-based prospective studies are needed to advance the field. However, AI will continue to develop and be used in daily clinical practice in the near future. In this review, we have highlighted the published literature along with providing current status and insights into the future of AI in GI endoscopy.
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Affiliation(s)
- Yutaka Okagawa
- Endoscopy Division, National Cancer Center Hospital, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan.,Department of Gastroenterology, Tonan Hospital, Sapporo, Japan
| | - Seiichiro Abe
- Endoscopy Division, National Cancer Center Hospital, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan.
| | - Masayoshi Yamada
- Endoscopy Division, National Cancer Center Hospital, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan
| | - Ichiro Oda
- Endoscopy Division, National Cancer Center Hospital, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan
| | - Yutaka Saito
- Endoscopy Division, National Cancer Center Hospital, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan
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11
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Automatic sleep stage classification: A light and efficient deep neural network model based on time, frequency and fractional Fourier transform domain features. Artif Intell Med 2022; 127:102279. [DOI: 10.1016/j.artmed.2022.102279] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2021] [Revised: 02/28/2022] [Accepted: 03/07/2022] [Indexed: 11/23/2022]
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Proposing Novel Data Analytics Method for Anatomical Landmark Identification from Endoscopic Video Frames. JOURNAL OF HEALTHCARE ENGINEERING 2022; 2022:8151177. [PMID: 35251578 PMCID: PMC8890842 DOI: 10.1155/2022/8151177] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/15/2021] [Revised: 12/20/2021] [Accepted: 01/07/2022] [Indexed: 11/18/2022]
Abstract
Background The anatomical landmarks contain the characteristics that are used to guide the gastroenterologists during the endoscopy. The expert can also ensure the completion of examination with the help of the anatomical landmarks. Automatic detection of anatomical landmarks in endoscopic video frames can be helpful for guiding the physicians during screening the gastrointestinal tract (GI). Method This study presents an automatic novel method for anatomical landmark detection of GI tract from endoscopic video frames based on semisupervised deep convolutional neural network (CNN) and compares the results with supervised CNN model. We consider the anatomical landmarks from Kvasir dataset that includes 500 images for each class of Z-line, pylorus, and cecum. The resolution of these images varies from 750 × 576 up to 1920 × 1072 pixels. Result Experimental results show that the supervised CNN has highly desirable performance with accuracy of 100%. Also, our proposed semisupervised CNN can compete with a slight difference similar to the CNN model. Our proposed semisupervised model trained using 1, 5, 10, and 20 percent of training data records as labeled training dataset has the average accuracy of 83%, 98%, 99%, and 99%, respectively. Conclusion The main advantage of our proposed method is achieving the high accuracy with small amount of labeled data without spending time for labeling more data. The strength of our proposed method saves the required labor, cost, and time for data labeling.
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Xu H, Han T, Wang H, Liu S, Hou G, Sun L, Jiang G, Yang F, Wang J, Deng K, Zhou J. OUP accepted manuscript. Eur J Cardiothorac Surg 2022; 62:6555788. [PMID: 35352106 PMCID: PMC9615432 DOI: 10.1093/ejcts/ezac154] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/15/2021] [Revised: 02/10/2022] [Accepted: 03/11/2011] [Indexed: 11/15/2022] Open
Affiliation(s)
- Hao Xu
- Department of Thoracic Surgery, Peking University People's Hospital, Beijing, China
| | - Tingxuan Han
- Center for Statistical Science & Department of Industrial Engineering, Tsinghua University, Beijing, China
| | - Haifeng Wang
- Center for Statistical Science & Department of Industrial Engineering, Tsinghua University, Beijing, China
| | - Shanggui Liu
- Department of Thoracic Surgery, Peking University People's Hospital, Beijing, China
| | - Guanghao Hou
- Department of Thoracic Surgery, Peking University People's Hospital, Beijing, China
| | - Lina Sun
- Central operating Theatre, Peking University People's Hospital, Beijing, China
| | - Guanchao Jiang
- Department of Thoracic Surgery, Peking University People's Hospital, Beijing, China
| | - Fan Yang
- Department of Thoracic Surgery, Peking University People's Hospital, Beijing, China
| | - Jun Wang
- Department of Thoracic Surgery, Peking University People's Hospital, Beijing, China
- Corresponding authors: Department of Thoracic Surgery, Peking University People's Hospital, Beijing 100044, China. Tel: +86 010-88326650; e-mail: (Dr. Jian Zhou); Department of Thoracic Surgery, Peking University People's Hospital, Beijing 100044, China. Tel: 010-88326652; e-mail: (Dr. Jun Wang); Center for Statistical Science & Department of Industrial Engineering, Tsinghua University, Beijing 100084, China. Tel: +86 010-62782453; e-mail: (Ke Deng)
| | - Ke Deng
- Center for Statistical Science & Department of Industrial Engineering, Tsinghua University, Beijing, China
- Corresponding authors: Department of Thoracic Surgery, Peking University People's Hospital, Beijing 100044, China. Tel: +86 010-88326650; e-mail: (Dr. Jian Zhou); Department of Thoracic Surgery, Peking University People's Hospital, Beijing 100044, China. Tel: 010-88326652; e-mail: (Dr. Jun Wang); Center for Statistical Science & Department of Industrial Engineering, Tsinghua University, Beijing 100084, China. Tel: +86 010-62782453; e-mail: (Ke Deng)
| | - Jian Zhou
- Department of Thoracic Surgery, Peking University People's Hospital, Beijing, China
- Corresponding authors: Department of Thoracic Surgery, Peking University People's Hospital, Beijing 100044, China. Tel: +86 010-88326650; e-mail: (Dr. Jian Zhou); Department of Thoracic Surgery, Peking University People's Hospital, Beijing 100044, China. Tel: 010-88326652; e-mail: (Dr. Jun Wang); Center for Statistical Science & Department of Industrial Engineering, Tsinghua University, Beijing 100084, China. Tel: +86 010-62782453; e-mail: (Ke Deng)
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Ayyaz MS, Lali MIU, Hussain M, Rauf HT, Alouffi B, Alyami H, Wasti S. Hybrid Deep Learning Model for Endoscopic Lesion Detection and Classification Using Endoscopy Videos. Diagnostics (Basel) 2021; 12:diagnostics12010043. [PMID: 35054210 PMCID: PMC8775223 DOI: 10.3390/diagnostics12010043] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2021] [Revised: 12/22/2021] [Accepted: 12/23/2021] [Indexed: 02/06/2023] Open
Abstract
In medical imaging, the detection and classification of stomach diseases are challenging due to the resemblance of different symptoms, image contrast, and complex background. Computer-aided diagnosis (CAD) plays a vital role in the medical imaging field, allowing accurate results to be obtained in minimal time. This article proposes a new hybrid method to detect and classify stomach diseases using endoscopy videos. The proposed methodology comprises seven significant steps: data acquisition, preprocessing of data, transfer learning of deep models, feature extraction, feature selection, hybridization, and classification. We selected two different CNN models (VGG19 and Alexnet) to extract features. We applied transfer learning techniques before using them as feature extractors. We used a genetic algorithm (GA) in feature selection, due to its adaptive nature. We fused selected features of both models using a serial-based approach. Finally, the best features were provided to multiple machine learning classifiers for detection and classification. The proposed approach was evaluated on a personally collected dataset of five classes, including gastritis, ulcer, esophagitis, bleeding, and healthy. We observed that the proposed technique performed superbly on Cubic SVM with 99.8% accuracy. For the authenticity of the proposed technique, we considered these statistical measures: classification accuracy, recall, precision, False Negative Rate (FNR), Area Under the Curve (AUC), and time. In addition, we provided a fair state-of-the-art comparison of our proposed technique with existing techniques that proves its worthiness.
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Affiliation(s)
- M Shahbaz Ayyaz
- Department of Computer Science, University of Gujrat, Gujrat 50700, Pakistan; (M.S.A.); (M.H.)
| | - Muhammad Ikram Ullah Lali
- Department of Information Sciences, University of Education Lahore, Lahore 41000, Pakistan; (M.I.U.L.); (S.W.)
| | - Mubbashar Hussain
- Department of Computer Science, University of Gujrat, Gujrat 50700, Pakistan; (M.S.A.); (M.H.)
| | - Hafiz Tayyab Rauf
- Centre for Smart Systems, AI and Cybersecurity, Staffordshire University, Stoke-on-Trent ST4 2DE, UK
- Correspondence:
| | - Bader Alouffi
- Department of Computer Science, College of Computers and Information Technology, Taif University, P. O. Box 11099, Taif 21944, Saudi Arabia; (B.A.); (H.A.)
| | - Hashem Alyami
- Department of Computer Science, College of Computers and Information Technology, Taif University, P. O. Box 11099, Taif 21944, Saudi Arabia; (B.A.); (H.A.)
| | - Shahbaz Wasti
- Department of Information Sciences, University of Education Lahore, Lahore 41000, Pakistan; (M.I.U.L.); (S.W.)
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15
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Kröner PT, Engels MML, Glicksberg BS, Johnson KW, Mzaik O, van Hooft JE, Wallace MB, El-Serag HB, Krittanawong C. Artificial intelligence in gastroenterology: A state-of-the-art review. World J Gastroenterol 2021; 27:6794-6824. [PMID: 34790008 PMCID: PMC8567482 DOI: 10.3748/wjg.v27.i40.6794] [Citation(s) in RCA: 74] [Impact Index Per Article: 18.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/11/2021] [Revised: 06/15/2021] [Accepted: 09/16/2021] [Indexed: 02/06/2023] Open
Abstract
The development of artificial intelligence (AI) has increased dramatically in the last 20 years, with clinical applications progressively being explored for most of the medical specialties. The field of gastroenterology and hepatology, substantially reliant on vast amounts of imaging studies, is not an exception. The clinical applications of AI systems in this field include the identification of premalignant or malignant lesions (e.g., identification of dysplasia or esophageal adenocarcinoma in Barrett’s esophagus, pancreatic malignancies), detection of lesions (e.g., polyp identification and classification, small-bowel bleeding lesion on capsule endoscopy, pancreatic cystic lesions), development of objective scoring systems for risk stratification, predicting disease prognosis or treatment response [e.g., determining survival in patients post-resection of hepatocellular carcinoma), determining which patients with inflammatory bowel disease (IBD) will benefit from biologic therapy], or evaluation of metrics such as bowel preparation score or quality of endoscopic examination. The objective of this comprehensive review is to analyze the available AI-related studies pertaining to the entirety of the gastrointestinal tract, including the upper, middle and lower tracts; IBD; the hepatobiliary system; and the pancreas, discussing the findings and clinical applications, as well as outlining the current limitations and future directions in this field.
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Affiliation(s)
- Paul T Kröner
- Division of Gastroenterology and Hepatology, Mayo Clinic, Jacksonville, FL 32224, United States
| | - Megan ML Engels
- Division of Gastroenterology and Hepatology, Mayo Clinic, Jacksonville, FL 32224, United States
- Cancer Center Amsterdam, Department of Gastroenterology and Hepatology, Amsterdam UMC, Location AMC, Amsterdam 1105, The Netherlands
| | - Benjamin S Glicksberg
- The Hasso Plattner Institute for Digital Health, Icahn School of Medicine at Mount Sinai, New York, NY 10029, United States
| | - Kipp W Johnson
- The Hasso Plattner Institute for Digital Health, Icahn School of Medicine at Mount Sinai, New York, NY 10029, United States
| | - Obaie Mzaik
- Division of Gastroenterology and Hepatology, Mayo Clinic, Jacksonville, FL 32224, United States
| | - Jeanin E van Hooft
- Department of Gastroenterology and Hepatology, Leiden University Medical Center, Amsterdam 2300, The Netherlands
| | - Michael B Wallace
- Division of Gastroenterology and Hepatology, Mayo Clinic, Jacksonville, FL 32224, United States
- Division of Gastroenterology and Hepatology, Sheikh Shakhbout Medical City, Abu Dhabi 11001, United Arab Emirates
| | - Hashem B El-Serag
- Section of Gastroenterology and Hepatology, Michael E. DeBakey VA Medical Center and Baylor College of Medicine, Houston, TX 77030, United States
- Section of Health Services Research, Michael E. DeBakey VA Medical Center and Baylor College of Medicine, Houston, TX 77030, United States
| | - Chayakrit Krittanawong
- Section of Health Services Research, Michael E. DeBakey VA Medical Center and Baylor College of Medicine, Houston, TX 77030, United States
- Section of Cardiology, Michael E. DeBakey VA Medical Center, Houston, TX 77030, United States
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16
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Mascarenhas Saraiva M, Ribeiro T, Afonso J, Ferreira JP, Cardoso H, Andrade P, Parente MP, Jorge RN, Macedo G. Artificial Intelligence and Capsule Endoscopy: Automatic Detection of Small Bowel Blood Content Using a Convolutional Neural Network. GE-PORTUGUESE JOURNAL OF GASTROENTEROLOGY 2021; 29:331-338. [PMID: 36159196 PMCID: PMC9485980 DOI: 10.1159/000518901] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/18/2021] [Accepted: 07/14/2021] [Indexed: 12/22/2022]
Abstract
Introduction Capsule endoscopy has revolutionized the management of patients with obscure gastrointestinal bleeding. Nevertheless, reading capsule endoscopy images is time-consuming and prone to overlooking significant lesions, thus limiting its diagnostic yield. We aimed to create a deep learning algorithm for automatic detection of blood and hematic residues in the enteric lumen in capsule endoscopy exams. Methods A convolutional neural network was developed based on a total pool of 22,095 capsule endoscopy images (13,510 images containing luminal blood and 8,585 of normal mucosa or other findings). A training dataset comprising 80% of the total pool of images was defined. The performance of the network was compared to a consensus classification provided by 2 specialists in capsule endoscopy. Subsequently, we evaluated the performance of the network using an independent validation dataset (20% of total image pool), calculating its sensitivity, specificity, accuracy, and precision. Results Our convolutional neural network detected blood and hematic residues in the small bowel lumen with an accuracy and precision of 98.5 and 98.7%, respectively. The sensitivity and specificity were 98.6 and 98.9%, respectively. The analysis of the testing dataset was completed in 24 s (approximately 184 frames/s). Discussion/Conclusion We have developed an artificial intelligence tool capable of effectively detecting luminal blood. The development of these tools may enhance the diagnostic accuracy of capsule endoscopy when evaluating patients presenting with obscure small bowel bleeding.
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Affiliation(s)
- Miguel Mascarenhas Saraiva
- Department of Gastroenterology, São João University Hospital, Porto, Portugal
- WGO Gastroenterology and Hepatology Training Center, Porto, Portugal
- Faculty of Medicine of the University of Porto, Porto, Portugal
- * Miguel Mascarenhas Saraiva,
| | - Tiago Ribeiro
- Department of Gastroenterology, São João University Hospital, Porto, Portugal
- WGO Gastroenterology and Hepatology Training Center, Porto, Portugal
| | - João Afonso
- Department of Gastroenterology, São João University Hospital, Porto, Portugal
- WGO Gastroenterology and Hepatology Training Center, Porto, Portugal
| | - João P.S. Ferreira
- Department of Mechanical Engineering, Faculty of Engineering of the University of Porto, Porto, Portugal
- INEGI − Institute of Science and Innovation in Mechanical and Industrial Engineering, Porto, Portugal
| | - Hélder Cardoso
- Department of Gastroenterology, São João University Hospital, Porto, Portugal
- WGO Gastroenterology and Hepatology Training Center, Porto, Portugal
- Faculty of Medicine of the University of Porto, Porto, Portugal
| | - Patrícia Andrade
- Department of Gastroenterology, São João University Hospital, Porto, Portugal
- WGO Gastroenterology and Hepatology Training Center, Porto, Portugal
- Faculty of Medicine of the University of Porto, Porto, Portugal
| | - Marco P.L. Parente
- Department of Mechanical Engineering, Faculty of Engineering of the University of Porto, Porto, Portugal
- INEGI − Institute of Science and Innovation in Mechanical and Industrial Engineering, Porto, Portugal
| | - Renato N. Jorge
- Department of Mechanical Engineering, Faculty of Engineering of the University of Porto, Porto, Portugal
- INEGI − Institute of Science and Innovation in Mechanical and Industrial Engineering, Porto, Portugal
| | - Guilherme Macedo
- Department of Gastroenterology, São João University Hospital, Porto, Portugal
- WGO Gastroenterology and Hepatology Training Center, Porto, Portugal
- Faculty of Medicine of the University of Porto, Porto, Portugal
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17
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Oka A, Ishimura N, Ishihara S. A New Dawn for the Use of Artificial Intelligence in Gastroenterology, Hepatology and Pancreatology. Diagnostics (Basel) 2021; 11:1719. [PMID: 34574060 PMCID: PMC8468082 DOI: 10.3390/diagnostics11091719] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Revised: 09/17/2021] [Accepted: 09/17/2021] [Indexed: 12/15/2022] Open
Abstract
Artificial intelligence (AI) is rapidly becoming an essential tool in the medical field as well as in daily life. Recent developments in deep learning, a subfield of AI, have brought remarkable advances in image recognition, which facilitates improvement in the early detection of cancer by endoscopy, ultrasonography, and computed tomography. In addition, AI-assisted big data analysis represents a great step forward for precision medicine. This review provides an overview of AI technology, particularly for gastroenterology, hepatology, and pancreatology, to help clinicians utilize AI in the near future.
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Affiliation(s)
- Akihiko Oka
- Department of Internal Medicine II, Faculty of Medicine, Shimane University, Izumo 693-8501, Shimane, Japan; (N.I.); (S.I.)
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18
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Feng LX, Li X, Wang HY, Zheng WY, Zhang YQ, Gao DR, Wang MQ. Automatic Sleep Staging Algorithm Based on Time Attention Mechanism. Front Hum Neurosci 2021; 15:692054. [PMID: 34483864 PMCID: PMC8416031 DOI: 10.3389/fnhum.2021.692054] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2021] [Accepted: 07/26/2021] [Indexed: 11/30/2022] Open
Abstract
The most important part of sleep quality assessment is the automatic classification of sleep stages. Sleep staging is helpful in the diagnosis of sleep-related diseases. This study proposes an automatic sleep staging algorithm based on the time attention mechanism. Time-frequency and non-linear features are extracted from the physiological signals of six channels and then normalized. The time attention mechanism combined with the two-way bi-directional gated recurrent unit (GRU) was used to reduce computing resources and time costs, and the conditional random field (CRF) was used to obtain information between tags. After five-fold cross-validation on the Sleep-EDF dataset, the values of accuracy, WF1, and Kappa were 0.9218, 0.9177, and 0.8751, respectively. After five-fold cross-validation on the our own dataset, the values of accuracy, WF1, and Kappa were 0.9006, 0.8991, and 0.8664, respectively, which is better than the result of the latest algorithm. In the study of sleep staging, the recognition rate of the N1 stage was low, and the imbalance has always been a problem. Therefore, this study introduces a type of balancing strategy. By adopting the proposed strategy, SEN-N1 and ACC of 0.7 and 0.86, respectively, can be achieved. The experimental results show that compared to the latest method, the proposed model can achieve significantly better performance and significantly improve the recognition rate of the N1 period. The performance comparison of different channels shows that even when the EEG channel was not used, considerable accuracy can be obtained.
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Affiliation(s)
- Li-Xiao Feng
- Department of Computer Science, Chengdu University of Information Technology, Chengdu, China
| | - Xin Li
- Department of Computer Science, Chengdu University of Information Technology, Chengdu, China
| | - Hong-Yu Wang
- Department of Computer Science, Chengdu University of Information Technology, Chengdu, China
| | - Wen-Yin Zheng
- Department of Computer Science, Chengdu University of Information Technology, Chengdu, China
| | - Yong-Qing Zhang
- Department of Computer Science, Chengdu University of Information Technology, Chengdu, China
- Department of Biological Engineering, University of Electronic Science and Technology of China, Chengdu, China
| | - Dong-Rui Gao
- Department of Computer Science, Chengdu University of Information Technology, Chengdu, China
- Department of Biological Engineering, University of Electronic Science and Technology of China, Chengdu, China
| | - Man-Qing Wang
- Department of Computer Science, Chengdu University of Information Technology, Chengdu, China
- Department of Biological Engineering, University of Electronic Science and Technology of China, Chengdu, China
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19
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Li H, Vong CM, Wong PK, Ip WF, Yan T, Choi IC, Yu HH. A multi-feature fusion method for image recognition of gastrointestinal metaplasia (GIM). Biomed Signal Process Control 2021; 69:102909. [DOI: 10.1016/j.bspc.2021.102909] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
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20
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Berbís MA, Aneiros-Fernández J, Mendoza Olivares FJ, Nava E, Luna A. Role of artificial intelligence in multidisciplinary imaging diagnosis of gastrointestinal diseases. World J Gastroenterol 2021; 27:4395-4412. [PMID: 34366612 PMCID: PMC8316909 DOI: 10.3748/wjg.v27.i27.4395] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/28/2021] [Revised: 04/14/2021] [Accepted: 06/07/2021] [Indexed: 02/06/2023] Open
Abstract
The use of artificial intelligence-based tools is regarded as a promising approach to increase clinical efficiency in diagnostic imaging, improve the interpretability of results, and support decision-making for the detection and prevention of diseases. Radiology, endoscopy and pathology images are suitable for deep-learning analysis, potentially changing the way care is delivered in gastroenterology. The aim of this review is to examine the key aspects of different neural network architectures used for the evaluation of gastrointestinal conditions, by discussing how different models behave in critical tasks, such as lesion detection or characterization (i.e. the distinction between benign and malignant lesions of the esophagus, the stomach and the colon). To this end, we provide an overview on recent achievements and future prospects in deep learning methods applied to the analysis of radiology, endoscopy and histologic whole-slide images of the gastrointestinal tract.
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Affiliation(s)
| | - José Aneiros-Fernández
- Department of Pathology, Hospital Universitario Clínico San Cecilio, Granada 18012, Spain
| | | | - Enrique Nava
- Department of Communications Engineering, University of Málaga, Malaga 29016, Spain
| | - Antonio Luna
- MRI Unit, Department of Radiology, HT Médica, Jaén 23007, Spain
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21
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Tziortziotis I, Laskaratos FM, Coda S. Role of Artificial Intelligence in Video Capsule Endoscopy. Diagnostics (Basel) 2021; 11:1192. [PMID: 34209029 PMCID: PMC8303156 DOI: 10.3390/diagnostics11071192] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2021] [Accepted: 06/28/2021] [Indexed: 02/06/2023] Open
Abstract
Capsule endoscopy (CE) has been increasingly utilised in recent years as a minimally invasive tool to investigate the whole gastrointestinal (GI) tract and a range of capsules are currently available for evaluation of upper GI, small bowel, and lower GI pathology. Although CE is undoubtedly an invaluable test for the investigation of small bowel pathology, it presents considerable challenges and limitations, such as long and laborious reading times, risk of missing lesions, lack of bowel cleansing score and lack of locomotion. Artificial intelligence (AI) seems to be a promising tool that may help improve the performance metrics of CE, and consequently translate to better patient care. In the last decade, significant progress has been made to apply AI in the field of endoscopy, including CE. Although it is certain that AI will find soon its place in day-to-day endoscopy clinical practice, there are still some open questions and barriers limiting its widespread application. In this review, we provide some general information about AI, and outline recent advances in AI and CE, issues around implementation of AI in medical practice and potential future applications of AI-aided CE.
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Affiliation(s)
- Ioannis Tziortziotis
- Endoscopy Unit, Digestive Diseases Centre, Queen’s Hospital, Barking Havering and Redbridge University Hospitals NHS Trust, Rom Valley Way, Romford, London RM7 0AG, UK; (I.T.); (S.C.)
| | - Faidon-Marios Laskaratos
- Endoscopy Unit, Digestive Diseases Centre, Queen’s Hospital, Barking Havering and Redbridge University Hospitals NHS Trust, Rom Valley Way, Romford, London RM7 0AG, UK; (I.T.); (S.C.)
| | - Sergio Coda
- Endoscopy Unit, Digestive Diseases Centre, Queen’s Hospital, Barking Havering and Redbridge University Hospitals NHS Trust, Rom Valley Way, Romford, London RM7 0AG, UK; (I.T.); (S.C.)
- Photonics Group-Department of Physics, Imperial College London, Exhibition Rd, South Kensington, London SW7 2BX, UK
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22
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Wang RG. Progress and prospects of artificial intelligence in colonoscopy. Artif Intell Gastrointest Endosc 2021; 2:63-70. [DOI: 10.37126/aige.v2.i3.63] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/29/2021] [Revised: 05/29/2021] [Accepted: 06/23/2021] [Indexed: 02/06/2023] Open
Abstract
Artificial intelligence (AI) is a branch of computer science. As a new technological science, it mainly develops and expands human intelligence through the research of intelligence theory, methods and technology. In the medical field, AI has bright application prospects (for example: imaging, diagnosis and treatment). The exploration of robotic gastroscopy and colonoscopy systems is not only a bold attempt, but also an inevitable trend of AI in the development of digestive endoscopy in the future. Based on the current research findings, this article summarizes the research progress of colonoscopy, and looking forward for the application of AI in colonoscopy.
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Affiliation(s)
- Rui-Gang Wang
- Department of Gastroenterology, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, Beijing 102218, China
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23
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Tan ZZ, Li XD, Kong CD, Sha N, Hou YN, Zhao KH. Engineering Bacteria to Monitor the Bleeding of Animals Using Far-Red Fluorescence. ACS Sens 2021; 6:1770-1778. [PMID: 33978416 DOI: 10.1021/acssensors.0c02482] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Microorganisms living in animals can function as drug delivery systems or as detectors for some diseases. Here, we developed a biosensor constructed by the deletion of hemF and harboring ho1, chuA, and bdfp1.6 in Escherichia coli. HemF is an enzyme involved in heme synthesis in E. coli. ChuA and HO1 can transfer extracellular heme into cells and generate biliverdin (BV). BDFP1.6 can bind BV autocatalytically, and it emits a far-red fluorescence signal at 667 nm. Therefore, we named this biosensor as the far-red light for bleeding detector (FRLBD). Our results indicated that the FRLBD was highly efficient and specific for detecting heme or blood in vitro. Moreover, the FRLBD could be used to detect bleeding in the zebrafish induced by aspirin, and a convolutional neural network was an appropriate model to identify the fluorescence features in the images.
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Affiliation(s)
- Zi-Zhu Tan
- State Key Laboratory of Agricultural Microbiology, Huazhong Agricultural University, Wuhan 430070, P.R. China
| | - Xiao-Dan Li
- State Key Laboratory of Agricultural Microbiology, Huazhong Agricultural University, Wuhan 430070, P.R. China
| | - Chao-Di Kong
- State Key Laboratory of Agricultural Microbiology, Huazhong Agricultural University, Wuhan 430070, P.R. China
| | - Na Sha
- State Key Laboratory of Agricultural Microbiology, Huazhong Agricultural University, Wuhan 430070, P.R. China
| | - Ya-Nan Hou
- State Key Laboratory of Agricultural Microbiology, Huazhong Agricultural University, Wuhan 430070, P.R. China
| | - Kai-Hong Zhao
- State Key Laboratory of Agricultural Microbiology, Huazhong Agricultural University, Wuhan 430070, P.R. China
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24
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Kumar R, Khan FU, Sharma A, Aziz IB, Poddar NK. Recent Applications of Artificial Intelligence in detection of Gastrointestinal, Hepatic and Pancreatic Diseases. Curr Med Chem 2021; 29:66-85. [PMID: 33820515 DOI: 10.2174/0929867328666210405114938] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2020] [Revised: 02/25/2021] [Accepted: 03/06/2021] [Indexed: 11/22/2022]
Abstract
There is substantial progress in artificial intelligence (AI) algorithms and their medical sciences applications in the last two decades. AI-assisted programs have already been established for remotely health monitoring using sensors and smartphones. A variety of AI-based prediction models available for the gastrointestinal inflammatory, non-malignant diseases, and bowel bleeding using wireless capsule endoscopy, electronic medical records for hepatitis-associated fibrosis, pancreatic carcinoma using endoscopic ultrasounds. AI-based models may be of immense help for healthcare professionals in the identification, analysis, and decision support using endoscopic images to establish prognosis and risk assessment of patient's treatment using multiple factors. Although enough randomized clinical trials are warranted to establish the efficacy of AI-algorithms assisted and non-AI based treatments before approval of such techniques from medical regulatory authorities. In this article, available AI approaches and AI-based prediction models for detecting gastrointestinal, hepatic, and pancreatic diseases are reviewed. The limitation of AI techniques in such disease prognosis, risk assessment, and decision support are discussed.
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Affiliation(s)
- Rajnish Kumar
- Amity Institute of Biotechnology, Amity University Uttar Pradesh Lucknow Campus, Uttar Pradesh. India
| | - Farhat Ullah Khan
- Computer and Information Sciences Department, Universiti Teknologi Petronas, 32610, Seri Iskander, Perak. Malaysia
| | - Anju Sharma
- Department of Applied Science, Indian Institute of Information Technology, Allahabad, Uttar Pradesh. India
| | - Izzatdin Ba Aziz
- Computer and Information Sciences Department, Universiti Teknologi Petronas, 32610, Seri Iskander, Perak. Malaysia
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25
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Small Bowel Capsule Endoscopy and artificial intelligence: First or second reader? Best Pract Res Clin Gastroenterol 2021; 52-53:101742. [PMID: 34172256 DOI: 10.1016/j.bpg.2021.101742] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/10/2021] [Accepted: 03/17/2021] [Indexed: 01/31/2023]
Abstract
Several machine learning algorithms have been developed in the past years with the aim to improve SBCE (Small Bowel Capsule Endoscopy) feasibility ensuring at the same time a high diagnostic accuracy. If past algorithms were affected by low performances and unsatisfactory accuracy, deep learning systems raised up the expectancy of effective AI (Artificial Intelligence) application in SBCE reading. Automatic detection and characterization of lesions, such as angioectasias, erosions and ulcers, would significantly shorten reading time other than improve reader attention during SBCE review in routine activity. It is debated whether AI can be used as first or second reader. This issue should be further investigated measuring accuracy and cost-effectiveness of AI systems. Currently, AI has been mostly evaluated as first reader. However, second reading may play an important role in SBCE training as well as for better characterizing lesions for which the first reader was uncertain.
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26
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Mascarenhas M, Afonso J, Andrade P, Cardoso H, Macedo G. Artificial intelligence and capsule endoscopy: unravelling the future. Ann Gastroenterol 2021; 34:300-309. [PMID: 33948053 PMCID: PMC8079882 DOI: 10.20524/aog.2021.0606] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/26/2020] [Accepted: 12/20/2020] [Indexed: 12/22/2022] Open
Abstract
The applicability of artificial intelligence (AI) in gastroenterology is a hot topic because of its disruptive nature. Capsule endoscopy plays an important role in several areas of digestive pathology, namely in the investigation of obscure hemorrhagic lesions and the management of inflammatory bowel disease. Therefore, there is growing interest in the use of AI in capsule endoscopy. Several studies have demonstrated the enormous potential of using convolutional neural networks in various areas of capsule endoscopy. The exponential development of the usefulness of AI in capsule endoscopy requires consideration of its medium- and long-term impact on clinical practice. Indeed, the advent of deep learning in the field of capsule endoscopy, with its evolutionary character, could lead to a paradigm shift in clinical activity in this setting. In this review, we aim to illustrate the state of the art of AI in the field of capsule endoscopy.
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Affiliation(s)
| | - João Afonso
- Gastroenterology Department, Hospital de São João, Porto, Portugal
| | - Patrícia Andrade
- Gastroenterology Department, Hospital de São João, Porto, Portugal
| | - Hélder Cardoso
- Gastroenterology Department, Hospital de São João, Porto, Portugal
| | - Guilherme Macedo
- Gastroenterology Department, Hospital de São João, Porto, Portugal
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27
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Intelligent automated drug administration and therapy: future of healthcare. Drug Deliv Transl Res 2021; 11:1878-1902. [PMID: 33447941 DOI: 10.1007/s13346-020-00876-4] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/09/2020] [Indexed: 12/13/2022]
Abstract
In the twenty-first century, the collaboration of control engineering and the healthcare sector has matured to some extent; however, the future will have promising opportunities, vast applications, and some challenges. Due to advancements in processing speed, the closed-loop administration of drugs has gained popularity for critically ill patients in intensive care units and routine life such as personalized drug delivery or implantable therapeutic devices. For developing a closed-loop drug delivery system, the control system works with a group of technologies like sensors, micromachining, wireless technologies, and pharmaceuticals. Recently, the integration of artificial intelligence techniques such as fuzzy logic, neural network, and reinforcement learning with the closed-loop drug delivery systems has brought their applications closer to fully intelligent automatic healthcare systems. This review's main objectives are to discuss the current developments, possibilities, and future visions in closed-loop drug delivery systems, for providing treatment to patients suffering from chronic diseases. It summarizes the present insight of closed-loop drug delivery/therapy for diabetes, gastrointestinal tract disease, cancer, anesthesia administration, cardiac ailments, and neurological disorders, from a perspective to show the research in the area of control theory.
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28
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Phillips F, Beg S. Video capsule endoscopy: pushing the boundaries with software technology. Transl Gastroenterol Hepatol 2021; 6:17. [PMID: 33409411 DOI: 10.21037/tgh.2020.02.01] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/29/2019] [Accepted: 12/11/2019] [Indexed: 12/14/2022] Open
Abstract
Video capsule endoscopy (VCE) has transformed imaging of the small bowel as it is a non-invasive and well tolerated modality with excellent diagnostic capabilities. The way we read VCE has not changed much since its introduction nearly two decades ago. Reading is still very time intensive and prone to reader error. This review outlines the evidence regarding software enhancements which aim to address these challenges. These include the suspected blood indicator (SBI), automated fast viewing modes including QuickView, lesion characterization tools such Fuji Intelligent Color Enhancement, and three-dimensional (3D) representation tools. We also outline the exciting new evidence of artificial intelligence (AI) and deep learning (DL), which promises to revolutionize capsule reading. DL algorithms have been developed for identifying organs of origin, intestinal motility events, active bleeding, coeliac disease, polyp detection, hookworms and angioectasias, all with impressively high sensitivity and accuracy. More recently, an algorithm has been created to detect multiple abnormalities with a sensitivity of 99.9% and reading time of only 5.9 minutes. These algorithms will need to be validated robustly. However, it will not be long before we see this in clinical practice, aiding the clinician in rapid and accurate diagnosis.
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Affiliation(s)
- Frank Phillips
- Department of Gastroenterology, NIHR Nottingham Digestive Diseases Biomedical Research Centre, Queens Medical Centre Campus, Nottingham University Hospitals NHS Trust, Nottingham, UK
| | - Sabina Beg
- Department of Gastroenterology, NIHR Nottingham Digestive Diseases Biomedical Research Centre, Queens Medical Centre Campus, Nottingham University Hospitals NHS Trust, Nottingham, UK
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29
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Meher D, Gogoi M, Bharali P, Anirvan P, Singh SP. Artificial Intelligence in Small Bowel Endoscopy: Current Perspectives and Future Directions. JOURNAL OF DIGESTIVE ENDOSCOPY 2020. [DOI: 10.1055/s-0040-1717824] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/07/2023] Open
Abstract
AbstractArtificial intelligence (AI) is a computer system that is able to perform tasks which normally require human intelligence. The role of AI in the field of gastroenterology has been gradually evolving since its inception in the 1950s. Discovery of wireless capsule endoscopy (WCE) and balloon enteroscopy (BE) has revolutionized small gut imaging. While WCE is a relatively patient-friendly and noninvasive mode to examine the nonobstructed small gut, it is limited by a lengthy examination time and the need for expertise in reading images acquired by the capsule. Similarly, BE, despite having the advantage of therapeutic intervention, is costly, invasive, and requires general sedation. Incorporation of concepts like machine learning and deep learning has been used to handle large amounts of data and images in gastroenterology. Interestingly, in small gut imaging, the application of AI has been limited to WCE only. This review was planned to examine and summarize available published data on various AI-based approaches applied to small bowel disease.
We conducted an extensive literature search using Google search engine, Google Scholar, and PubMed database for published literature in English on the application of different AI techniques in small bowel endoscopy, and have summarized the outcome and benefits of these applications of AI in small bowel endoscopy. Incorporation of AI in WCE has resulted in significant advancements in the detection of various lesions starting from dysplastic mucosa, inflammatory and nonmalignant lesions to the detection of bleeding with increasing accuracy and has shortened the lengthy review time in image analysis. As most of the studies to evaluate AI are retrospective, the presence of inherent selection bias cannot be excluded. Besides, the interpretability (black-box nature) of AI models remains a cause for concern. Finally, issues related to medical ethics and AI need to be judiciously addressed to enable its seamless use in future.
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Affiliation(s)
- Dinesh Meher
- Department of Gastroenterology, S.C.B. Medical College, Cuttack, Odisha, India
| | - Mrinal Gogoi
- Department of Gastroenterology, S.C.B. Medical College, Cuttack, Odisha, India
| | - Pankaj Bharali
- Department of Gastroenterology, S.C.B. Medical College, Cuttack, Odisha, India
| | - Prajna Anirvan
- Department of Gastroenterology, S.C.B. Medical College, Cuttack, Odisha, India
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Abstract
Artificial intelligence (AI) is now a trendy subject in clinical medicine and especially in gastrointestinal (GI) endoscopy. AI has the potential to improve the quality of GI endoscopy at all levels. It will compensate for humans' errors and limited capabilities by bringing more accuracy, consistency, and higher speed, making endoscopic procedures more efficient and of higher quality. AI showed great results in diagnostic and therapeutic endoscopy in all parts of the GI tract. More studies are still needed before the introduction of this new technology in our daily practice and clinical guidelines. Furthermore, ethical clearance and new legislations might be needed. In conclusion, the introduction of AI will be a big breakthrough in the field of GI endoscopy in the upcoming years. It has the potential to bring major improvements to GI endoscopy at all levels.
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Affiliation(s)
- Ahmad El Hajjar
- Department of Gastroenterology and Digestive Endoscopy, Arnault Tzanck Institute, Saint-Laurent du Var 06700, France
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Jani KK, Srivastava R. A Survey on Medical Image Analysis in Capsule Endoscopy. Curr Med Imaging 2020; 15:622-636. [PMID: 32008510 DOI: 10.2174/1573405614666181102152434] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2018] [Revised: 10/14/2018] [Accepted: 10/22/2018] [Indexed: 02/06/2023]
Abstract
BACKGROUND AND OBJECTIVE Capsule Endoscopy (CE) is a non-invasive, patient-friendly alternative to conventional endoscopy procedure. However, CE produces 6 to 8 hrs long video posing a tedious challenge to a gastroenterologist for abnormality detection. Major challenges to an expert are lengthy videos, need of constant concentration and subjectivity of the abnormality. To address these challenges along with high diagnostic accuracy, design and development of automated abnormality detection system is a must. Machine learning and computer vision techniques are devised to develop such automated systems. METHODS Study presents a review of quality research papers published in IEEE, Scopus, and Science Direct database with search criteria as capsule endoscopy, engineering, and journal papers. The initial search retrieved 144 publications. After evaluating all articles, 62 publications pertaining to image analysis are selected. RESULTS This paper presents a rigorous review comprising all the aspects of medical image analysis concerning capsule endoscopy namely video summarization and redundant image elimination, Image enhancement and interpretation, segmentation and region identification, Computer-aided abnormality detection in capsule endoscopy, Image and video compression. The study provides a comparative analysis of various approaches, experimental setup, performance, strengths, and limitations of the aspects stated above. CONCLUSIONS The analyzed image analysis techniques for capsule endoscopy have not yet overcome all current challenges mainly due to lack of dataset and complex nature of the gastrointestinal tract.
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Affiliation(s)
- Kuntesh Ketan Jani
- Computer Science and Engineering Department, Indian Institute of Technology (Banaras Hindu University) Varanasi, Varanasi, Uttar Pradesh, India
| | - Rajeev Srivastava
- Computer Science and Engineering Department, Indian Institute of Technology (Banaras Hindu University) Varanasi, Varanasi, Uttar Pradesh, India
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Parasa S, Wallace M, Bagci U, Antonino M, Berzin T, Byrne M, Celik H, Farahani K, Golding M, Gross S, Jamali V, Mendonca P, Mori Y, Ninh A, Repici A, Rex D, Skrinak K, Thakkar SJ, van Hooft JE, Vargo J, Yu H, Xu Z, Sharma P. Proceedings from the First Global Artificial Intelligence in Gastroenterology and Endoscopy Summit. Gastrointest Endosc 2020; 92:938-945.e1. [PMID: 32343978 DOI: 10.1016/j.gie.2020.04.044] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/29/2020] [Accepted: 04/16/2020] [Indexed: 02/08/2023]
Abstract
BACKGROUND AND AIMS Artificial intelligence (AI), specifically deep learning, offers the potential to enhance the field of GI endoscopy in areas ranging from lesion detection and classification to quality metrics and documentation. Progress in this field will be measured by whether AI implementation can lead to improved patient outcomes and more efficient clinical workflow for GI endoscopists. The aims of this article are to report the findings of a multidisciplinary group of experts focusing on issues in AI research and applications related to gastroenterology and endoscopy, to review the current status of the field, and to produce recommendations for investigators developing and studying new AI technologies for gastroenterology. METHODS A multidisciplinary meeting was held on September 28, 2019, bringing together academic, industry, and regulatory experts in diverse fields including gastroenterology, computer and imaging sciences, machine learning, computer vision, U.S. Food and Drug Administration, and the National Institutes of Health. Recent and ongoing studies in gastroenterology and current technology in AI were presented and discussed, key gaps in knowledge were identified, and recommendations were made for research that would have the highest impact in making advances and implementation in the field of AI to gastroenterology. RESULTS There was a consensus that AI will transform the field of gastroenterology, particularly endoscopy and image interpretation. Powered by advanced machine learning algorithms, the use of computer vision in endoscopy has the potential to result in better prediction and treatment outcomes for patients with gastroenterology disorders and cancer. Large libraries of endoscopic images, "EndoNet," will be important to facilitate development and application of AI systems. The regulatory environment for implementation of AI systems is evolving, but common outcomes such as colon polyp detection have been highlighted as potential clinical trial endpoints. Other threshold outcomes will be important, as well as clarity on iterative improvement of clinical systems. CONCLUSIONS Gastroenterology is a prime candidate for early adoption of AI. AI is rapidly moving from an experimental phase to a clinical implementation phase in gastroenterology. It is anticipated that the implementation of AI in gastroenterology over the next decade will have a significant and positive impact on patient care and clinical workflows. Ongoing collaboration among gastroenterologists, industry experts, and regulatory agencies will be important to ensure that progress is rapid and clinically meaningful. However, several constraints and areas will benefit from further exploration, including potential clinical applications, implementation, structure and governance, role of gastroenterologists, and potential impact of AI in gastroenterology.
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Affiliation(s)
- Sravanthi Parasa
- Department of Gastroenterology, Swedish Medical Center, Seattle, Washington, USA
| | - Michael Wallace
- Department of Medicine, Mayo Clinic, Director, Digestive Diseases Research Program, Editor in Chief Gastrointestinal Endoscopy, President, Florida Gastroenterology Society, Jacksonville, Florida, USA
| | - Ulas Bagci
- Artificial Intelligence in Medicine (AIM), Center for Research in Computer Vision, University of Central Florida, Orlando, Florida, USA
| | - Mark Antonino
- Gastroenterology and Endoscopy Devices Team, Division of Renal, Gastrointestinal, Obesity and Transplant Devices, Office of Gastrorenal, ObGyn, General Hospital and Urology Devices, Office of Product Evaluation and Quality, Center for Devices and Radiological Health, U.S. Food and Drug Administration, Silver Spring, Maryland, USA
| | - Tyler Berzin
- Department of Medicine, Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA
| | - Michael Byrne
- Division of Gastroenterology, Vancouver General Hospital/University of British Columbia, Vancouver, British Columbia, Canada
| | - Haydar Celik
- Clinical Center, National Institutes of Health, Bethesda, Maryland, USA; George Washington University, Washington, DC, USA
| | - Keyvan Farahani
- Image-Guided Interventions and Imaging Informatics, National Cancer Institute, National Institutes of Health, Rockville, Maryland, USA
| | - Martin Golding
- Gastroenterology and Endoscopy Devices Team, Division of Renal, Gastrointestinal, Obesity and Transplant Devices, Office of Gastrorenal, ObGyn, General Hospital and Urology Devices, Office of Product Evaluation and Quality, Center for Devices and Radiological Health, U.S. Food and Drug Administration, Silver Spring, Maryland, USA
| | - Seth Gross
- Department of Medicine, Division of Gastroenterology, Clinical Care and Quality, NYU Langone Health, New York, New York, USA
| | - Vafa Jamali
- Respiratory, Gastrointestinal & Informatics, Medtronic Inc, Boulder, Colorado, USA
| | - Paulo Mendonca
- Digestive Disease Center, Showa University, Northern Yokohama Hospital, Yokohama, Japan
| | | | | | - Alessandro Repici
- Digestive Endoscopy Unit, Humanitas, Research Hospital, Milan, Italy
| | - Douglas Rex
- Departments of Medicine, Endoscopy, and Gastroenterology, Indiana University of School of Medicine, Indianapolis, Indiana, USA
| | - Kris Skrinak
- Global Machine Learning Segment Lead, Amazon Web Services, New York, New York, USA
| | - Shyam J Thakkar
- Department of Endoscopy, Allegheny Health Network, Department of Medicine, Temple University, Philadelphia, Pennsylvania, USA; Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA
| | | | - John Vargo
- Department of Medicine, Gastroenterology, Hepatology & Nutrition, Cleveland Clinic, Cleveland, Ohio, USA
| | - Honggang Yu
- Division of Gastroenterology, Renmin Hospital, Wuhan University, Wuhan, China
| | - Ziyue Xu
- Medical Image Analysis, NVIDIA, Bethesda, Maryland, USA
| | - Prateek Sharma
- Division of Gastroenterology and Hepatology, University of Kansas School of Medicine, Kansas City, Kansas, USA
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Yang YJ. The Future of Capsule Endoscopy: The Role of Artificial Intelligence and Other Technical Advancements. Clin Endosc 2020; 53:387-394. [PMID: 32668529 PMCID: PMC7403015 DOI: 10.5946/ce.2020.133] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/15/2020] [Accepted: 05/24/2020] [Indexed: 12/13/2022] Open
Abstract
Capsule endoscopy has revolutionized the management of small-bowel diseases owing to its convenience and noninvasiveness. Capsule endoscopy is a common method for the evaluation of obscure gastrointestinal bleeding, Crohn’s disease, small-bowel tumors, and polyposis syndrome. However, the laborious reading process, oversight of small-bowel lesions, and lack of locomotion are major obstacles to expanding its application. Along with recent advances in artificial intelligence, several studies have reported the promising performance of convolutional neural network systems for the diagnosis of various small-bowel lesions including erosion/ulcers, angioectasias, polyps, and bleeding lesions, which have reduced the time needed for capsule endoscopy interpretation. Furthermore, colon capsule endoscopy and capsule endoscopy locomotion driven by magnetic force have been investigated for clinical application, and various capsule endoscopy prototypes for active locomotion, biopsy, or therapeutic approaches have been introduced. In this review, we will discuss the recent advancements in artificial intelligence in the field of capsule endoscopy, as well as studies on other technological improvements in capsule endoscopy.
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Affiliation(s)
- Young Joo Yang
- Department of Internal Medicine, Hallym University College of Medicine, Chuncheon, Korea.,Institute for Liver and Digestive Diseases, Hallym University, Chuncheon, Korea
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Yang C, Zhang H. Singing voice separation with pre-learned dictionary and reconstructed voice spectrogram. Neural Comput Appl 2020. [DOI: 10.1007/s00521-018-3757-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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Hassan AR, Subasi A, Zhang Y. Epilepsy seizure detection using complete ensemble empirical mode decomposition with adaptive noise. Knowl Based Syst 2020. [DOI: 10.1016/j.knosys.2019.105333] [Citation(s) in RCA: 42] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
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Mujtaba S, Chawla S, Massaad JF. Diagnosis and Management of Non-Variceal Gastrointestinal Hemorrhage: A Review of Current Guidelines and Future Perspectives. J Clin Med 2020; 9:402. [PMID: 32024301 PMCID: PMC7074258 DOI: 10.3390/jcm9020402] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2019] [Revised: 01/21/2020] [Accepted: 01/24/2020] [Indexed: 01/30/2023] Open
Abstract
Non-variceal gastrointestinal bleeding (GIB) is a significant cause of mortality and morbidity worldwide which is encountered in the ambulatory and hospital settings. Hemorrhage form the gastrointestinal (GI) tract is categorized as upper GIB, small bowel bleeding (also formerly referred to as obscure GIB) or lower GIB. Although the etiologies of GIB are variable, a strong, consistent risk factor is use of non-steroidal anti-inflammatory drugs. Advances in the endoscopic diagnosis and treatment of GIB have led to improved outcomes. We present an updated review of the current practices regarding the diagnosis and management of non-variceal GIB, and possible future directions.
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Affiliation(s)
| | | | - Julia Fayez Massaad
- Division of Digestive Diseases, Emory University, 1365 Clifton Road, Northeast, Building B, Suite 1200, Atlanta, GA 30322, USA; (S.M.); (S.C.)
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Liaqat A, Khan MA, Sharif M, Mittal M, Saba T, Manic KS, Al Attar FNH. Gastric Tract Infections Detection and Classification from Wireless Capsule Endoscopy using Computer Vision Techniques: A Review. Curr Med Imaging 2020; 16:1229-1242. [PMID: 32334504 DOI: 10.2174/1573405616666200425220513] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2019] [Revised: 01/14/2020] [Accepted: 01/30/2020] [Indexed: 11/22/2022]
Abstract
Recent facts and figures published in various studies in the US show that approximately 27,510 new cases of gastric infections are diagnosed. Furthermore, it has also been reported that the mortality rate is quite high in diagnosed cases. The early detection of these infections can save precious human lives. As the manual process of these infections is time-consuming and expensive, therefore automated Computer-Aided Diagnosis (CAD) systems are required which helps the endoscopy specialists in their clinics. Generally, an automated method of gastric infection detections using Wireless Capsule Endoscopy (WCE) is comprised of the following steps such as contrast preprocessing, feature extraction, segmentation of infected regions, and classification into their relevant categories. These steps consist of various challenges that reduce the detection and recognition accuracy as well as increase the computation time. In this review, authors have focused on the importance of WCE in medical imaging, the role of endoscopy for bleeding-related infections, and the scope of endoscopy. Further, the general steps and highlighting the importance of each step have been presented. A detailed discussion and future directions have been provided at the end.
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Affiliation(s)
- Amna Liaqat
- Department of Computer Science, COMSATS University Islamabad, Wah Cantt, Pakistan
| | | | - Muhammad Sharif
- Department of Computer Science, COMSATS University Islamabad, Wah Cantt, Pakistan
| | - Mamta Mittal
- Department of Computer Science & Engineering, G.B. Pant Govt. Engineering College, New Delhi, India
| | - Tanzila Saba
- Department of Computer and Information Sciences, Prince Sultan University, Riyadh, Saudi Arabia
| | - K Suresh Manic
- Department of Electrical & Computer Engineering, National University of Science & Technology, Muscat, Oman
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Le Berre C, Sandborn WJ, Aridhi S, Devignes MD, Fournier L, Smaïl-Tabbone M, Danese S, Peyrin-Biroulet L. Application of Artificial Intelligence to Gastroenterology and Hepatology. Gastroenterology 2020; 158:76-94.e2. [PMID: 31593701 DOI: 10.1053/j.gastro.2019.08.058] [Citation(s) in RCA: 321] [Impact Index Per Article: 64.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/20/2018] [Revised: 08/22/2019] [Accepted: 08/24/2019] [Indexed: 02/07/2023]
Abstract
Since 2010, substantial progress has been made in artificial intelligence (AI) and its application to medicine. AI is explored in gastroenterology for endoscopic analysis of lesions, in detection of cancer, and to facilitate the analysis of inflammatory lesions or gastrointestinal bleeding during wireless capsule endoscopy. AI is also tested to assess liver fibrosis and to differentiate patients with pancreatic cancer from those with pancreatitis. AI might also be used to establish prognoses of patients or predict their response to treatments, based on multiple factors. We review the ways in which AI may help physicians make a diagnosis or establish a prognosis and discuss its limitations, knowing that further randomized controlled studies will be required before the approval of AI techniques by the health authorities.
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Affiliation(s)
- Catherine Le Berre
- Institut des Maladies de l'Appareil Digestif, Nantes University Hospital, France; Institut National de la Santé et de la Recherche Médicale U954 and Department of Gastroenterology, Nancy University Hospital, University of Lorraine, France
| | | | - Sabeur Aridhi
- University of Lorraine, Le Centre National de la Recherche Scientifique, Inria, Laboratoire Lorrain de Recherche en Informatique et ses Applications, Nancy, France
| | - Marie-Dominique Devignes
- University of Lorraine, Le Centre National de la Recherche Scientifique, Inria, Laboratoire Lorrain de Recherche en Informatique et ses Applications, Nancy, France
| | - Laure Fournier
- Université Paris-Descartes, Institut National de la Santé et de la Recherche Médicale, Unité Mixte De Recherché S970, Assistance Publique-Hôpitaux de Paris, Paris, France
| | - Malika Smaïl-Tabbone
- University of Lorraine, Le Centre National de la Recherche Scientifique, Inria, Laboratoire Lorrain de Recherche en Informatique et ses Applications, Nancy, France
| | - Silvio Danese
- Inflammatory Bowel Disease Center and Department of Biomedical Sciences, Humanitas Clinical and Research Center, Humanitas University, Milan, Italy
| | - Laurent Peyrin-Biroulet
- Institut National de la Santé et de la Recherche Médicale U954 and Department of Gastroenterology, Nancy University Hospital, University of Lorraine, France.
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Sukhia K, Riaz M, Ghafoor A, Ali S, Iltaf N. Content-based histopathological image retrieval using multi-scale and multichannel decoder based LTP. Biomed Signal Process Control 2019. [DOI: 10.1016/j.bspc.2019.101582] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
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40
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Ali H, Sharif M, Yasmin M, Rehmani MH, Riaz F. A survey of feature extraction and fusion of deep learning for detection of abnormalities in video endoscopy of gastrointestinal-tract. Artif Intell Rev 2019. [DOI: 10.1007/s10462-019-09743-2] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
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Cummins G, Cox BF, Ciuti G, Anbarasan T, Desmulliez MPY, Cochran S, Steele R, Plevris JN, Koulaouzidis A. Gastrointestinal diagnosis using non-white light imaging capsule endoscopy. Nat Rev Gastroenterol Hepatol 2019; 16:429-447. [PMID: 30988520 DOI: 10.1038/s41575-019-0140-z] [Citation(s) in RCA: 30] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
Capsule endoscopy (CE) has proved to be a powerful tool in the diagnosis and management of small bowel disorders since its introduction in 2001. However, white light imaging (WLI) is the principal technology used in clinical CE at present, and therefore, CE is limited to mucosal inspection, with diagnosis remaining reliant on visible manifestations of disease. The introduction of WLI CE has motivated a wide range of research to improve its diagnostic capabilities through integration with other sensing modalities. These developments have the potential to overcome the limitations of WLI through enhanced detection of subtle mucosal microlesions and submucosal and/or transmural pathology, providing novel diagnostic avenues. Other research aims to utilize a range of sensors to measure physiological parameters or to discover new biomarkers to improve the sensitivity, specificity and thus the clinical utility of CE. This multidisciplinary Review summarizes research into non-WLI CE devices by organizing them into a taxonomic structure on the basis of their sensing modality. The potential of these capsules to realize clinically useful virtual biopsy and computer-aided diagnosis (CADx) is also reported.
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Affiliation(s)
- Gerard Cummins
- School of Engineering and Physical Sciences, Heriot-Watt University, Edinburgh, UK.
| | | | - Gastone Ciuti
- The BioRobotics Institute, Scuola Superiore Sant'Anna, Pisa, Italy
| | | | - Marc P Y Desmulliez
- School of Engineering and Physical Sciences, Heriot-Watt University, Edinburgh, UK
| | - Sandy Cochran
- School of Engineering, University of Glasgow, Glasgow, UK
| | - Robert Steele
- School of Medicine, University of Dundee, Dundee, UK
| | - John N Plevris
- Centre for Liver and Digestive Disorders, The Royal Infirmary of Edinburgh, Edinburgh, UK
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Alagappan M, Brown JRG, Mori Y, Berzin TM. Artificial intelligence in gastrointestinal endoscopy: The future is almost here. World J Gastrointest Endosc 2018; 10:239-249. [PMID: 30364792 PMCID: PMC6198310 DOI: 10.4253/wjge.v10.i10.239] [Citation(s) in RCA: 101] [Impact Index Per Article: 14.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/09/2018] [Revised: 06/09/2018] [Accepted: 06/30/2018] [Indexed: 02/06/2023] Open
Abstract
Artificial intelligence (AI) enables machines to provide unparalleled value in a myriad of industries and applications. In recent years, researchers have harnessed artificial intelligence to analyze large-volume, unstructured medical data and perform clinical tasks, such as the identification of diabetic retinopathy or the diagnosis of cutaneous malignancies. Applications of artificial intelligence techniques, specifically machine learning and more recently deep learning, are beginning to emerge in gastrointestinal endoscopy. The most promising of these efforts have been in computer-aided detection and computer-aided diagnosis of colorectal polyps, with recent systems demonstrating high sensitivity and accuracy even when compared to expert human endoscopists. AI has also been utilized to identify gastrointestinal bleeding, to detect areas of inflammation, and even to diagnose certain gastrointestinal infections. Future work in the field should concentrate on creating seamless integration of AI systems with current endoscopy platforms and electronic medical records, developing training modules to teach clinicians how to use AI tools, and determining the best means for regulation and approval of new AI technology.
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Affiliation(s)
- Muthuraman Alagappan
- Center for Advanced Endoscopy, Beth Israel Deaconess Medical Center, Harvard Medical, Boston, MA 02215, United States
| | - Jeremy R Glissen Brown
- Center for Advanced Endoscopy, Beth Israel Deaconess Medical Center, Harvard Medical, Boston, MA 02215, United States
| | - Yuichi Mori
- Digestive Disease Center, Showa University Northern Yokohama Hospital, Yokohama, Japan
| | - Tyler M Berzin
- Center for Advanced Endoscopy, Beth Israel Deaconess Medical Center, Harvard Medical, Boston, MA 02215, United States
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Alizadeh Savareh B, Bashiri A, Behmanesh A, Meftahi GH, Hatef B. Performance comparison of machine learning techniques in sleep scoring based on wavelet features and neighboring component analysis. PeerJ 2018; 6:e5247. [PMID: 30065866 PMCID: PMC6064207 DOI: 10.7717/peerj.5247] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2017] [Accepted: 06/26/2018] [Indexed: 01/17/2023] Open
Abstract
Introduction Sleep scoring is an important step in the treatment of sleep disorders. Manual annotation of sleep stages is time-consuming and experience-relevant and, therefore, needs to be done using machine learning techniques. Methods Sleep-EDF polysomnography was used in this study as a dataset. Support vector machines and artificial neural network performance were compared in sleep scoring using wavelet tree features and neighborhood component analysis. Results Neighboring component analysis as a combination of linear and non-linear feature selection method had a substantial role in feature dimension reduction. Artificial neural network and support vector machine achieved 90.30% and 89.93% accuracy, respectively. Discussion and Conclusion Similar to the state of the art performance, the introduced method in the present study achieved an acceptable performance in sleep scoring. Furthermore, its performance can be enhanced using a technique combined with other techniques in feature generation and dimension reduction. It is hoped that, in the future, intelligent techniques can be used in the process of diagnosing and treating sleep disorders.
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Affiliation(s)
- Behrouz Alizadeh Savareh
- Student Research Committee, School of Allied Medical Sciences, Shahid Beheshti University of Medical Scinces, Tehran, Iran
| | - Azadeh Bashiri
- Health Information Management Department, School of Allied Medical Sciences, Tehran University of Medical Sciences, Tehran, Iran
| | - Ali Behmanesh
- Student Research Committee, School of Health Management and Information Sciences Branch, Iran University of Medical Sciences, Tehran, Iran
| | | | - Boshra Hatef
- Neuroscience Research Center, Baqiyatallah University of Medical Sciences, Tehran, Iran
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LIAQAT AMNA, KHAN MUHAMMADATTIQUE, SHAH JAMALHUSSAIN, SHARIF MUHAMMAD, YASMIN MUSSARAT, FERNANDES STEVENLAWRENCE. AUTOMATED ULCER AND BLEEDING CLASSIFICATION FROM WCE IMAGES USING MULTIPLE FEATURES FUSION AND SELECTION. J MECH MED BIOL 2018; 18:1850038. [DOI: 10.1142/s0219519418500380] [Citation(s) in RCA: 74] [Impact Index Per Article: 10.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/25/2024]
Abstract
In the area of medical imaging and computer vision, automatic diagnosis of ulcer and bleeding from wireless capsule endoscopy images has been an active research domain. It contains several challenges including low contrast, complex background, lesion shape and color which affect its segmentation and classification accuracy. In this article, a novel method for automated detection and classification of stomach infection is implemented. The proposed method consists of four major steps including preprocessing, lesion segmentation, image representation and classification. The lesion contrast is improved in preprocessing step by employing 3D-box filtering, 3D-median filtering and HSV transformation. In the second step, geometric features are extracted and applied to the saturated channel to give a binary image. The binary image is further improved by fusion of generated mask. After that, extraction of three types of features including color, shape and surf is performed from HSV and binary segmented images and their information is fused by a serial based method. A principal component analysis (PCA) and correlation coefficient based feature selection approach is proposed which are classified by multi class support vector machine (M-SVM). The proposed method is evaluated on personally collected images of three different classes including ulcer, bleeding and healthy. The M-SVM performs well with a maximum accuracy of 98.3% which shows the authenticity of presented method.
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Affiliation(s)
- AMNA LIAQAT
- Department of Computer Science, COMSATS University Islamabad, Wah Campus, Pakistan
| | - MUHAMMAD ATTIQUE KHAN
- Department of Computer Science, COMSATS University Islamabad, Wah Campus, Pakistan
- Department of Computer Science and Engineering, HITEC University, Museum Road, Taxila, Pakistan
| | - JAMAL HUSSAIN SHAH
- Department of Computer Science, COMSATS University Islamabad, Wah Campus, Pakistan
| | - MUHAMMAD SHARIF
- Department of Computer Science, COMSATS University Islamabad, Wah Campus, Pakistan
| | - MUSSARAT YASMIN
- Department of Computer Science, COMSATS University Islamabad, Wah Campus, Pakistan
| | - STEVEN LAWRENCE FERNANDES
- Department of Electronics and Communication Engineering, Sahyadri College of Engineering and Management, Mangalore, Karnataka, India
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Ali H, Yasmin M, Sharif M, Rehmani MH. Computer assisted gastric abnormalities detection using hybrid texture descriptors for chromoendoscopy images. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2018; 157:39-47. [PMID: 29477434 DOI: 10.1016/j.cmpb.2018.01.013] [Citation(s) in RCA: 31] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/17/2017] [Revised: 11/25/2017] [Accepted: 01/10/2018] [Indexed: 05/11/2023]
Abstract
BACKGROUND AND OBJECTIVE The early diagnosis of stomach cancer can be performed by using a proper screening procedure. Chromoendoscopy (CH) is an image-enhanced video endoscopy technique, which is used for inspection of the gastrointestinal-tract by spraying dyes to highlight the gastric mucosal structures. An endoscopy session can end up with generating a large number of video frames. Therefore, inspection of every individual endoscopic-frame is an exhaustive task for the medical experts. In contrast with manual inspection, the automated analysis of gastroenterology images using computer vision based techniques can provide assistance to endoscopist, by finding out abnormal frames from the whole endoscopic sequence. METHODS In this paper, we have presented a new feature extraction method named as Gabor-based gray-level co-occurrence matrix (G2LCM) for computer-aided detection of CH abnormal frames. It is a hybrid texture extraction approach which extracts a combination both local and global texture descriptors. Moreover, texture information of a CH image is represented by computing the gray level co-occurrence matrix of Gabor filters responses. Furthermore, the second-order statistics of these co-occurrence matrices are computed to represent images' texture. RESULTS The obtained results show the possibility to correctly classifying abnormal from normal frames, with sensitivity, specificity, accuracy, and area under the curve as 91%, 82%, 87% and 0.91 respectively, by using a support vector machine classifier and G2LCM texture features. CONCLUSION It is apparent from results that the proposed system can be used for providing aid to the gastroenterologist in the screening of the gastric tract. Ultimately, the time taken by an endoscopic procedure will be sufficiently reduced.
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Affiliation(s)
- Hussam Ali
- COMSATS Institute of Information Technology Wah, Pakistan.
| | | | | | - Mubashir Husain Rehmani
- Telecommunications Software and Systems Group (TSSG) Waterford Institute of Technology (WIT), Ireland
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Flemming J, Cameron S. Small bowel capsule endoscopy: Indications, results, and clinical benefit in a University environment. Medicine (Baltimore) 2018; 97:e0148. [PMID: 29620627 PMCID: PMC5902276 DOI: 10.1097/md.0000000000010148] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/19/2022] Open
Abstract
Capsule endoscopy (CE) opened a new method for visualization of the small intestine. We here further explore its clinical implications.We retrospectively analyzed the clinical benefit of CE in view of medical history, diagnostics, and therapy. Our patient collective consisted of 203 patients. CE was investigated in the context of bleeding, anemia, abdominal pain, diarrhea, Crohn's disease, and suspected tumors.The study collective consisted of 118 male and 85 female patients with a mean age of 58 years (range 8-90 years). Complete bowel transit took place in 82% of the patients. The diagnostic yield in the detection of obscure gastrointestinal bleeding was 80% and for anemia 78%. Mucosal lesions were the most common finding (43%). Unclear abdominal pain had the lowest diagnostic yield (41%). Ensuing therapeutic interventions were mostly medical (66%), and to a minor extent surgical (4.4%) as well as endoscopic (4%).In conclusion, small intestinal CE is a secure method to clarify small intestinal diseases, especially obscure gastrointestinal bleeding, even in pre-operated patients without stenosis symptoms. Our study emphasizes in a collective of patients with extensive prior diagnostics that due to CE therapeutic measures resulted in 73%.
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Affiliation(s)
- Juliane Flemming
- Clinic for Gastroenterology and Gastrointestinal Oncology, University Medicine Göttingen, Göttingen
- St. Joseph Hospital, Berlin Tempelhof, Berlin, Germany
| | - Silke Cameron
- Clinic for Gastroenterology and Gastrointestinal Oncology, University Medicine Göttingen, Göttingen
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Ghosh T, Fattah SA, Wahid KA. CHOBS: Color Histogram of Block Statistics for Automatic Bleeding Detection in Wireless Capsule Endoscopy Video. IEEE JOURNAL OF TRANSLATIONAL ENGINEERING IN HEALTH AND MEDICINE-JTEHM 2018; 6:1800112. [PMID: 29468094 PMCID: PMC5815328 DOI: 10.1109/jtehm.2017.2756034] [Citation(s) in RCA: 30] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/24/2017] [Revised: 05/05/2017] [Accepted: 06/18/2017] [Indexed: 12/22/2022]
Abstract
Wireless capsule endoscopy (WCE) is the most advanced technology to visualize whole gastrointestinal (GI) tract in a non-invasive way. But the major disadvantage here, it takes long reviewing time, which is very laborious as continuous manual intervention is necessary. In order to reduce the burden of the clinician, in this paper, an automatic bleeding detection method for WCE video is proposed based on the color histogram of block statistics, namely CHOBS. A single pixel in WCE image may be distorted due to the capsule motion in the GI tract. Instead of considering individual pixel values, a block surrounding to that individual pixel is chosen for extracting local statistical features. By combining local block features of three different color planes of RGB color space, an index value is defined. A color histogram, which is extracted from those index values, provides distinguishable color texture feature. A feature reduction technique utilizing color histogram pattern and principal component analysis is proposed, which can drastically reduce the feature dimension. For bleeding zone detection, blocks are classified using extracted local features that do not incorporate any computational burden for feature extraction. From extensive experimentation on several WCE videos and 2300 images, which are collected from a publicly available database, a very satisfactory bleeding frame and zone detection performance is achieved in comparison to that obtained by some of the existing methods. In the case of bleeding frame detection, the accuracy, sensitivity, and specificity obtained from proposed method are 97.85%, 99.47%, and 99.15%, respectively, and in the case of bleeding zone detection, 95.75% of precision is achieved. The proposed method offers not only low feature dimension but also highly satisfactory bleeding detection performance, which even can effectively detect bleeding frame and zone in a continuous WCE video data.
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Affiliation(s)
- Tonmoy Ghosh
- Department of Electrical Electronic EngineeringPabna University of Science and TechnologyPabna6600Bangladesh.,Department of Electrical Electronic EngineeringBangladesh University of Engineering and TechnologyDhaka1000Bangladesh.,Department of ECEUniversity of SaskatchewanSaskatoonSK S7N 5A9Canada
| | - Shaikh Anowarul Fattah
- Department of Electrical Electronic EngineeringPabna University of Science and TechnologyPabna6600Bangladesh.,Department of Electrical Electronic EngineeringBangladesh University of Engineering and TechnologyDhaka1000Bangladesh.,Department of ECEUniversity of SaskatchewanSaskatoonSK S7N 5A9Canada
| | - Khan A Wahid
- Department of Electrical Electronic EngineeringPabna University of Science and TechnologyPabna6600Bangladesh.,Department of Electrical Electronic EngineeringBangladesh University of Engineering and TechnologyDhaka1000Bangladesh.,Department of ECEUniversity of SaskatchewanSaskatoonSK S7N 5A9Canada
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Ali H, Sharif M, Yasmin M, Rehmani MH. Computer-based classification of chromoendoscopy images using homogeneous texture descriptors. Comput Biol Med 2017; 88:84-92. [DOI: 10.1016/j.compbiomed.2017.07.002] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2017] [Revised: 06/29/2017] [Accepted: 07/02/2017] [Indexed: 01/22/2023]
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An Automated Self-Learning Quantification System to Identify Visible Areas in Capsule Endoscopy Images. J Med Syst 2017; 41:119. [DOI: 10.1007/s10916-017-0769-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2016] [Accepted: 06/29/2017] [Indexed: 10/19/2022]
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