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Galati JS, Duve RJ, O'Mara M, Gross SA. Artificial intelligence in gastroenterology: A narrative review. Artif Intell Gastroenterol 2022; 3:117-141. [DOI: 10.35712/aig.v3.i5.117] [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: 10/09/2022] [Revised: 11/21/2022] [Accepted: 12/21/2022] [Indexed: 12/28/2022] Open
Abstract
Artificial intelligence (AI) is a complex concept, broadly defined in medicine as the development of computer systems to perform tasks that require human intelligence. It has the capacity to revolutionize medicine by increasing efficiency, expediting data and image analysis and identifying patterns, trends and associations in large datasets. Within gastroenterology, recent research efforts have focused on using AI in esophagogastroduodenoscopy, wireless capsule endoscopy (WCE) and colonoscopy to assist in diagnosis, disease monitoring, lesion detection and therapeutic intervention. The main objective of this narrative review is to provide a comprehensive overview of the research being performed within gastroenterology on AI in esophagogastroduodenoscopy, WCE and colonoscopy.
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Affiliation(s)
- Jonathan S Galati
- Department of Medicine, NYU Langone Health, New York, NY 10016, United States
| | - Robert J Duve
- Department of Internal Medicine, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, Buffalo, NY 14203, United States
| | - Matthew O'Mara
- Division of Gastroenterology, NYU Langone Health, New York, NY 10016, United States
| | - Seth A Gross
- Division of Gastroenterology, NYU Langone Health, New York, NY 10016, United States
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Tharwat M, Sakr NA, El-Sappagh S, Soliman H, Kwak KS, Elmogy M. Colon Cancer Diagnosis Based on Machine Learning and Deep Learning: Modalities and Analysis Techniques. SENSORS (BASEL, SWITZERLAND) 2022; 22:9250. [PMID: 36501951 PMCID: PMC9739266 DOI: 10.3390/s22239250] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/24/2022] [Accepted: 11/24/2022] [Indexed: 06/17/2023]
Abstract
The treatment and diagnosis of colon cancer are considered to be social and economic challenges due to the high mortality rates. Every year, around the world, almost half a million people contract cancer, including colon cancer. Determining the grade of colon cancer mainly depends on analyzing the gland's structure by tissue region, which has led to the existence of various tests for screening that can be utilized to investigate polyp images and colorectal cancer. This article presents a comprehensive survey on the diagnosis of colon cancer. This covers many aspects related to colon cancer, such as its symptoms and grades as well as the available imaging modalities (particularly, histopathology images used for analysis) in addition to common diagnosis systems. Furthermore, the most widely used datasets and performance evaluation metrics are discussed. We provide a comprehensive review of the current studies on colon cancer, classified into deep-learning (DL) and machine-learning (ML) techniques, and we identify their main strengths and limitations. These techniques provide extensive support for identifying the early stages of cancer that lead to early treatment of the disease and produce a lower mortality rate compared with the rate produced after symptoms develop. In addition, these methods can help to prevent colorectal cancer from progressing through the removal of pre-malignant polyps, which can be achieved using screening tests to make the disease easier to diagnose. Finally, the existing challenges and future research directions that open the way for future work in this field are presented.
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Affiliation(s)
- Mai Tharwat
- Information Technology Department, Faculty of Computers and Information, Mansoura University, Mansoura 35516, Egypt
| | - Nehal A. Sakr
- Information Technology Department, Faculty of Computers and Information, Mansoura University, Mansoura 35516, Egypt
| | - Shaker El-Sappagh
- Information Systems Department, Faculty of Computers and Artificial Intelligence, Benha University, Benha 13512, Egypt
- Faculty of Computer Science and Engineering, Galala University, Suez 435611, Egypt
| | - Hassan Soliman
- Information Technology Department, Faculty of Computers and Information, Mansoura University, Mansoura 35516, Egypt
| | - Kyung-Sup Kwak
- Department of Information and Communication Engineering, Inha University, Incheon 22212, Republic of Korea
| | - Mohammed Elmogy
- Information Technology Department, Faculty of Computers and Information, Mansoura University, Mansoura 35516, Egypt
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Sharma P, Balabantaray BK, Bora K, Mallik S, Kasugai K, Zhao Z. An Ensemble-Based Deep Convolutional Neural Network for Computer-Aided Polyps Identification From Colonoscopy. Front Genet 2022; 13:844391. [PMID: 35559018 PMCID: PMC9086187 DOI: 10.3389/fgene.2022.844391] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2021] [Accepted: 03/14/2022] [Indexed: 01/16/2023] Open
Abstract
Colorectal cancer (CRC) is the third leading cause of cancer death globally. Early detection and removal of precancerous polyps can significantly reduce the chance of CRC patient death. Currently, the polyp detection rate mainly depends on the skill and expertise of gastroenterologists. Over time, unidentified polyps can develop into cancer. Machine learning has recently emerged as a powerful method in assisting clinical diagnosis. Several classification models have been proposed to identify polyps, but their performance has not been comparable to an expert endoscopist yet. Here, we propose a multiple classifier consultation strategy to create an effective and powerful classifier for polyp identification. This strategy benefits from recent findings that different classification models can better learn and extract various information within the image. Therefore, our Ensemble classifier can derive a more consequential decision than each individual classifier. The extracted combined information inherits the ResNet's advantage of residual connection, while it also extracts objects when covered by occlusions through depth-wise separable convolution layer of the Xception model. Here, we applied our strategy to still frames extracted from a colonoscopy video. It outperformed other state-of-the-art techniques with a performance measure greater than 95% in each of the algorithm parameters. Our method will help researchers and gastroenterologists develop clinically applicable, computational-guided tools for colonoscopy screening. It may be extended to other clinical diagnoses that rely on image.
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Affiliation(s)
- Pallabi Sharma
- Department of Computer Science and Engineering, National Institute of Technology Meghalaya, Shillong, India
| | - Bunil Kumar Balabantaray
- Department of Computer Science and Engineering, National Institute of Technology Meghalaya, Shillong, India
| | - Kangkana Bora
- Computer Science and Information Technology, Cotton University, Guwahati, India
| | - Saurav Mallik
- Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, United States
| | - Kunio Kasugai
- Department of Gastroenterology, Aichi Medical University, Nagakute, Japan
| | - Zhongming Zhao
- Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, United States
- Human Genetics Center, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, United States
- MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, Houston, TX, United States
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Predicting Colorectal Cancer Using Residual Deep Learning with Nursing Care. CONTRAST MEDIA & MOLECULAR IMAGING 2022; 2022:7996195. [PMID: 35291423 PMCID: PMC8898865 DOI: 10.1155/2022/7996195] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/31/2021] [Revised: 12/02/2021] [Accepted: 02/03/2022] [Indexed: 02/02/2023]
Abstract
Presently, colorectal cancer is the second most dangerous cancer; around 13% of people have been affected; and it requires an effective image analysis and earlier cancer prediction (IAECP) system for reducing the mortality rate. Here, the IAECP system uses MRI radio imaging for predicting colorectal cancer. During this process, high- and low-level features are required to examine cancer in an earlier stage. Due to the limitation of the conventional feature extraction process, both features are difficult to extract from cancer suffered locations. Hence, a deep learning system (DLS) is used to examine the entire bowel MRI image to identify the cancer-affected location, feature extraction, and feature training process. Furthermore, the DLS-based IAECP system helps improve the overall colorectal cancer identification accuracy for further process. The derived bowel features are trained by applying the residual convolution network, which minimizes the error between predicted and actual values. Finally, the test query images are compared with the trained image by applying the sum, which is more absolute to the cross-correlation template feature matching (SACC) algorithm. The experimental process is performed using 100,000 histological data sets, which is considered a publicly available data set. Moreover, the introduced method does not use generic features, whereas the deep learning features help improve the overall IAECP prediction rate (99.8%) ratio as predicted at lab-scale analysis.
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Recognizing Gastrointestinal Malignancies on WCE and CCE Images by an Ensemble of Deep and Handcrafted Features with Entropy and PCA Based Features Optimization. Neural Process Lett 2021. [DOI: 10.1007/s11063-021-10481-2] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
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Naz J, Sharif M, Yasmin M, Raza M, Khan MA. Detection and Classification of Gastrointestinal Diseases using Machine Learning. Curr Med Imaging 2021; 17:479-490. [PMID: 32988355 DOI: 10.2174/1573405616666200928144626] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2020] [Revised: 07/07/2020] [Accepted: 07/23/2020] [Indexed: 12/22/2022]
Abstract
BACKGROUND Traditional endoscopy is an invasive and painful method of examining the gastrointestinal tract (GIT) not supported by physicians and patients. To handle this issue, video endoscopy (VE) or wireless capsule endoscopy (WCE) is recommended and utilized for GIT examination. Furthermore, manual assessment of captured images is not possible for an expert physician because it's a time taking task to analyze thousands of images thoroughly. Hence, there comes the need for a Computer-Aided-Diagnosis (CAD) method to help doctors analyze images. Many researchers have proposed techniques for automated recognition and classification of abnormality in captured images. METHODS In this article, existing methods for automated classification, segmentation and detection of several GI diseases are discussed. Paper gives a comprehensive detail about these state-of-theart methods. Furthermore, literature is divided into several subsections based on preprocessing techniques, segmentation techniques, handcrafted features based techniques and deep learning based techniques. Finally, issues, challenges and limitations are also undertaken. RESULTS A comparative analysis of different approaches for the detection and classification of GI infections. CONCLUSION This comprehensive review article combines information related to a number of GI diseases diagnosis methods at one place. This article will facilitate the researchers to develop new algorithms and approaches for early detection of GI diseases detection with more promising results as compared to the existing ones of literature.
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Affiliation(s)
- Javeria Naz
- 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
| | - Mudassar Raza
- Department of Computer Science, COMSATS University Islamabad, Wah Campus, Pakistan
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Hwang M, Wang D, Kong XX, Wang Z, Li J, Jiang WC, Hwang KS, Ding K. An automated detection system for colonoscopy images using a dual encoder-decoder model. Comput Med Imaging Graph 2020; 84:101763. [PMID: 32805673 DOI: 10.1016/j.compmedimag.2020.101763] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2019] [Revised: 07/10/2020] [Accepted: 07/10/2020] [Indexed: 02/07/2023]
Abstract
Conventional computer-aided detection systems (CADs) for colonoscopic images utilize shape, texture, or temporal information to detect polyps, so they have limited sensitivity and specificity. This study proposes a method to extract possible polyp features automatically using convolutional neural networks (CNNs). The objective of this work aims at building up a light-weight dual encoder-decoder model structure for polyp detection in colonoscopy Images. This proposed model, though with a relatively shallow structure, is expected to have the capability of a similar performance to the methods with much deeper structures. The proposed CAD model consists of two sequential encoder-decoder networks that consist of several CNN layers and full connection layers. The front end of the model is a hetero-associator (also known as hetero-encoder) that uses backpropagation learning to generate a set of reliably corrupted labeled images with a certain degree of similarity to a ground truth image, which eliminates the need for a large amount of training data that is usually required for medical images tasks. This dual CNN architecture generates a set of noisy images that are similar to the labeled data to train its counterpart, the auto-associator (also known as auto-encoder), in order to increase the successor's discriminative power in classification. The auto-encoder is also equipped with CNNs to simultaneously capture the features of the labeled images that contain noise. The proposed method uses features that are learned from open medical datasets and the dataset of Zhejiang University (ZJU), which contains around one thousand images. The performance of the proposed architecture is compared with a state-of-the-art detection model in terms of the metrics of the Jaccard index, the DICE similarity score, and two other geometric measures. The improvements in the performance of the proposed model are attributed to the effective reduction in false positives in the auto-encoder and the generation of noisy candidate images by the hetero-encoder.
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Affiliation(s)
- Maxwell Hwang
- Department of Colorectal Surgery, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China; Cancer Institute (Key Laboratory of Cancer Prevention and Intervention, China National Ministry of Education, Key Laboratory of Molecular Biology in Medical Sciences, Zhejiang Province, China; The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Da Wang
- Department of Colorectal Surgery, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China; Cancer Institute (Key Laboratory of Cancer Prevention and Intervention, China National Ministry of Education, Key Laboratory of Molecular Biology in Medical Sciences, Zhejiang Province, China; The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Xiang-Xing Kong
- Department of Colorectal Surgery, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China; Cancer Institute (Key Laboratory of Cancer Prevention and Intervention, China National Ministry of Education, Key Laboratory of Molecular Biology in Medical Sciences, Zhejiang Province, China; The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Zhanhuai Wang
- Department of Colorectal Surgery, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China; Cancer Institute (Key Laboratory of Cancer Prevention and Intervention, China National Ministry of Education, Key Laboratory of Molecular Biology in Medical Sciences, Zhejiang Province, China; The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Jun Li
- Department of Colorectal Surgery, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China; Cancer Institute (Key Laboratory of Cancer Prevention and Intervention, China National Ministry of Education, Key Laboratory of Molecular Biology in Medical Sciences, Zhejiang Province, China; The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Wei-Cheng Jiang
- Department of Electrical Engineering, Tunghai University, Taichung, Taiwan, China
| | - Kao-Shing Hwang
- Department of Electrical Engineering, National Sun Yat-sen University, Kaohsiung, Taiwan, China
| | - Kefeng Ding
- Department of Colorectal Surgery, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China; Cancer Institute (Key Laboratory of Cancer Prevention and Intervention, China National Ministry of Education, Key Laboratory of Molecular Biology in Medical Sciences, Zhejiang Province, China; The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
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Yang J, Chang L, Li S, He X, Zhu T. WCE polyp detection based on novel feature descriptor with normalized variance locality-constrained linear coding. Int J Comput Assist Radiol Surg 2020; 15:1291-1302. [PMID: 32447521 DOI: 10.1007/s11548-020-02190-3] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/01/2020] [Accepted: 04/28/2020] [Indexed: 12/14/2022]
Abstract
PURPOSE Wireless capsule endoscopy (WCE) has become an effective facility to detect digestive tract diseases. To further improve the accuracy and efficiency of computer-aided diagnosis system in the detection of intestine polyps, a novel algorithm is proposed for WCE polyp detection in this paper. METHODS First, by considering the rich color information of endoscopic images, a novel local color texture feature called histogram of local color difference (LCDH) is proposed for describing endoscopic images. A codebook acquisition method which is based upon positive samples is also proposed, generating more balanced visual words with the LCDH features. Furthermore, based on locality-constrained linear coding (LLC) algorithm, a normalized variance regular term is introduced as NVLLC algorithm, which considers the dispersion degree between k nearest visual words and features in the approximate coding phase. The final image representations are obtained from using the spatial matching pyramid model. Finally, the support vector machine is employed to classify the polyp images. RESULTS The WCE dataset including 500 polyp and 500 normal images is adopted for evaluating the proposed method. Experimental results indicate that the classification accuracy, sensitivity and specificity have reached 96.00%, 95.80% and 96.20%, which performances better than traditional ways. CONCLUSION A novel method for WCE polyp detection is developed using LCDH feature descriptor and NVLLC coding scheme, which achieves a promising performance and can be implemented in clinical-assisted diagnosis of intestinal diseases.
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Affiliation(s)
- Jianjun Yang
- College of Information Engineering, Zhejiang University of Technology, Hangzhou, 310023, Zhejiang, People's Republic of China
| | - Liping Chang
- College of Information Engineering, Zhejiang University of Technology, Hangzhou, 310023, Zhejiang, People's Republic of China.
| | - Sheng Li
- College of Information Engineering, Zhejiang University of Technology, Hangzhou, 310023, Zhejiang, People's Republic of China
| | - Xiongxiong He
- College of Information Engineering, Zhejiang University of Technology, Hangzhou, 310023, Zhejiang, People's Republic of China
| | - Tingwei Zhu
- College of Information Engineering, Zhejiang University of Technology, Hangzhou, 310023, Zhejiang, People's Republic of China
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