Artificial Intelligence in Gastric Cancer: Identifying Gastric Cancer Using Endoscopic Images with Convolutional Neural Network

Gastric cancer (GC) is one of the most newly diagnosed cancers and the fifth leading cause of death globally. Identification of early gastric cancer (EGC) can ensure quick treatment and reduce significant mortality. Therefore, we aimed to conduct a systematic review with a meta-analysis of current l...

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Autores principales:	Md. Mohaimenul Islam, Tahmina Nasrin Poly, Bruno Andreas Walther, Ming-Chin Lin, Yu-Chuan (Jack) Li
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	convolutional neural network deep learning gastric cancer endoscopy image artificial intelligence Neoplasms. Tumors. Oncology. Including cancer and carcinogens RC254-282
Acceso en línea:	https://doaj.org/article/76c4133e7c2641668dd8d89d053156d7
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spelling	oai:doaj.org-article:76c4133e7c2641668dd8d89d053156d72021-11-11T15:26:19ZArtificial Intelligence in Gastric Cancer: Identifying Gastric Cancer Using Endoscopic Images with Convolutional Neural Network10.3390/cancers132152532072-6694https://doaj.org/article/76c4133e7c2641668dd8d89d053156d72021-10-01T00:00:00Zhttps://www.mdpi.com/2072-6694/13/21/5253https://doaj.org/toc/2072-6694Gastric cancer (GC) is one of the most newly diagnosed cancers and the fifth leading cause of death globally. Identification of early gastric cancer (EGC) can ensure quick treatment and reduce significant mortality. Therefore, we aimed to conduct a systematic review with a meta-analysis of current literature to evaluate the performance of the CNN model in detecting EGC. We conducted a systematic search in the online databases (e.g., PubMed, Embase, and Web of Science) for all relevant original studies on the subject of CNN in EGC published between 1 January 2010, and 26 March 2021. The Quality Assessment of Diagnostic Accuracy Studies-2 was used to assess the risk of bias. Pooled sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, and diagnostic odds ratio were calculated. Moreover, a summary receiver operating characteristic curve (SROC) was plotted. Of the 171 studies retrieved, 15 studies met inclusion criteria. The application of the CNN model in the diagnosis of EGC achieved a SROC of 0.95, with corresponding sensitivity of 0.89 (0.88–0.89), and specificity of 0.89 (0.89–0.90). Pooled sensitivity and specificity for experts endoscopists were 0.77 (0.76–0.78), and 0.92 (0.91–0.93), respectively. However, the overall SROC for the CNN model and expert endoscopists was 0.95 and 0.90. The findings of this comprehensive study show that CNN model exhibited comparable performance to endoscopists in the diagnosis of EGC using digital endoscopy images. Given its scalability, the CNN model could enhance the performance of endoscopists to correctly stratify EGC patients and reduce work load.Md. Mohaimenul IslamTahmina Nasrin PolyBruno Andreas WaltherMing-Chin LinYu-Chuan (Jack) LiMDPI AGarticleconvolutional neural networkdeep learninggastric cancerendoscopy imageartificial intelligenceNeoplasms. Tumors. Oncology. Including cancer and carcinogensRC254-282ENCancers, Vol 13, Iss 5253, p 5253 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	convolutional neural network deep learning gastric cancer endoscopy image artificial intelligence Neoplasms. Tumors. Oncology. Including cancer and carcinogens RC254-282
spellingShingle	convolutional neural network deep learning gastric cancer endoscopy image artificial intelligence Neoplasms. Tumors. Oncology. Including cancer and carcinogens RC254-282 Md. Mohaimenul Islam Tahmina Nasrin Poly Bruno Andreas Walther Ming-Chin Lin Yu-Chuan (Jack) Li Artificial Intelligence in Gastric Cancer: Identifying Gastric Cancer Using Endoscopic Images with Convolutional Neural Network
description	Gastric cancer (GC) is one of the most newly diagnosed cancers and the fifth leading cause of death globally. Identification of early gastric cancer (EGC) can ensure quick treatment and reduce significant mortality. Therefore, we aimed to conduct a systematic review with a meta-analysis of current literature to evaluate the performance of the CNN model in detecting EGC. We conducted a systematic search in the online databases (e.g., PubMed, Embase, and Web of Science) for all relevant original studies on the subject of CNN in EGC published between 1 January 2010, and 26 March 2021. The Quality Assessment of Diagnostic Accuracy Studies-2 was used to assess the risk of bias. Pooled sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, and diagnostic odds ratio were calculated. Moreover, a summary receiver operating characteristic curve (SROC) was plotted. Of the 171 studies retrieved, 15 studies met inclusion criteria. The application of the CNN model in the diagnosis of EGC achieved a SROC of 0.95, with corresponding sensitivity of 0.89 (0.88–0.89), and specificity of 0.89 (0.89–0.90). Pooled sensitivity and specificity for experts endoscopists were 0.77 (0.76–0.78), and 0.92 (0.91–0.93), respectively. However, the overall SROC for the CNN model and expert endoscopists was 0.95 and 0.90. The findings of this comprehensive study show that CNN model exhibited comparable performance to endoscopists in the diagnosis of EGC using digital endoscopy images. Given its scalability, the CNN model could enhance the performance of endoscopists to correctly stratify EGC patients and reduce work load.
format	article
author	Md. Mohaimenul Islam Tahmina Nasrin Poly Bruno Andreas Walther Ming-Chin Lin Yu-Chuan (Jack) Li
author_facet	Md. Mohaimenul Islam Tahmina Nasrin Poly Bruno Andreas Walther Ming-Chin Lin Yu-Chuan (Jack) Li
author_sort	Md. Mohaimenul Islam
title	Artificial Intelligence in Gastric Cancer: Identifying Gastric Cancer Using Endoscopic Images with Convolutional Neural Network
title_short	Artificial Intelligence in Gastric Cancer: Identifying Gastric Cancer Using Endoscopic Images with Convolutional Neural Network
title_full	Artificial Intelligence in Gastric Cancer: Identifying Gastric Cancer Using Endoscopic Images with Convolutional Neural Network
title_fullStr	Artificial Intelligence in Gastric Cancer: Identifying Gastric Cancer Using Endoscopic Images with Convolutional Neural Network
title_full_unstemmed	Artificial Intelligence in Gastric Cancer: Identifying Gastric Cancer Using Endoscopic Images with Convolutional Neural Network
title_sort	artificial intelligence in gastric cancer: identifying gastric cancer using endoscopic images with convolutional neural network
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/76c4133e7c2641668dd8d89d053156d7
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Artificial Intelligence in Gastric Cancer: Identifying Gastric Cancer Using Endoscopic Images with Convolutional Neural Network

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