Evidence map›Paper›PMID 32395530›Full record

ArticleAnnals of translational medicine2020

Automatic classification of esophageal lesions in endoscopic images using a convolutional neural network.

Gaoshuang Liu, Jie Hua, Zhan Wu, Tianfang Meng, Mengxue Sun, Peiyun Huang, Xiaopu He, Weihao Sun, Xueliang Li, Yang Chen

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Article in Annals of translational medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
22citing papers in PubMed, 3 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

22 citing papers in PubMed, 3 syntheses or guidelines pooled it.

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  15. Artificial Intelligence in Endoscopy.Digestive diseases and sciences · 2022
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Gaoshuang LiuDepartment of Geriatric Gerontology, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, China.
Jie HuaDepartment of Gastroenterology, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, China.
Zhan WuLaboratory of Image Science and Technology, School of Computer Science and Engineering, Southeast University, Nanjing 211102, China.
Tianfang MengLaboratory of Image Science and Technology, School of Computer Science and Engineering, Southeast University, Nanjing 211102, China.
Mengxue SunDepartment of Geriatric Gerontology, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, China.
Peiyun HuangDepartment of Geriatric Gerontology, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, China.
Xiaopu HeDepartment of Geriatric Gerontology, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, China.
Weihao SunDepartment of Geriatric Gerontology, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, China.
Xueliang LiDepartment of Gastroenterology, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, China.
Yang ChenLaboratory of Image Science and Technology, School of Computer Science and Engineering, Southeast University, Nanjing 211102, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundUsing deep learning techniques in image analysis is a dynamically emerging field. This study aims to use a convolutional neural network (CNN), a deep learning approach, to automatically classify esophageal cancer (EC) and distinguish it from premalignant lesions.

methodsA total of 1,272 white-light images were adopted from 748 subjects, including normal cases, premalignant lesions, and cancerous lesions; 1,017 images were used to train the CNN, and another 255 images were examined to evaluate the CNN architecture. Our proposed CNN structure consists of two subnetworks (O-stream and P-stream). The original images were used as the inputs of the O-stream to extract the color and global features, and the pre-processed esophageal images were used as the inputs of the P-stream to extract the texture and detail features.

resultsThe CNN system we developed achieved an accuracy of 85.83%, a sensitivity of 94.23%, and a specificity of 94.67% after the fusion of the 2 streams was accomplished. The classification accuracy of normal esophagus, premalignant lesion, and EC were 94.23%, 82.5%, and 77.14%, respectively, which shows a better performance than the Local Binary Patterns (LBP) + Support Vector Machine (SVM) and Histogram of Gradient (HOG) + SVM methods. A total of 8 of the 35 (22.85%) EC lesions were categorized as premalignant lesions because of their slightly reddish and flat lesions.

conclusionsThe CNN system, with 2 streams, demonstrated high sensitivity and specificity with the endoscopic images. It obtained better detection performance than the currently used methods based on the same datasets and has great application prospects in assisting endoscopists to distinguish esophageal lesion subclasses.

Indexed as

convolutional neural network (CNN)deep learningendoscopic diagnosisEsophageal cancer (EC)

Identifiers

PMID32395530
PMCPMC7210177

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.