Evidence map›Paper›PMID 37208546›Full record

ArticleDiscover oncology2023

Artificial intelligence assisted detection of superficial esophageal squamous cell carcinoma in white-light endoscopic images by using a generalized system.

Yadong Feng, Yan Liang, Peng Li, Qigang Long, Jie Song, Mengjie Li, Xiaofen Wang, Cui-E Cheng, Kai Zhao, Jifeng Ma and 1 more

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Discover oncology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
2.9field-weighted citation impact, top 9% of its field
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

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

10 citing papers in PubMed, 9 citations in OpenAlex.

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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

11 authors at 7 institutions in 1 country.

Yadong Feng *Department of Gastroenterology, Zhongda Hospital Southeast University, 87 Dingjiaqiao Street, Nanjing, 210009, China. drfengyd@126.com.
Yan Liang *Department of Gastroenterology, Zhongda Hospital Southeast University, 87 Dingjiaqiao Street, Nanjing, 210009, China.
Peng Li *Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, 88 Keling Road, Suzhou, 215163, China.
Qigang LongSuzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, 88 Keling Road, Suzhou, 215163, China.
Jie SongDepartment of Gastroenterology, Zhongda Hospital Southeast University, 87 Dingjiaqiao Street, Nanjing, 210009, China.
Mengjie LiDepartment of Gastroenterology, Zhongda Hospital Southeast University, 87 Dingjiaqiao Street, Nanjing, 210009, China.
Xiaofen WangDepartment of Gastroenterology, Zhongda Hospital Southeast University, 87 Dingjiaqiao Street, Nanjing, 210009, China.
Cui-E ChengDepartment of Gastroenterology, the Affiliated Changshu Hospital of Nantong University, Changshu No. 2 People's Hospital, 18 Taishan Road, Suzhou, 215500, China.
Kai ZhaoDepartment of Gastroenterology, Changzhou Jintan First People's Hospital Affiliated to Jiangsu University, 500 Jintan Avenue, Jintan, 210036, China.
Jifeng MaDepartment of Gastroenterology, General Global Maanshan 17th Metallurgy Hospital, 828 West Hunan Road, Maanshan, 243011, China.
Lingxiao ZhaoSuzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, 88 Keling Road, Suzhou, 215163, China. zhaolx@sibet.ac.cn.
Zhongda Hospital Southeast University · CNChinese Academy of Sciences · CNChangshu No.1 People's Hospital · CNChangshu No. 2 People's HospitalJiangsu University · CNMasteel (China) · CNUniversity of Science and Technology of China · CN

Funding

Changzhou Municipal Social Development Program CE20195001Health Program of Chinese Society for Metals, Safety and Health Branch jkws202019Jiangsu Provincial Special Program of Medical Science BE2019710Scientific and Technologic Development Program of Suzhou SYS2019007Technologic Development Program of Maanshan YL-2021-18
6 · The paper itself

Abstract

backgroundThe use of artificial intelligence (AI) assisted white light imaging (WLI) detection systems for superficial esophageal squamous cell carcinoma (SESCC) is limited by training with images from one specific endoscopy platform.

methodsIn this study, we developed an AI system with a convolutional neural network (CNN) model using WLI images from Olympus and Fujifilm endoscopy platforms. The training dataset consisted of 5892 WLI images from 1283 patients, and the validation dataset included 4529 images from 1224 patients. We assessed the diagnostic performance of the AI system and compared it with that of endoscopists. We analyzed the system's ability to identify cancerous imaging characteristics and investigated the efficacy of the AI system as an assistant in diagnosis.

resultsIn the internal validation set, the AI system's per-image analysis had a sensitivity, specificity, accuracy, positive predictive value (PPV), and negative predictive value (NPV) of 96.64%, 95.35%, 91.75%, 90.91%, and 98.33%, respectively. In patient-based analysis, these values were 90.17%, 94.34%, 88.38%, 89.50%, and 94.72%, respectively. The diagnostic results in the external validation set were also favorable. The CNN model's diagnostic performance in recognizing cancerous imaging characteristics was comparable to that of expert endoscopists and significantly higher than that of mid-level and junior endoscopists. This model was competent in localizing SESCC lesions. Manual diagnostic performances were significantly improved with the assistance by AI system, especially in terms of accuracy (75.12% vs. 84.95%, p = 0.008), specificity (63.29% vs. 76.59%, p = 0.017) and PPV (64.95% vs. 75.23%, p = 0.006).

conclusionsThe results of this study demonstrate that the developed AI system is highly effective in automatically recognizing SESCC, displaying impressive diagnostic performance, and exhibiting strong generalizability. Furthermore, when used as an assistant in the diagnosis process, the system improved manual diagnostic performance.

Identifiers

PMID37208546
PMCPMC10199153
OpenAlexW4377115558

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

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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.