Evidence map›Paper›PMID 37515597›Full record

ArticleJournal of gastroenterology2023

Exploring the challenge of early gastric cancer diagnostic AI system face in multiple centers and its potential solutions.

Zehua Dong, Xiao Tao, Hongliu Du, Junxiao Wang, Li Huang, Chiyi He, Zhifeng Zhao, Xinli Mao, Yaowei Ai, Beiping Zhang and 16 more

Abstract readMulticenter Study
PubMed Publisher
In one paragraph

Article in Journal of gastroenterology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
–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

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

6 citing papers in PubMed.

  1. Trial
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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

26 authors.

Zehua Dong *Renmin Hospital of Wuhan University, Wuhan, China.
Xiao Tao *Renmin Hospital of Wuhan University, Wuhan, China.
Hongliu DuRenmin Hospital of Wuhan University, Wuhan, China.
Junxiao WangRenmin Hospital of Wuhan University, Wuhan, China.
Li HuangRenmin Hospital of Wuhan University, Wuhan, China.
Chiyi HeDepartment of Gastroenterology, Yijishan Hospital of Wannan Medical College, Wuhu, 241001, Anhui, People's Republic of China.
Zhifeng ZhaoDepartment of Digestive Endoscopy, The Fourth Hospital of China Medical University, Shenyang, 110032, Liaoning Province, People's Republic of China.
Xinli MaoDepartment of Gastroenterology, Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, Linhai, Zhejiang, China.
Yaowei AiDepartment of Gastroenterology, The People's Hospital of China Three Gorges University, The First People's Hospital of Yichang, Yichang, China.
Beiping ZhangDepartment of Gastroenterology, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China.
Mei LiuDepartment of Gastroenterology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Hong XuDepartment of Endoscopy, The First Hospital of Jilin University, Changchun, China.
Zhenyu JiangDepartment of Gastroenterology, The Second Affiliated Hospital of Baotou Medical College, Baotou, Inner Mongolia, China.
Yunwei SunDepartment of Gastroenterology, Ruijin Hospital, Shanghai Jiaotong University, Gubei Branch, Shanghai, People's Republic of China.
Xiuling LiDepartment of Gastroenterology, School of Clinical Medicine, Henan Provincial People's Hospital, People's Hospital of Zhengzhou University, Henan University, Zhengzhou, Henan, China.
Zhihong LiuDepartment of Gastroenterology, Jilin City People's Hospital, Jilin, China.
Jinzhong ChenEndoscopy Center, School of Medicine, The First Affiliated Hospital of Xiamen University, Xiamen University, Xiamen, China.
Ying SongDepartment of Gastroenterology, Xi'an Gaoxin Hospital, Xi'an, 710032, Shaanxi Province, China.
Guowei LiuYi Xin Clinic, Changzhou, Jiangsu, China.
Chaijie LuoRenmin Hospital of Wuhan University, Wuhan, China.
Yanxia LiRenmin Hospital of Wuhan University, Wuhan, China.
Xiaoquan ZengRenmin Hospital of Wuhan University, Wuhan, China.
Jun LiuRenmin Hospital of Wuhan University, Wuhan, China.
Yijie ZhuRenmin Hospital of Wuhan University, Wuhan, China.
Lianlian WuRenmin Hospital of Wuhan University, Wuhan, China. wu_leanne@163.com.
Honggang YuRenmin Hospital of Wuhan University, Wuhan, China. yuhonggang@whu.edu.cn.ORCID 0000-0001-5986-1284

Funding

Artificial Intelligence Application Demonstration Scenario Project Wuhan 2022YYCJ01National Natural Science Foundation of China-Youth Science Fund 82202257Special projects for knowledge innovation of Wuhan 2022020801020482
6 · The paper itself

Abstract

backgroundArtificial intelligence (AI) performed variously among test sets with different diversity due to sample selection bias, which can be stumbling block for AI applications. We previously tested AI named ENDOANGEL, diagnosing early gastric cancer (EGC) on single-center videos in man-machine competition. We aimed to re-test ENDOANGEL on multi-center videos to explore challenges applying AI in multiple centers, then upgrade ENDOANGEL and explore solutions to the challenge.

methodsENDOANGEL was re-tested on multi-center videos retrospectively collected from 12 institutions and compared with performance in previously reported single-center videos. We then upgraded ENDOANGEL to ENDOANGEL-2022 with more training samples and novel algorithms and conducted competition between ENDOANGEL-2022 and endoscopists. ENDOANGEL-2022 was then tested on single-center videos and compared with performance in multi-center videos; the two AI systems were also compared with each other and endoscopists.

resultsForty-six EGCs and 54 non-cancers were included in multi-center video cohort. On diagnosing EGCs, compared with single-center videos, ENDOANGEL showed stable sensitivity (97.83% vs. 100.00%) while sharply decreased specificity (61.11% vs. 82.54%); ENDOANGEL-2022 showed similar tendency while achieving significantly higher specificity (79.63%, p < 0.01) making fewer mistakes on typical lesions than ENDOANGEL. On detecting gastric neoplasms, both AI showed stable sensitivity while sharply decreased specificity. Nevertheless, both AI outperformed endoscopists in the two competitions.

conclusionsGreat increase of false positives is a prominent challenge for applying EGC diagnostic AI in multiple centers due to high heterogeneity of negative cases. Optimizing AI by adding samples and using novel algorithms is promising to overcome this challenge.

Indexed as

Artificial IntelligenceStomach NeoplasmsAlgorithmsHumansResearch DesignRetrospective StudiesArtificial intelligenceCase diversityEarly gastric cancer

Identifiers

PMID37515597

What OpenQuestion holds

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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.