Evidence map›Paper›PMID 40008787›Full record

ReviewChinese medical journal2025

Artificial intelligence in endoscopic diagnosis of esophageal squamous cell carcinoma and precancerous lesions.

Nuoya Zhou, Xianglei Yuan, Wei Liu, Qi Luo, Ruide Liu, Bing Hu

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Chinese medical journal, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Review
  2. Review
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

6 authors.

Nuoya ZhouDepartment of Gastroenterology and Hepatology, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China.
Xianglei YuanDigestive Endoscopy Medical Engineering Research Laboratory, West China Hospital, Med-X Center for Materials, Sichuan University, Chengdu, Sichuan 610041, China.
Wei LiuDepartment of Gastroenterology and Hepatology, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China.
Qi LuoDepartment of Gastroenterology and Hepatology, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China.
Ruide LiuDepartment of Gastroenterology and Hepatology, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China.
Bing HuDepartment of Gastroenterology and Hepatology, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

abstractEsophageal squamous cell carcinoma (ESCC) poses a significant global health challenge, necessitating early detection, timely diagnosis, and prompt treatment to improve patient outcomes. Endoscopic examination plays a pivotal role in this regard. However, despite the availability of various endoscopic techniques, certain limitations can result in missed or misdiagnosed ESCCs. Currently, artificial intelligence (AI)-assisted endoscopic diagnosis has made significant strides in addressing these limitations and improving the diagnosis of ESCC and precancerous lesions. In this review, we provide an overview of the current state of AI applications for endoscopic diagnosis of ESCC and precancerous lesions in aspects including lesion characterization, margin delineation, invasion depth estimation, and microvascular subtype classification. Furthermore, we offer insights into the future direction of this field, highlighting potential advancements that can lead to more accurate diagnoses and ultimately better prognoses for patients.

Indexed as

Artificial IntelligenceEsophageal NeoplasmsEsophageal Squamous Cell CarcinomaPrecancerous ConditionsHumansArtificial intelligenceEndoscopyEsophageal squamous cell carcinomaPrecancerous lesions

Identifiers

PMID40008787
PMCPMC12180810

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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Registered trials

None linked

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.