Evidence map›Paper›PMID 33624608›Full record

ReviewNan fang yi ke da xue xue bao = Journal of Southern Medical University2021

[Application of artificial intelligence for detection and classification of colon polyps].

X Wang, J Huang, X Ji, Z Zhu

Abstract readReview
In one paragraph

Review in Nan fang yi ke da xue xue bao = Journal of Southern Medical University, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed, 1 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

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 synthesis or guideline pooled it.

  1. Pooled it
  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

4 authors.

X WangInformation Center, First Affiliated Hospital of Kunming Medical University, Kunming 65003, China.
J HuangDepartment of Oncology, First Affiliated Hospital of Kunming Medical University, Kunming 65003, China.
X JiDay Surgery Center, First Affiliated Hospital of Kunming Medical University, Kunming 65003, China.
Z ZhuDay Surgery Center, First Affiliated Hospital of Kunming Medical University, Kunming 65003, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Colorectal cancer is one of the most common cancers worldwide, and colonoscopy has proven to be a preferable modality for screening and surveillance of colorectal cancer. This review discusses the clinical application of artificial intelligence (AI) and computer-aided diagnosis for automated colonoscopic detection and diagnosis of colorectal polyps for better understanding of the application of AI-based computer-aided diagnosis systems especially in terms of machine learning, deep learning and convolutional neural network for screening and surveillance of colorectal cancer.

Indexed as

Colonic PolypsDeep LearningArtificial IntelligenceColonoscopyHumansartificial intelligencecolonic polypscolonoscopycolorectal cancercomputer- aided detectioncomputer-aided diagnosisconvolutional neural networkdeep learningmachine learning

Identifiers

PMID33624608
PMCPMC7905248

What OpenQuestion holds

Textmetadata
Read underepoch 390

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.