Evidence map›Paper›PMID 37283110›Full record

ReviewZhejiang da xue xue bao. Yi xue ban = Journal of Zhejiang University. Medical sciences2023

Application of artificial neural network algorithm in pathological diagnosis and prognosis prediction of digestive tract malignant tumors.

Ya Xiao, Shuyang Wang, Ren Ling, Yufei Song

Open access · diamondAbstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Zhejiang da xue xue bao. Yi xue ban = Journal of Zhejiang University. Medical sciences, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
0.9field-weighted citation impact, top 25% 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

0 citing papers in PubMed, 4 citations in OpenAlex.

No citing paper in PubMed yet.

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 at 4 institutions in 1 country.

Ya XiaoHealth Science Center, Ningbo University, Ningbo 315211, Zhejiang Province, China. xiaoyatosee@163.com.
Shuyang WangDepartment of Pathology, School of Basic Medical Sciences, Fudan University, Shanghai Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention, Shanghai 200032, China.
Ren LingShanghai Laizi Software Technology Co. Ltd., Shanghai 201499, China.
Yufei SongDepartment of Gastroenterology, the Affiliated Lihuili Hospital, Ningbo University, Ningbo 315046, Zhejiang Province, China. 517582445@qq.com.
DHC Software (China) · CNNingbo Medical Center Lihuili Hospital · CNNingbo University · CNShanghai Medical College of Fudan University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The application of artificial neural network algorithm in pathological diagnosis of gastrointestinal malignant tumors has become a research hotspot. In the previous studies, the algorithm research mainly focused on the model development based on convolutional neural networks, while only a few studies used the combination of convolutional neural networks and recurrent neural networks. The research contents included classical histopathological diagnosis and molecular typing of malignant tumors, and the prediction of patient prognosis by utilizing artificial neural networks. This article reviews the research progress on artificial neural network algorithm in the pathological diagnosis and prognosis prediction of digestive tract malignant tumors.

Indexed as

Gastrointestinal NeoplasmsNeural Networks, ComputerAlgorithmsHumansPrognosisAlgorithmArtificial neural networkDigestive tract malignant tumorPathological diagnosisPrognosis predictionReview

Identifiers

PMID37283110
PMCPMC10293779
OpenAlexW4376130564

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.