Evidence map›Paper›PMID 36193309›Full record

ArticleBioMed research international2022

Artificial Neural Network Assisted Cancer Risk Prediction of Oral Precancerous Lesions.

Wenao Chen, Ruijie Zeng, Yiyao Jin, Xi Sun, Zihan Zhou, Chao Zhu

RetractedAbstract readRetracted Publication
In one paragraph

Article in BioMed research international, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It has been retracted, and should not be counted. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Review
  2. Article
  3. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

6 authors.

Wenao ChenI.M. Borovsky Institute of Dentistry, I.M. Sechenov First Moscow State Medical University, Moscow 119991, Russia.
Ruijie ZengI.M. Borovsky Institute of Dentistry, I.M. Sechenov First Moscow State Medical University, Moscow 119991, Russia.
Yiyao JinI.M. Borovsky Institute of Dentistry, I.M. Sechenov First Moscow State Medical University, Moscow 119991, Russia.
Xi SunI.M. Borovsky Institute of Dentistry, I.M. Sechenov First Moscow State Medical University, Moscow 119991, Russia.
Zihan ZhouSchool of Stomatology, Anhui Medical University, Hefei, 230032 Anhui, China.
Chao ZhuLinyi People's Hospital Department of Oral and Maxillofacial Surgery, Linyi, 276003 Shandong, China.ORCID https://orcid.org/0000-0003-1535-4744

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The incidence of oral cancer is still increasing. It has become very common in patients with malignant tumors, which has forced medical personnel to continuously explore its treatment methods. What kind of method can effectively and correctly diagnose the disease in the early stage and improve the survival rate has become one of the research topics that have attracted much attention. Aiming at this problem, it has great research significance for the field of oral precancerous lesions diagnosis. With the in-depth research on oral precancerous diagnosis, the research on artificial neural network (ANN) in medical diagnosis is gradually carried out. Its performance advantage is of great significance to solve the problem of early and correct disease diagnosis. This paper aimed to investigate the application of ANN-assisted cancer risk prediction method in risk prediction of oral precancerous lesions. Through the analysis and research of ANN and oral cancer, the construction of oral cancer risk prediction model was applied to solve the problem of improving the survival rate of oral cancer patients. In this paper, ANN and oral precancerous lesions were analyzed, the performance of the algorithm was experimentally analyzed, and the relevant theoretical formulas were used to explain. The results showed that the method had higher accuracy than traditional forecasting methods. When

Indexed as

Mouth NeoplasmsPrecancerous ConditionsAlgorithmsHumansIncidenceNeural Networks, Computer

Identifiers

PMID36193309
PMCPMC9526607

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.