Evidence map›Paper›PMID 37749498›Full record

ArticleBMC medical imaging2023

Point-wise spatial network for identifying carcinoma at the upper digestive and respiratory tract.

Lei Zhou, Huaili Jiang, Guangyao Li, Jiaye Ding, Cuicui Lv, Maoli Duan, Wenfeng Wang, Kongyang Chen, Na Shen, Xinsheng Huang

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC medical imaging, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.

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

3 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
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

10 authors.

Lei Zhou *Department of Otorhinolaryngology-Head and Neck Surgery, Zhongshan Hospital Affiliated to Fudan University, Xuhui District, 180 Fenglin Road, , Shanghai, 200032, P. R. China.
Huaili Jiang *Department of Otorhinolaryngology-Head and Neck Surgery, Zhongshan Hospital Affiliated to Fudan University, Xuhui District, 180 Fenglin Road, , Shanghai, 200032, P. R. China.
Guangyao LiDepartment of Otorhinolaryngology-Head and Neck Surgery, Zhongshan Hospital Affiliated to Fudan University, Xuhui District, 180 Fenglin Road, , Shanghai, 200032, P. R. China.
Jiaye DingDepartment of Otorhinolaryngology-Head and Neck Surgery, Zhongshan Hospital Affiliated to Fudan University, Xuhui District, 180 Fenglin Road, , Shanghai, 200032, P. R. China.
Cuicui LvDepartment of Otorhinolaryngology-Head and Neck Surgery, Zhongshan Hospital Affiliated to Fudan University, Xuhui District, 180 Fenglin Road, , Shanghai, 200032, P. R. China.
Maoli DuanDepartment of Clinical Science, Intervention and Technology, Karolinska Institutet, Stockholm, Sweden.
Wenfeng WangInstitute of Artificial Intelligence and Blockchain, Guangzhou University, Guangzhou, 510006, P. R. China.
Kongyang ChenInstitute of Artificial Intelligence and Blockchain, Guangzhou University, Guangzhou, 510006, P. R. China.
Na ShenDepartment of Otorhinolaryngology-Head and Neck Surgery, Zhongshan Hospital Affiliated to Fudan University, Xuhui District, 180 Fenglin Road, , Shanghai, 200032, P. R. China. shen.na@zs-hospital.sh.cn.
Xinsheng HuangDepartment of Otorhinolaryngology-Head and Neck Surgery, Zhongshan Hospital Affiliated to Fudan University, Xuhui District, 180 Fenglin Road, , Shanghai, 200032, P. R. China. huang.xinsheng@zs-hospital.sh.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

problemArtificial intelligence has been widely investigated for diagnosis and treatment strategy design, with some models proposed for detecting oral pharyngeal, nasopharyngeal, or laryngeal carcinoma. However, no comprehensive model has been established for these regions.

aimOur hypothesis was that a common pattern in the cancerous appearance of these regions could be recognized and integrated into a single model, thus improving the efficacy of deep learning models.

methodsWe utilized a point-wise spatial attention network model to perform semantic segmentation in these regions.

resultsOur study demonstrated an excellent outcome, with an average mIoU of 86.3%, and an average pixel accuracy of 96.3%.

conclusionThe research confirmed that the mucosa of oral pharyngeal, nasopharyngeal, and laryngeal regions may share a common appearance, including the appearance of tumors, which can be recognized by a single artificial intelligence model. Therefore, a deep learning model could be constructed to effectively recognize these tumors.

Indexed as

Artificial IntelligenceCarcinomaHumansRespiratory SystemSemanticsArtificial intelligenceHypopharynxLarynxNasopharynxOral pharynx

Identifiers

PMID37749498
PMCPMC10521533

What OpenQuestion holds

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LicenceCC BY
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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.