Evidence map›Paper›PMID 39707394›Full record

ArticleJournal of translational medicine2024

Design and implementation of a radiomic-driven intelligent dental hospital diversion system utilizing multilabel imaging data.

Yanchan Wu, Tao Yu, Meijia Zhang, Yichen Li, Yijun Wang, Dongren Yang, Yun Yang, Hao Lou, Chufan Ren, Enna Cai and 6 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of translational medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

16 authors.

Yanchan Wu *Department of Oral Maxillofacial Surgery, School and Hospital of Stomatology, Wenzhou Medical University, Wenzhou, 325000, Zhejiang, P.R. China.
Tao Yu *Wenzhou Institute, University of Chinese Academy of Sciences, Wenzhou, 325000, P.R. China.
Meijia Zhang *The First School of Medicine, School of Information and Engineering, Wenzhou Medical University, Wenzhou, 325000, P.R. China.
Yichen Li *School of Computer Science and Software Engineering, University of Science and Technology Liaoning, Anshan, 114000, P.R. China.
Yijun WangThe Second Clinical Medical College of Wenzhou Medical University, Wenzhou, 325000, P.R. China.
Dongren YangThe First School of Medicine, School of Information and Engineering, Wenzhou Medical University, Wenzhou, 325000, P.R. China.
Yun YangSchool of Nursing, Wenzhou Medical University, Wenzhou, 325000, P.R. China.
Hao LouThe Second Clinical Medical College of Wenzhou Medical University, Wenzhou, 325000, P.R. China.
Chufan RenThe First School of Medicine, School of Information and Engineering, Wenzhou Medical University, Wenzhou, 325000, P.R. China.
Enna CaiSchool of Nursing, Wenzhou Medical University, Wenzhou, 325000, P.R. China.
Chenyue DaiSchool of Nursing, Wenzhou Medical University, Wenzhou, 325000, P.R. China.
Ruidian SunDepartment of Stomatology, Yueqing Second People's Hospital, Wenzhou, 325000, P.R. China.
Qiang XuDepartment of Oral Maxillofacial Surgery, School and Hospital of Stomatology, Wenzhou Medical University, Wenzhou, 325000, Zhejiang, P.R. China.
Qi ZhaoWenzhou Institute, University of Chinese Academy of Sciences, Wenzhou, 325000, P.R. China. zhaoqi@lnu.edu.cn.
Huanhuan ZhangDepartment of Prosthetics, School and Hospital of Stomatology, Wenzhou Medical University, Wenzhou, 325000, Zhejiang, P.R. China. 13758773520@163.com.
Jiefan LiuDepartment of Oral Maxillofacial Surgery, School and Hospital of Stomatology, Wenzhou Medical University, Wenzhou, 325000, Zhejiang, P.R. China. altmanandliu@163.com.

Funding

Fundamental Research Funds for the Liaoning Universities LJ212410146026Natural Science Foundation of Liaoning Province 2023-MS-288
6 · The paper itself

Abstract

backgroundWith the increasing burden of dental diseases and the limited availability of healthcare resources, traditional triage methods are inadequate in efficiently utilizing healthcare resources and meeting patient needs. The aim of this study is to develop an advanced triage system that combines oral radiomics and biological multi-omics data, which enables accurate departmental referral of patients by automatically interpreting biological information in oral X-ray images.

methodsUsing a multi-label learning algorithm, we analyzed multi-omics data from 3,942 patients with oral diseases from three cohorts between July 1, 2023 and August 18, 2023, and continuously monitored classification accuracy (ACC) metrics.

resultsIn the test cohort and external validation cohort, we used the DenseNet121 model to analyze the multi-omics data and achieved classification accuracies of 0.80 and 0.82, respectively.

conclusionsThe main contribution of this study is to propose a new treatment process that incorporates biological multi-omics data, which reduces the workload of physicians while providing timely and accurate medical care to patients. Through comparative experiments, we demonstrate that the process is more efficient than existing processes. In addition, this intelligent triage system demonstrates high prediction accuracy in practical applications, providing new ideas and methods for biological multi-omics research.

Indexed as

TriageAdultAlgorithmsCohort StudiesDentistryFemaleHospitalsHumansImage Processing, Computer-AssistedMaleMiddle AgedRadiomicsAutomated triageDental hospitalDental X-raysIntelligent triage systemMultiple omics data in biologyMultitagged data

Identifiers

PMID39707394
PMCPMC11662546

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.