Evidence map›Paper›PMID 38202143›Full record

ArticleJournal of clinical medicine2023

Machine Learning Analysis of Gaze Data for Enhanced Precision in Diagnosing Oral Mucosal Diseases.

Shuji Uchida, Shin-Ichiro Hiraoka, Kohei Kawamura, Katsuya Sakamoto, Ryo Akiyama, Susumu Tanaka

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of clinical medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.5field-weighted citation impact, top 33% 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

2 citing papers in PubMed, 2 citations in OpenAlex.

  1. Article
  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

6 authors at 1 institution in 1 country.

Shuji UchidaDepartment of Oral and Maxillofacial Surgery, Graduate School of Dentistry, Osaka University, 1-8 Yamada-Oka, Suita 565-0871, Osaka, Japan.
Shin-Ichiro HiraokaDepartment of Oral and Maxillofacial Surgery, Graduate School of Dentistry, Osaka University, 1-8 Yamada-Oka, Suita 565-0871, Osaka, Japan.
Kohei KawamuraDepartment of Oral and Maxillofacial Surgery, Graduate School of Dentistry, Osaka University, 1-8 Yamada-Oka, Suita 565-0871, Osaka, Japan.ORCID 0000-0002-2983-0943
Katsuya SakamotoDepartment of Oral and Maxillofacial Surgery, Graduate School of Dentistry, Osaka University, 1-8 Yamada-Oka, Suita 565-0871, Osaka, Japan.
Ryo AkiyamaDepartment of Oral and Maxillofacial Surgery, Graduate School of Dentistry, Osaka University, 1-8 Yamada-Oka, Suita 565-0871, Osaka, Japan.
Susumu TanakaDepartment of Oral and Maxillofacial Surgery, Graduate School of Dentistry, Osaka University, 1-8 Yamada-Oka, Suita 565-0871, Osaka, Japan.ORCID 0000-0002-0306-3627
The University of Osaka · JP

Funding

Japan Society for the Promotion of Science Grants-in-Aid for Scien-tific Research program JP20K10115
6 · The paper itself

Abstract

The diagnosis of oral mucosal diseases is a significant challenge due to their diverse differential characteristics. Risk assessment of lesions by visual examination is a complex process due to the lack of definitive guidelines. This study aimed to improve this process by creating a diagnostic algorithm using gaze data acquired during oral mucosal disease examinations. A total of 78 dentists were included in this study. Tobii Pro Nano

Indexed as

diagnostic algorithmgaze dataheatmap analysisintraoral photographsoral mucosal diseasessupport vector machinesvisual examination

Identifiers

PMID38202143
PMCPMC10780288
OpenAlexW4390272559

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.