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

Uchida, Shuji; Hiraoka, Shin-Ichiro; Kawamura, Kohei et al.

Journal of Clinical Medicine, 2024, 13(1), 136

Number of Access:912（2025-11-01 19:12 Counts）

Identifier to cite or link to this item: https://hdl.handle.net/11094/93533

Link to primary information 

File Format Terms of use Size Views Date.Available Description information
JClinMed_13_1 pdf None 14.0 MB 79 2024.02.01  
Creative Commons : Attribution

Item Information

Output File Export EndNote Basic Export Mendeley

Title
Machine Learning Analysis of Gaze Data for Enhanced Precision in Diagnosing Oral Mucosal Diseases
Creator
CreatorName
Uchida, Shuji
NRID
1000070615616 1000070615616
researchmap permalink
shinichirohiraoka shinichirohiraoka
Scopus Author ID
8670641300 8670641300
CreatorName
Hiraoka, Shin-Ichiro
NRID
1000020880157 1000020880157
ORCID
0000-0002-2983-0943 0000-0002-2983-0943
researchmap permalink
koheikawamura koheikawamura
CreatorName
Kawamura, Kohei
CreatorName
Sakamoto, Katsuya
CreatorName
Akiyama, Ryo
NRID
1000000367541 1000000367541
ORCID
0000-0002-0306-3627 0000-0002-0306-3627
researchmap permalink
mes5 mes5
Scopus Author ID
56395750500 56395750500
CreatorName
Tanaka, Susumu
Subject
diagnostic algorithm
gaze data
heatmap analysis
intraoral photographs
oral mucosal diseases
support vector machines
visual examination
Description
Uchida S., Hiraoka S.I., Kawamura K., et al. Machine Learning Analysis of Gaze Data for Enhanced Precision in Diagnosing Oral Mucosal Diseases. Journal of Clinical Medicine 13, 136 (2024); https://doi.org/10.3390/jcm13010136.
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® (Tobii Technology) was used to acquire gaze data during clinical photographic visual examinations. Advanced analysis tools such as support vector machines and heatmaps were used to visualize the gazing tendencies of a group of skilled oral surgeons, focusing on the number of gazes per region and the gazing time ratios. The preliminary findings showed the possibility of visualizing gazing tendencies and identifying areas of importance for diagnosis. The classification of intraoral photographs based on gross features revealed the existence of an optimal examination method for each category and diagnostically significant areas. This novel approach to analyzing gaze data has the potential to refine diagnostic techniques and increase both accuracy and efficiency.
Publisher
Multidisciplinary Digital Publishing Institute (MDPI)
Source Title
Journal of Clinical Medicine
Volume (Issue)
13(1)
Page
136
Date of Issued
2024-01-01
Language
English
Handle URL
https://hdl.handle.net/11094/93533
eISSN
20770383
Relation.isIdenticalTo
DOI (Publisher Version)
https://doi.org/10.3390/jcm13010136
Access Rights
open access
CopyRight
This article is licensed under a Creative Commons Attribution 4.0 International License.
oaire:version
Version of Record
Category
学術雑誌論文 Journal Article