Automatic visualization of the mandibular canal in relation to an impacted mandibular third molar on panoramic radiographs using deep learning segmentation and transfer learning techniques

射线照相术 臼齿 分割 雅卡索引 下颌管 医学 下颌骨(节肢动物口器) 口腔正畸科 牙科 学习迁移 人工智能 计算机科学 模式识别(心理学) 放射科 植物 生物
作者
Yoshiko Ariji,Mizuho Mori,Motoki Fukuda,Akitoshi Katsumata,Eiichiro Ariji
出处
期刊:Oral Surgery, Oral Medicine, Oral Pathology, and Oral Radiology [Elsevier BV]
卷期号:134 (6): 749-757 被引量:15
标识
DOI:10.1016/j.oooo.2022.05.014
摘要

ObjectiveThe aim of this study was to create and assess a deep learning model using segmentation and transfer learning methods to visualize the proximity of the mandibular canal to an impacted third molar on panoramic radiographs.Study DesignThe panoramic radiographs containing the mandibular canal and impacted third molar were collected from 2 hospitals (Hospitals A and B). A total of 3200 areas were used for creating and evaluating learning models. A source model was created using the data from Hospital A, simulatively transferred to Hospital B, and trained using various amounts of data from Hospital B to create target models. The same data were then applied to the target models to calculate the Dice coefficient, Jaccard index, and sensitivity.ResultsThe performance of target models trained using 200 or more data sets was equivalent to that of the source model tested using data obtained from the same hospital (Hospital A).ConclusionsSufficiently qualified models could delineate the mandibular canal in relation to an impacted third molar on panoramic radiographs using a segmentation technique. Transfer learning appears to be an effective method for creating such models using a relatively small number of data sets. The aim of this study was to create and assess a deep learning model using segmentation and transfer learning methods to visualize the proximity of the mandibular canal to an impacted third molar on panoramic radiographs. The panoramic radiographs containing the mandibular canal and impacted third molar were collected from 2 hospitals (Hospitals A and B). A total of 3200 areas were used for creating and evaluating learning models. A source model was created using the data from Hospital A, simulatively transferred to Hospital B, and trained using various amounts of data from Hospital B to create target models. The same data were then applied to the target models to calculate the Dice coefficient, Jaccard index, and sensitivity. The performance of target models trained using 200 or more data sets was equivalent to that of the source model tested using data obtained from the same hospital (Hospital A). Sufficiently qualified models could delineate the mandibular canal in relation to an impacted third molar on panoramic radiographs using a segmentation technique. Transfer learning appears to be an effective method for creating such models using a relatively small number of data sets.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
卷卷完成签到,获得积分10
2秒前
JSY完成签到 ,获得积分20
2秒前
xyh完成签到,获得积分10
3秒前
小曾应助Florencia采纳,获得10
4秒前
神外王001完成签到 ,获得积分10
4秒前
9秒前
你是谁完成签到,获得积分10
10秒前
majf完成签到,获得积分10
11秒前
linhanwenzhou完成签到,获得积分10
11秒前
JSY关注了科研通微信公众号
11秒前
853225598完成签到,获得积分10
11秒前
798完成签到,获得积分10
12秒前
善学以致用应助董怼怼采纳,获得10
12秒前
妍儿完成签到,获得积分20
13秒前
隐形曼青应助高大的水壶采纳,获得10
13秒前
马哥二弟无敌完成签到 ,获得积分10
14秒前
15秒前
Florencia完成签到,获得积分10
15秒前
务实颜完成签到 ,获得积分10
15秒前
15秒前
amberzyc应助小远采纳,获得10
16秒前
16秒前
17秒前
17秒前
18秒前
18秒前
Rondab应助小猪采纳,获得30
18秒前
DLDL完成签到,获得积分10
18秒前
19秒前
沧海云完成签到 ,获得积分10
19秒前
发嗲的迎天完成签到 ,获得积分10
20秒前
hahaha发布了新的文献求助10
21秒前
小马甲应助zx0914采纳,获得10
21秒前
阳光保温杯完成签到 ,获得积分10
21秒前
微冷潇一应助mo采纳,获得10
22秒前
顺心凝天完成签到,获得积分10
22秒前
yishiqi10086发布了新的文献求助10
23秒前
何相逢应助科研通管家采纳,获得10
24秒前
24秒前
CipherSage应助科研通管家采纳,获得10
24秒前
高分求助中
【提示信息,请勿应助】关于scihub 10000
Les Mantodea de Guyane: Insecta, Polyneoptera [The Mantids of French Guiana] 3000
徐淮辽南地区新元古代叠层石及生物地层 3000
The Mother of All Tableaux: Order, Equivalence, and Geometry in the Large-scale Structure of Optimality Theory 3000
Handbook of Industrial Diamonds.Vol2 1100
Global Eyelash Assessment scale (GEA) 1000
Picture Books with Same-sex Parented Families: Unintentional Censorship 550
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 遗传学 基因 物理化学 催化作用 冶金 细胞生物学 免疫学
热门帖子
关注 科研通微信公众号,转发送积分 4038368
求助须知:如何正确求助?哪些是违规求助? 3576068
关于积分的说明 11374313
捐赠科研通 3305780
什么是DOI,文献DOI怎么找? 1819322
邀请新用户注册赠送积分活动 892672
科研通“疑难数据库(出版商)”最低求助积分说明 815029