Diagnostic Performance of Ai vs Junior Dentists
Comparison of Artificial Intelligence vs. Junior Dentists Diagnostic Performance for Radiographic Caries & Periapical Lesion Detection
OBJECTIVES
- Compare diagnosis of Periapical lesions & Proximal caries by AI and junior dentists
- Evaluate Validity of a deep learning based convolutional neural network (CNN) for detecting proximal caries lesions, periapical lesions compared to junior dentists.
研究概览
地位
地位
详细说明
Statement of problem Current evidence supports the use of AI as a valuable diagnostic platform adjunct for detecting periapical lesions. However, well-designed, high-quality randomized clinical trials are required to assess the potential implementation of AI in the routine practice of periapical lesion diagnosis. In addition, most studies were done on OPG and CBCT not on periapical radiographs. Evidence suggests that AI-assisted systems are effective in detecting large, distinct periapical lesions. However, their performance drops in more complex cases, such as with root-filled teeth and in small periodical lesions The performance of AI in caries detection remains moderate, and its periapical status assessment is insufficient due to high false-positive rates
Research Questions
- Does Trained AI outperform junior dentists in basic diagnostic tasks, specifically in detecting tooth caries and periapical lesions through radiographic interpretation?
- Does AI increase the accuracy of lesion detection?
- Is AI more accurate than junior dentists in detecting carious lesions or periapical lesions?
- Is there a difference between diagnosis of Caries vs Periapical lesions through AI tools compared to junior dentists?
研究类型
研究类型
注册 (估计的)
注册
联系人和位置
学习联系方式
学习联系方式
- 姓名:Susan Hattar, PhD
- 电话号码:+962795642613
- 邮箱:s.hattar@ju.edu.jo
学习地点
-
-
-
Amman、约旦、11181
- The university of Jordan
-
-
参与标准
资格标准
资格标准
适合学习的年龄
接受健康志愿者
有资格学习的性别
取样方法
研究人群
Patients over 18 years of age that were clear and displayed the full anatomical crown and root apex.
描述
Inclusion Criteria:
- Bitewings obtained from patients treated at Jordan University Hospital. Only bitewings of permanent maxillary and mandibular teeth, with the crowns of both dental arch being detectable. Coronal part of the tooth had to be in toto visible on the radiograph and that no structure is projected on the examined tooth, teeth with overlap free projection selected
- Periapical Radiographs: patients over 18 years of age that were clear and displayed the full anatomical crown and root apex.
Exclusion Criteria:
- Radiographs of deciduous teeth or mixed dentition, those with severe noise or haziness, radiographs of partially edentulous areas, teeth exhibiting abnormal crown or root anatomy, and any radiographs that were difficult to assess.
- Overlapping images, radiographs with distortions, noise and shadows, and low quality radiographs
- Radiographs excluded if at least 2-3 examiners did not agree.
学习计划
研究是如何设计的?
设计细节
- 观测模型:COHORT
- 时间观点:CROSS_SECTIONAL
研究衡量的是什么?
主要结果指标
主要结果指标
结果测量 |
措施说明 |
大体时间 |
|---|---|---|
|
(ROCAUC)
大体时间:12 months
|
Receiver-Operating-Characteristics curve |
12 months
|
次要结果测量
次要结果测量
结果测量 |
措施说明 |
大体时间 |
|---|---|---|
|
Diagnostic test parameters
大体时间:12 months
|
Sensitivity, Specificity, PPV, NPV |
12 months
|
合作者和调查者
调查人员
调查人员
- 研究主任:Susan Hattar, PhD、University of Jordan
出版物和有用的链接
一般刊物
- Mertens S, Krois J, Cantu AG, Arsiwala LT, Schwendicke F. Artificial intelligence for caries detection: Randomized trial. J Dent. 2021 Dec;115:103849. doi: 10.1016/j.jdent.2021.103849. Epub 2021 Oct 14.
- Schwendicke F, Tzschoppe M, Paris S. Radiographic caries detection: A systematic review and meta-analysis. J Dent. 2015 Aug;43(8):924-33. doi: 10.1016/j.jdent.2015.02.009. Epub 2015 Feb 24.
- Cantu AG, Gehrung S, Krois J, Chaurasia A, Rossi JG, Gaudin R, Elhennawy K, Schwendicke F. Detecting caries lesions of different radiographic extension on bitewings using deep learning. J Dent. 2020 Sep;100:103425. doi: 10.1016/j.jdent.2020.103425. Epub 2020 Jul 4.
- Putra RH, Doi C, Yoda N, Astuti ER, Sasaki K. Current applications and development of artificial intelligence for digital dental radiography. Dentomaxillofac Radiol. 2022 Jan 1;51(1):20210197. doi: 10.1259/dmfr.20210197. Epub 2021 Jul 8.
- Kuhnisch J, Meyer O, Hesenius M, Hickel R, Gruhn V. Caries Detection on Intraoral Images Using Artificial Intelligence. J Dent Res. 2022 Feb;101(2):158-165. doi: 10.1177/00220345211032524. Epub 2021 Aug 20.
研究记录日期
研究主要日期
学习开始 (估计的)
学习开始
初级完成 (估计的)
初级完成
研究完成 (估计的)
研究完成
研究注册日期
首次提交
首次提交
首先提交符合 QC 标准的
首先提交符合 QC 标准的
首次发布 (实际的)
首次发布
研究记录更新
最后更新发布 (实际的)
最后更新发布
上次提交的符合 QC 标准的更新
上次提交的符合 QC 标准的更新
最后验证
最后验证
更多信息
与本研究相关的术语
其他相关的 MeSH 术语
其他研究编号
其他研究编号
- ToothVision
- IRB (其他标识符:IRB Jordan University Hospital)
计划个人参与者数据 (IPD)
计划共享个人参与者数据 (IPD)?
药物和器械信息、研究文件
研究美国 FDA 监管的药品
研究美国 FDA 监管的设备产品
此信息直接从 clinicaltrials.gov 网站检索,没有任何更改。如果您有任何更改、删除或更新研究详细信息的请求,请联系 register@clinicaltrials.gov. clinicaltrials.gov 上实施更改,我们的网站上也会自动更新.