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Updated: Mar 18, 2026

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紧的卷积变压器用于在全景射线图上自动检测儿科牙数异常.

Esra Sivari Resul¹, Güler Burcu Senirkentli², Gazi Erkan Bostancı³

¹Department of Computer Engineering, Cankiri Karatekin University, Cankiri, 18100, Turkey. esrasivari@karatekin.edu.tr.

Journal of imaging informatics in medicine

|March 17, 2026

概括

一种新的AI模型,即紧的卷积变压器 (CIT),通过全景X射线精确检测儿童的牙数异常. 这种自动化的方法有助于诊断诸如牙胚胎缺陷和多余的牙等疾病.

关键词:

紧型进化变压器紧型进化变压器深度学习是一种深度学习.细菌缺乏症细菌缺乏症全景X射线图片全景X射线图片儿科牙科儿科牙科更多的牙是多余的牙.

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科学领域:

牙科牙科是指牙科的专业.
人工智能的人工智能
医疗成像医学成像

背景情况:

儿科牙数异常,包括细菌缺乏和超数牙,显著影响塞,面发育和治疗计划.
准确及时诊断这些异常对于有效的儿科牙科护理至关重要.

研究的目的:

介绍和评估CIT,这是一种新的AI模型,用于在儿科全景X射线图上自动检测永久牙胚胎缺陷和超数牙.
评估与最先进的方法和人类专家牙医相比,CIT的诊断性能.

主要方法:

开发了紧的卷积变压器 (CIT),是一种利用基于自适应卷积的标记器来分析儿科全景放射图的变压器架构.
经验丰富的儿科牙医对1170张儿科全景射线图进行了回顾性收集和选,并通过经验丰富的儿科牙医验证了标签.
评估CIT在多类 (细菌缺乏,正常,超数) 和二进制任务上的表现,包括与两个独立的牙医队伍进行比较.

主要成果:

在三类设置中,CIT实现了高性能,96.00%的准确性,95.29%的F1得分,95.76%的ROC-AUC和93.28%的马修斯相关系数.
人工智能模型的诊断性能明显高于专家儿科牙医组.
使用Grad-CAM可视化来检查模型的决策过程.

结论:

紧卷积变压器 (CIT) 代表了从全景放射图中自动检测儿科牙数异常的重大进步.
本研究介绍了第一个人工智能方法,用于自动检测牙胚胎缺陷,以及在儿童牙科成像变压器中初始应用基于卷积的标记器.
在儿童牙科中,CIT显示了提高诊断准确性和效率的潜力.