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人工智能模型检测上鼻和后牙之间的接触关系

Wanghui Ding¹, Yindi Jiang², Gaozhi Pang³

¹Stomatology Hospital, School of Stomatology, Zhejiang University School of Medicine, Zhejiang Provincial Clinical Research Center for Oral Diseases, Key Laboratory of Oral Biomedical Research of Zhejiang Province, Cancer Center of Zhejiang University, Hangzhou, China.

|May 27, 2024

概括

一个新的深度学习网络,MSF-MPTnet,使用全景放射图准确地评估了上鼻床 (MSF) 和后牙 (MPT) 之间的关系. 这种人工智能模型在检测这些解剖关系方面优于牙医和放射科医生.

科学领域:

牙科牙科是指牙科的专业.
放射学放射学是一门学科.
人工智能的人工智能
医疗成像医学成像

背景情况:

大鼻底 (MSF) 和大后牙 (MPT) 之间的关系对于牙科植入物规划至关重要.
传统上,准确的评估依赖于复杂的成像,如形光束计算机断层扫描 (CBCT),需要先进的分析.

研究的目的:

开发和验证一个深度学习网络,MSF-MPTnet,用于使用全景射线图 (PRs) 进行MSF-MPT关系的自动评估.
将MSF-MPTnet的诊断准确度与人类专家 (牙医和放射科医生) 的诊断准确度进行比较.

主要方法:

收集了1035个PR和1035个CBCT图像的数据集.
根据CBCT,关系被分为非接触 (I类) 和接触 (II类) 组.
用350名PR的子集来测试MSF-MPTnet模型,牙医和放射科医生.

主要成果:

无国界医生-MPTnet表现出可靠的性能,具有灵敏度 (0.682-0.852) 和准确度 (0.890-0.951).
与牙医和放射科医生相比,人工智能模型在检测不同牙位置的I类关系方面取得了更高的准确性 (p < 0.05).
对于MSF-MPTnet的整体准确性在79.7%至90.3%之间,超过了人类专家.

结论:

MSF-MPTnet显著提高了从PRs中检测MSF-MPT关系的准确性.
人工智能模型最大限度地减少了假阳性接触者识别,并可能减少对CBCT扫描的需求.
这种深度学习方法为牙科手术前评估提供了可靠和有效的工具.

关键词:

人工智能的人工智能是人工智能.决策方式决策方式深度学习/机器学习口腔诊断是一种口腔诊断.放射学放射学是一门学科.

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