Chen Z T, Chen Z F
Department of Oral Implantology, Hospital of Stomatology, Guanghua School of Stomatology, Sun Yat-sen University & Guangdong Provincial Key Laboratory of Stomatology, Guangzhou510055, China.
Clinic of Zhujiang New Town, Hospital of Stomatology, Guanghua School of Stomatology, Sun Yat-sen University & Guangdong Provincial Key Laboratory of Stomatology, Guangzhou510623, China.
Zhonghua Kou Qiang Yi Xue Za Zhi. 2024 Nov 9;59(11):1094-1100. doi: 10.3760/cma.j.cn112144-20240312-00109.
The oral implant surgery robot could achieved basic "surgical operation intelligence"; however, "decision-making artificial intelligence" has not yet been achieved. The author previously discussed the specific concept of decision-making artificial intelligence. During our exploration of decision-making artificial intelligence, our team further integrated the clinical diagnosis and treatment process of oral implantation, along with the data characteristics of decision indicators and the distribution characteristics of demographic information. As a result, we identified five key scientific and technological issues in the process of decision-making artificial intelligence, namely the construction of a specialized annotation database for oral implantation, the prediction of quantitative indicators, the application of three-dimensional imaging, the solution of data imbalance within indicators, and the joint output of multi-property and multimodal indicators in clinical pathways. This paper will review artificial intelligence research in oral implantology and our team's research progress, elaborating on the aforementioned challenges in intelligentization. It aims to provide references for addressing the mentioned scientific issues and guiding future research directions in the construction of decision-making artificial intelligence in oral medicine.
口腔种植手术机器人能够实现基本的“手术操作智能化”；然而，“决策人工智能”尚未实现。作者此前曾探讨过决策人工智能的具体概念。在我们对决策人工智能的探索过程中，我们的团队进一步整合了口腔种植的临床诊疗过程、决策指标的数据特征以及人口统计学信息的分布特征。结果，我们确定了决策人工智能过程中的五个关键科技问题，即口腔种植专用标注数据库的构建、定量指标的预测、三维成像的应用、指标内数据不平衡的解决以及临床路径中多属性和多模态指标的联合输出。本文将回顾口腔种植学中的人工智能研究以及我们团队的研究进展，阐述上述智能化挑战。旨在为解决上述科学问题以及指导口腔医学决策人工智能构建的未来研究方向提供参考。
