Int J Stomatol

Int J Stomatol ›› 2021, Vol. 48 ›› Issue (4): 475-484.doi: 10.7518/gjkq.2021046

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Application of artificial intelligence in oral diagnosis and treatment

Tian Erkang1(),Xiang Qianrong1,Zhao Xinran1,Peng Jiahan1,Shu Rui2()   

  1. 1. State Key Laboratory of Oral Diseases & National Clinical Research Center for Oral Diseases & West China School of Stomatology, Sichuan University, Chengdu 610041, China
    2. State Key Laboratory of Oral Diseases & National Clinical Research Center for Oral Diseases & Dept. of Pediatric Dentistry, West China Hospital of Stomatology, Sichuan University, Chengdu 610041, China
  • Received:2020-08-16 Revised:2020-12-06 Online:2021-07-01 Published:2021-06-30
  • Contact: Rui Shu E-mail:1064564771@qq.com;weis2004@163.com
  • Supported by:
    National Natural Science Foundation of China(81500885)

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Abstract:

Artificial intelligence is a subject that makes computer simulate some thinking process and intelligent behavior of human, such as learning, reasoning, thinking, planning, etc.. Since its birth, artificial intelligence has developed rapidly and has been widely used in fields including biomedicine, financial trade and so on, while “artificial intelligence + medical treatment” undertakes the important task of promoting medical progress and changing the status quo of medical treatment. As an important part of medicine, stomatology is characterized by complex diseases and precise operation. Traditional diagnosis and treatment methods have some problems that need to be solved urgently. The application of artificial intelligence in stomatology is committed to solving these problems. This paper reviews the application of artificial intelligence in stomatology and gives a prospect.

Key words: artificial intelligence, oral diagnosis and treatment, intelligent medical treatment

CLC Number: 

  • R78

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Fig 1

Application of artificial intelligence in oral diagnosis and treatment"

Tab 1

Application of CNN in the field of stomatology"

CNN的类型 应用 样本数量/个 敏感性/% 特异性/% 准确性/% 年份 参考文献
CNN 区分鳞状细胞癌组织与正常组织,甲状腺癌组织与正常组织,用于头颈部癌性组织的诊断 7、13 81.0、93.0 80.0、89.0 81.0、92.0 2018 [9]
完全深度学习掩码区域卷积神经网络（regional con-
volutional neural network,R-CNN）		 检测和定位牙齿结构 1 024 — — 85.8 2020 [10]
16层CNN 检测胶质母细胞瘤和角囊性牙源性肿瘤（keratocystic odontogenic tumor,KCOT） 500 81.8 83.3 83.0 2018 [11]
CNN 在口腔全景片上分割下牙槽神经（inferior alveolar nerve,IAN）与下颌第三磨牙（M3） 81 95.0、83.8 — 94.7、76.8 2019 [12]
12层（7×7×3通道斑块）CNN 口腔黏膜上皮、上皮下、角蛋白等不同组成层的鉴定及角蛋白区内角蛋白珠特征的识别,为临床医生在诊断口腔癌过程中评估组织学图像提供重要帮助 80 97.8 — 96.9 2018 [13]
回归-深度CNN 区分癌组织与正常组织,自动诊断口腔癌 100 94.0 91.0 91.4 2019 [14]
7层前馈CNN 在全景片上检测根尖病变 2 238 65.0 87.0 85.0 2019 [15]
CNN 在锥形束计算机断层扫描（cone beam computed tomography,CBCT）图像上进行3种牙源性囊性病变：牙源性角化囊肿、牙源性囊肿和根尖周囊肿的检测和诊断 2 126 88.2 77.0 84.7 2020 [16]
深度卷积神经网络 识别基于物联网智能医疗系统中的口腔癌区域结构 1 500 92.0 97.0 96.8 2019 [17]
CNN 预测计算机辅助设计和计算机辅助制造（computer aided design and computer aided manufacturing,CAD/CAM）全瓷冠的脱粘接概率 2 160 — — 97.0 2019 [18]
CNN 口腔鳞状细胞癌颈淋巴结转移的术前CT评价 50 92.0 84.0 88.0 2013 [19]
深度学习模型 识别口腔图像中的咬合关系,对咬合面上的龋齿进行检测和分类 79 — — 61.9 2019 [20]
16层CNN 对全景片进行牙齿检测和编号 1 352 98.0 99.9 99.5 2019 [21]
CNN 识别头颈部肿瘤边缘,包括鳞状细胞癌和甲状腺癌 11 84.0、91.0 77.0、88.0 81.0、90.0 2018 [22]

Fig 2

Workflow of the dental implant planning-surgical navigation-surgical navigation-robot control software system"

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