Application of Deep Learning in Dental Implant Imaging: Research Progress and Challenges

doi:10.11871/jfdc.issn.2096-742X.2024.03.004

Abstract

Abstract:

[Objective] This study systematically reviews and summarizes the research progress of deep learning in the field of implantology, including its applications in image processing, implant system detection, and dental implant prognosis. [Methods] The research in the field of dental implantology based on deep learning is classified and summarized according to research directions, elucidating the main research topics and conclusions. [Results] Deep learning technology has made significant achievements in the field of oral implantology. Intelligent segmentation and recognition techniques in oral imaging have improved the diagnostic accuracy and efficiency of oral healthcare professionals. Additionally, automated detection of implant systems in oral surgery aids in rapid assessing of patients'oral conditions. Furthermore, deep learning plays a crucial role in predicting oral implant outcomes, enabling healthcare providers to intervene early and enhance treatment results. [Conclusions] Deep learning holds immense potential in the field of oral implantology, facilitating more precise and efficient procedures, thereby empowering oral healthcare professionals.

Key words: oral implantology, deep learning, neural networks, cone-beam computed tomography (CBCT)

ZHENG Yinuo, SUN Muyi, ZHANG Hongyun, ZHANG Jing, DENG Tianzheng, LIU Qian. Application of Deep Learning in Dental Implant Imaging: Research Progress and Challenges[J]. Frontiers of Data and Computing, 2024, 6(3): 41-49, https://cstr.cn/32002.14.jfdc.CN10-1649/TP.2024.03.004.

Figures/Tables 1

Table 1

Basic information about the included articles"

文献编号	发表年份	所用算法	研究对象	研究内容
[1]	2020	U-Net及RNN	CBCT	分割牙根影像
[2]	2022	EfficientDet-D3	曲面断层片	识别牙齿及补料等
[3]	2023	YOLOv7	曲面断层片	识别牙齿及修复体等
[4]	2022	Mask R-CNN及Faster R-CNN	曲面断层片	分割识别牙齿
[5]	2023	CNN transformer UNet	CBCT	分割识别牙齿
[6]	2022	DenseNet169	CBCT	识别缺失牙位
[7]	2022	Faster R-CNN	根尖片	识别种植体及其周围组织
[8]	2023	3D-UNet	CBCT	分割识别种植体
[9]	2022	UNet	CBCT	分割上颌窦
[10]	2023	YOLOv5	CBCT	识别并分类异常上颌窦
[11]	2020	UNet	CBCT	分割下颌神经管
[12]	2022	3D U-Net	CBCT	分割下颌神经管
[13]	2022	Multi-Scale input Residual UNet	CBCT	分割下颌神经管
[14]	2023	类UNet	CBCT	分割下颌神经管
[15]	2023	STSNet	数字化口内扫描	分割牙齿
[16]	2020	Densenet	CBCT及数字化口内扫描	配准方法
[17]	2020	SqueezeNet、GoogLeNet、ResNet-18、MobileNet-v2及ResNet-50	根尖片	识别种植体系统
[18]	2020	GoogLeNet Inception CNN network	曲面断层片	诊断多种牙齿问题
[19]	2023	EfficientNet及Res2Next	曲面断层片	识别种植体系统
[20]	2023	YOLO	曲面断层片	种植体系统分类
[21]	2020	automated deep convolutional neural network	曲面断层片及根尖片	识别种植体系统
[22]	2022	automated deep convolutional neural network	曲面断层片	识别种植体系统
[23]	2023	automatic DL	曲面断层片及根尖片	识别种植体系统
[24]	2023	automatic DL	曲面断层片及根尖片	识别种植体系统
[25]	2021	ResNet	曲面断层片	识别种植体系统及治疗阶段
[26]	2022	Faster R-CNN	根尖片	检测种植体边缘骨丧失
[27]	2023	MobileNet-V3	曲面断层片及根尖片	检测种植体骨结合情况
[28]	2021	R-CNN	根尖片	检测种植体边缘骨丧失
[29]	2023	YOLOv3	根尖片及咬合翼片	检测种植体边缘骨丧失
[30]	2023	YOLOv2	根尖片	检测种植体周围炎
[31]	2022	VGG16、Inception V3及ResNet50	CBCT	预测种植体脱落风险
[32]	2023	ResNet-50	曲面断层片及根尖片	预测种植体预后
[33]	2021	RCNN	CBCT	评估种植体稳定性

Table 1

References 36

[1]	LI Q, CHEN K, HAN L, et al. Automatic tooth roots segmentation of cone beam computed tomography image sequences using U-net and RNN[J]. Journal of X-Ray Science and Technology, 2020, 28(5): 905-922. doi: 10.3233/XST-200678 pmid: 32986647
[2]	CHOI H R, SIADARI T S, KIM J E, et al. Automatic detection of teeth and dental treatment patterns on dental panoramic radiographs using deep neural networks[J]. Forensic Sciences Research, 2022, 7(3): 456-466.
[3]	ALI M A, FUJITA D, KOBASHI S. Teeth and prostheses detection in dental panoramic X-rays using CNN-based object detector and a priori knowledge-based algorithm[J/OL]. Scientific Reports, 2023, 13(1): https://doi.org/10.1038/s41598-023-43591-z.
[4]	CHANDRASHEKAR G, ALQARNI S, BUMANN E E, et al. Collaborative deep learning model for tooth segmentation and identification using panoramic radiographs[J/OL]. Computers in Biology and Medicine, 2022, 148: https://doi.org/10.1016/j.compbiomed.2022.105829.
[5]	CHEN Z, CHEN S, HU F. CTA-UNet: CNN-transformer architecture UNet for dental CBCT images segmentation[J/OL]. Physics in Medicine and Biology, 2023, 68(17): https://dx.doi.org/10.1088/1361-6560/acf026.
[6]	AL-SAREM M, AL-ASALI M, ALQUTAIBI A Y, et al. Enhanced Tooth Region Detection Using Pretrained Deep Learning Models[J/OL]. International Journal of Environmental Research and Public Health, 2022, 19(22): https://doi.org/10.3390/ijerph-192215414.
[7]	JANG W S, KIM S, YUN P S, et al. Accurate detection for dental implant and peri-implant tissue by transfer learning of faster R-CNN: a diagnostic accuracy study[J]. BMC Oral Health, 2022, 22(1): 591. doi: 10.1186/s12903-022-02539-x pmid: 36494645
[8]	ELGARBA B M, VAN AELST S, SWAITY A, et al. Deep learning-based segmentation of dental implants on cone-beam computed tomography images: A validation study[J/OL]. Journal of Dentistry, 2023, 137: https://doi.org/10.1016/j.jdent.2023.104639.
[9]	CHOI H, JEON K J, KIM Y H, et al. Deep learning-based fully automatic segmentation of the maxillary sinus on cone-beam computed tomographic images[J/OL]. Scientific Reports, 2022, 12(1): https://doi.org/10.1038/s41598-022-18436-w.
[10]	ZENG P, SONG R, LIN Y, et al. Abnormal maxillary sinus diagnosing on CBCT images via object detection and 'straight-forward' classification deep learning strategy[J/OL]. Journal of Oral Rehabilitation, 2023, https://dx.doi.org/10.1111/joor.13585.
[11]	JASKARI J, SAHLSTEN J, JÄRNSTEDT J, et al. Deep Learning Method for Mandibular Canal Segmentation in Dental Cone Beam Computed Tomography Volumes[J/OL]. Scientific Reports, 2020, 10(1): https://doi.org/10.1038/s41598-020-62321-3.
[12]	LAHOUD P, DIELS S, NICLAES L, et al. Development and validation of a novel artificial intelligence driven tool for accurate mandibular canal segmentation on CBCT[J/OL]. Journal of Dentistry, 2022, 116: https://doi.org/10.1016/j.jdent.2021.103891.
[13]	USMAN M, REHMAN A, SALEEM A M, et al. Dual-Stage Deeply Supervised Attention-Based Convolutional Neural Networks for Mandibular Canal Segmentation in CBCT Scans[J/OL]. Sensors (Basel), 2022, 22(24): https://doi.org/10.3390/s22249877.
[14]	ZHAO H, CHEN J, YUN Z, et al. Whole mandibular canal segmentation using transformed dental CBCT volume in Frenet frame[J]. Heliyon, 2023, 9(7): e17651.
[15]	LIU Z, HE X, WANG H, et al. Hierarchical Self-Supervised Learning for 3D Tooth Segmentation in Intra-Oral Mesh Scans[J]. IEEE Transactions on Medical Imaging, 2023, 42(2): 467-480.
[16]	KIM M, CHUNG M, SHIN Y G, et al. Automatic registration of dental CT and 3D scanned model using deep split jaw and surface curvature[J/OL]. Computer Methods and Programs in Biomedicine, 2023, 233: https://doi.org/10.1016/j.cmpb.2023.107467.
[17]	KIM J E, NAM N E, SHIM J S, et al. Transfer Learning via Deep Neural Networks for Implant Fixture System Classification Using Periapical Radiographs[J/OL]. Journal of Clinical Medicine, 2020, 9(4): https://doi.org/10.3390/jcm9041117.
[18]	HADJ SAÏD M, LE ROUX M K, CATHERINE J H, et al. Development of an Artificial Intelligence Model to Identify a Dental Implant from a Radiograph[J]. International Journal of Oral and Maxillofacial Implants, 2020, 36(6): 1077-1082. doi: 10.11607/jomi.8060 pmid: 33270045
[19]	KONG H J, EOM S H, YOO J Y, et al. Identification of 130 Dental Implant Types Using Ensemble Deep Learning[J]. International Journal of Oral and Maxillofacial Implants, 2023, 38(1): 150-156.
[20]	KONG H J, YOO J Y, LEE J H, et al. Performance evaluation of deep learning models for the classification and identification of dental implants[J/OL]. Journal of Prosthetic Dentistry, 2023, https://doi.org/10.1016/j.prosdent.2023.07.009.
[21]	LEE J H, KIM Y T, LEE J B, et al. A Performance Comparison between Automated Deep Learning and Dental Professionals in Classification of Dental Implant Systems from Dental Imaging: A Multi-Center Study[J/OL]. Diagnostics (Basel), 2020, 10(11): https://doi.org/10.3390/diagnostics10110910.
[22]	LEE J H, KIM Y T, LEE J B, et al. Deep learning improves implant classification by dental professionals: a multi-center evaluation of accuracy and efficiency[J]. Journal of Periodontal & Implant Science, 2022, 52(3): 220-229.
[23]	PARK W, SCHWENDICKE F, KROIS J, et al. Identification of Dental Implant Systems Using a Large-Scale Multicenter Data Set[J]. Journal of Dental Research, 2023, 102(7): 727-733.
[24]	PARK W, HUH J K, LEE J H. Automated deep learning for classification of dental implant radiographs using a large multi-center dataset[J]. Scientific Reports, 2023, 13(1): 4862.
[25]	SUKEGAWA S, YOSHII K, HARA T, et al. Multi-Task Deep Learning Model for Classification of Dental Implant Brand and Treatment Stage Using Dental Panoramic Radiograph Images[J/OL]. Biomolecules, 2021, 11(6): https://doi.org/10.3390/biom11060815.
[26]	LIU M, WANG S, CHEN H, et al. A pilot study of a deep learning approach to detect marginal bone loss around implants[J]. BMC Oral Health, 2022, 22(1): 11. doi: 10.1186/s12903-021-02035-8 pmid: 35034611
[27]	OH S, KIM Y J, KIM J, et al. Deep learning-based prediction of osseointegration for dental implant using plain radiography[J]. BMC Oral Health, 2023, 23(1): 208. doi: 10.1186/s12903-023-02921-3 pmid: 37031221
[28]	CHA J Y, YOON H I, YEO I S, et al. Peri-Implant Bone Loss Measurement Using a Region-Based Convolutional Neural Network on Dental Periapical Radiographs[J/OL]. Journal of Clinical Medicine, 2021, 10(5): https://doi.org/10.3390/jcm10051009.
[29]	VERA M, GÓMEZ-SILVA M J, VERA V, et al. Artificial Intelligence Techniques for Automatic Detection of Peri-implant Marginal Bone Remodeling in Intraoral Radiographs[J]. Journal of Digital Imaging, 2023, 36(5): 2259-2277. doi: 10.1007/s10278-023-00880-3 pmid: 37468696
[30]	CHEN Y C, CHEN M Y, CHEN T Y, et al. Improving Dental Implant Outcomes: CNN-Based System Accurately Measures Degree of Peri-Implantitis Damage on Periapical Film[J/OL]. Bioengineering (Basel), 2023, 10(6): https://doi.org/10.3390/bioengineering10060640.
[31]	HUANG N, LIU P, YAN Y, et al. Predicting the risk of dental implant loss using deep learning[J]. Journal of Clinical Periodontology, 2022, 49(9): 872-883.
[32]	ZHANG C, FAN L, ZHANG S, et al. Deep learning based dental implant failure prediction from periapical and panoramic films[J]. Quant Imaging Med Surg, 2023, 13(2): 935-945.
[33]	HUANG Z, ZHENG H, HUANG J, et al. The Construction and Evaluation of a Multi-Task Convolutional Neural Network for a Cone-Beam Computed-Tomography-Based Assessment of Implant Stability[J/OL]. Diagnostics (Basel), 2022, 12(11): https://doi.org/10.3390/diagnostics12112673.
[34]	KURT BAYRAKDAR S, ORHAN K, et al. A deep learning approach for dental implant planning in cone-beam computed tomography images[J]. BMC Medical Imaging, 2021, 21(1): 86. doi: 10.1186/s12880-021-00618-z pmid: 34011314
[35]	XIAO Y, LIANG Q, ZHOU L, et al. Construction of a new automatic grading system for jaw bone mineral density level based on deep learning using cone beam computed tomography[J/OL]. Scientific Reports, 2022, 12(1): https://doi.org/10.1038/s41598-022-16074-w.
[36]	SAKAI T, LI H, SHIMADA T, et al. Development of artificial intelligence model for supporting implant drilling protocol decision making[J]. Journal of Prosthodontic Research, 2023, 67(3): 360-365.