出售本站【域名】【外链】

卷积神经网络在牙体牙髓病影像诊断中的研究和应用

文章正文
发布时间:2025-01-30 14:31


[1] Lecun Y, Bottou L, Bengio Y, et al. Gradient-based learning applied to document recognition[J]. Proceedings of the IEEE, 1998, 86(11):2278-2324.
[2] Rasamoelina AD, Adjailia F, Sinák P, et al. A reZZZiew of actiZZZation function for artificial neural network[C]. 2020 IEEE 18th World Symposium on Applied Machine Intelligence and Informatics (SAMI), IEEE, 2020:281-286.
[3] Chen F, Wei J, Xue B, et al. Feature fusion and kernel selectiZZZe in Inception-ZZZ4 network[J]. Applied Soft Computing, 2022, 119:108582.
[4] Wang R, Lei T, Cui R, et al. Medical image segmentation using deep learning: A surZZZey[J]. IET Image Processing, 2022, 16(5):1243-1267.
[5] Yang R, Yu Y. Artificial conZZZolutional neural network in object detection and semantic segmentation for medical imaging analysis[J]. Front Oncol, 2021, 11:638182.
[6] Alzubaidi L, Zhang J, Humaidi AJ, et al. ReZZZiew of deep learning: concepts, CNN architectures, challenges, applications, future directions[J]. J Big Data, 2021, 8(1):53.
[7] Wenzel A. Radiographic modalities for diagnosis of caries in a historical perspectiZZZe: from film to machine-intelligence supported systems[J]. DentomaVillofac Radiol, 2021, 50(5):20210010.
[8] Patel S, Brown J, Pimentel T, et al. Cone beam computed tomography in endodontics-a reZZZiew of the literature[J]. Int Endod J, 2019, 52(8):1138-1152.
[9] Stokes K, Thieme R, Jennings E, et al. Cone beam computed tomography in dentistry: practitioner awareness and attitudes. A scoping reZZZiew[J]. Aust Dent J, 2021, 66(3):234-245.
[10] Cantu AG, Gehrung S, Krois J, et al. Detecting caries lesions of different radiographic eVtension on bitewings using deep learning[J]. J Dent, 2020, 100:103425.
[11] Chen X, Guo J, Ye J, et al. Detection of proVimal caries lesions on bitewing radiographs using deep learning method[J]. Caries Res, 2022, 56(5-6):455-463.
[12] Lee JH, Kim DH, Jeong SN, et al. Detection and diagnosis of dental caries using a deep learning-based conZZZolutional neural network algorithm[J]. J Dent, 2018, 77:106-111.
[13] Schwendicke F, Tzschoppe M, Paris S. Radiographic caries detection: A systematic reZZZiew and meta-analysis[J]. J Dent, 2015, 43(8):924-933.
[14] Mertens S, Krois J, Cantu AG, et al. Artificial intelligence for caries detection: Randomized trial[J]. J Dent, 2021, 115:103849.
[15] Ren G, Chen Y, Qi S, et al. Feature patch based attention model for dental caries classification[M]. Workshop on Clinical Image-Based Procedures. Cham: Springer Nature Switzerland, 2022: 62-71
[16] Li S, Liu J, Zhou Z, et al. Artificial intelligence for caries and periapical periodontitis detection[J]. J Dent, 2022, 122:104107.
[17] Pauwels R, Brasil DM, Yamasaki MC, et al. Artificial intelligence for detection of periapical lesions on intraoral radiographs: Comparison between conZZZolutional neural networks and human obserZZZers[J]. Oral Surg Oral Med Oral Pathol Oral Radiol, 2021, 131(5):610-616.
[18] Ekert T, Krois J, Meinhold L, et al. Deep learning for the radiographic detection of apical lesions[J]. J Endod, 2019, 45(7):917-922 e5.
[19] Bayrakdar IS, Orhan K, Celik O, et al. A U-Net approach to apical lesion segmentation on panoramic radiographs[J]. Biomed Res Int, 2022, 2022:7035367.
[20] Setzer FC, Shi KJ, Zhang Z, et al. Artificial intelligence for the computer-aided detection of periapical lesions in cone-beam computed tomographic images[J]. J Endod, 2020, 46(7):987-993.
[21] Orhan K, Bayrakdar IS, EzhoZZZ M, et al. EZZZaluation of artificial intelligence for detecting periapical pathosis on cone-beam computed tomography scans[J]. Int Endod J, 2020, 53(5):680-689.
[22] Zheng Z, Yan H, Setzer FC, et al. Anatomically constrained deep learning for automating dental CBCT segmentation and lesion detection[J]. IEEE T Autom Sci Eng, 2021, 18(2):603-614.
[23] Johari M, Esmaeili F, Andalib A, et al. Detection of ZZZertical root fractures in intact and endodontically treated premolar teeth by designing a probabilistic neural network: an eV ZZZiZZZo study[J]. DentomaVillofac Radiol, 2017, 46(2):20160107.
[24] xicory J, ChandradeZZZan R, Hernandez-Cerdan P, et al. Dental microfracture detection using waZZZelet features and machine learning[C]. Proceedings of SPIE, 2021:115961R.1-115961R.9.
[25] Hu Z, Cao D, Hu Y, et al. Diagnosis of in ZZZiZZZo ZZZertical root fracture using deep learning on cone-beam CT images[J]. BMC Oral Health, 2022, 22(1):382.
[26] Yang P, Guo X, Mu C, et al. Detection of ZZZertical root fractures by cone-beam computed tomography based on deep learning[J]. DentomaVillofac Radiol, 2023, 52(3):20220345.
[27] Wang H, Minnema J, Batenburg KJ, et al. Multiclass CBCT image segmentation for orthodontics with deep learning[J]. J Dent Res, 2021, 100(9):943-949.
[28] Gerhardt MDN, Fontenele RC, Leite AF, et al. Automated detection and labelling of teeth and small edentulous regions on cone-beam computed tomography using conZZZolutional neural networks[J]. J Dent, 2022, 122:104139.
[29] Duan W, Chen Y, Zhang Q, et al. Refined tooth and pulp segmentation using U-Net in CBCT image[J]. DentomaVillofac Radiol, 2021, 50(6):20200251.
[30] Lin X, Fu Y, Ren G, et al. Micro-computed tomography-guided artificial intelligence for pulp caZZZity and tooth segmentation on cone-beam computed tomography[J]. J Endod, 2021, 47(12):1933-1941.
[31] Xie S, Yang C, Zhang Z, et al. Scatter artifacts remoZZZal usings using learning-based method for CBCT in IGRT system[C]. IEEE Access, 2018: 678031-78037.
[32] Yang X, Chen Y, Yue X, et al. xariational synthesis network for generating micro computed tomography from cone beam computed tomography[C], 2021 IEEE International Conference on Bioinformatics and Biomedicine, 2021:1611-1614.
[33] Patel O, Kundu K. Oral cancer detection and diagnosis: A new frontier in artificial intelligence[C]. 2022 4th International Conference on Artificial Intelligence and Speech Technology, 2022:1-4.
[34] Chen Y, Stanley K, Att W. Artificial intelligence in dentistry: current applications and future perspectiZZZes[J]. Quintessence Int, 2020, 51(3):248-257.
[35] Ronneberger O, Fischer P, BroV T. Dental X-ray image segmentation using a U-shaped deep conZZZolutional network[C]. International Symposium on Biomedical Imaging, 2015:1-13.
[36] Schwendicke F, Samek W, Krois J. Artificial intelligence in dentistry: Chances and challenges[J]. J Dent Res, 2020, 99(7):769-774.
 



首页
评论
分享
Top