Titlebook: Artificial Intelligence in Dentistry; Khalid Shaikh,Sreelekshmi Vivek Bekal,Lubna Abdel Book 2023 The Editor(s) (if applicable) and The A

婉言

书目名称Artificial Intelligence in Dentistry影响因子(影响力)




书目名称Artificial Intelligence in Dentistry影响因子(影响力)学科排名




书目名称Artificial Intelligence in Dentistry网络公开度




书目名称Artificial Intelligence in Dentistry网络公开度学科排名




书目名称Artificial Intelligence in Dentistry被引频次




书目名称Artificial Intelligence in Dentistry被引频次学科排名




书目名称Artificial Intelligence in Dentistry年度引用




书目名称Artificial Intelligence in Dentistry年度引用学科排名




书目名称Artificial Intelligence in Dentistry读者反馈




书目名称Artificial Intelligence in Dentistry读者反馈学科排名

使痛苦

ACME

Most Common Oral Health Conditions,er providing a clear insight on the prevalence of oral diseases, this chapter elaborates on the aetiology, classification, diagnosis and treatment aspects of conditions like dental caries, periodontal diseases, oral cancer, oral manifestation HIV infection, oro-dental trauma, NOMA, cleft lip & palat

Odyssey

Advancements in Dentistry,cements in Dentistry succeeded by the review of the latest technologies. This chapter divulges the various notions that steer the future of Dentistry like Artificial Intelligence, Smart Toothbrush, Augmented Reality, Virtual Reality, Virtual patients, Teledentistry, Smart Teeth, 3D printing, Intra o

打包

Pessary

Artificial Intelligence-Based Dental Diseases Through X-Ray Images Using Entropy CNN-Based and Supps. AI technology has had a significant impact on the health-care business due to the need for precise diagnosis and enhanced patient care. Artificial intelligence has a long way to go in the fields of dentistry and medicine. We proposed three classes for dental classification in this paper: “cavity,

erythema

一回合

Familie: Sozialwissenschaftliche Konstrukte,map. Eventually, to forecast the perfect match for the classification process, support value-based fusion matching is used in the classification phase. When compared to existing approaches, the accuracy is improved to 99.7%.

certain

Artificial Intelligence-Based Dental Diseases Through X-Ray Images Using Entropy CNN-Based and Suppmap. Eventually, to forecast the perfect match for the classification process, support value-based fusion matching is used in the classification phase. When compared to existing approaches, the accuracy is improved to 99.7%.

去才蔑视

ction to next-generation applications and technologies for improving diagnostic accuracy and prediction of treatment outcomes in dentistry through the use of artificial intelligence (AI) and machine learning (ML). The authors attempt to bridge the gap between dental research and global health outcom