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Machine Learning in Dentistry [electronic resource]
Ko, Ching-Chang
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| 자료유형 | 단행본 |
|---|---|
| 개인저자 | Ko, Ching-Chang.,editor. Shen, Dinggang.,editor. Wang, Li.,editor. |
| 단체저자명 | SpringerLink (Online service). |
| 서명/저자사항 | Machine Learning in Dentistry [electronic resource] / edited by Ching-Chang Ko, Dinggang Shen, Li Wang. |
| 판사항 | 1st ed. 2021. |
| 형태사항 | X, 188 p. 90 illus., 76 illus. in color:online resource. |
| 기본자료 저록 | Springer Nature eBook |
| 기타형태 저록 | Printed edition:9783030718800Printed edition:9783030718824Printed edition:9783030718831 |
| ISBN | 9783030718817 |
| 기타표준부호 | 10.1007/978-3-030-71881-7 |
| 내용주기 | Machine Learning for Dental Imaging: Machine Learning for CBCT Segmentation of Craniofacial 3D Image -- Machine Learning for Automatic Landmark Detection of 3D Imaging -- Machine Learning for Generating Dental CT from Magnetic Resonance Imaging (MRI) -- Machine Learning for 2D Dynamic Facial Photographs. Machine Learning for Oral Diagnosis and Treatment: Machine Learning for Orthodontic Diagnosis and Treatment Planning -- Machine Learning for Diagnosis of Periodontal Diseases -- Machine Learning for Oral Microbiome -- Machine Learning for Characterization of Craniofacial Anomaly -- Machine Learning for Orthognathic Surgery -- Machine Learning for Bone Tissue Engineering. Machine Learning and Dental Designs: Machine Learning for Orthodontic CAD/CAM Technologies -- Machine Learning for Design of Dental Implants -- Machine Learning for Optimization of Dental Material Processing. Machine Learning Supporting Dental Research: Machine Learning for Data Mining in Teledentistry -- Machine Learning for Evidence-Based Literature Search -- Machine Learning in Genetics and Genomics -- Machine Learning and Finite Element Modeling. |
| 요약 | This book reviews all aspects of the use of machine learning in contemporary dentistry, clearly explaining its significance for dental imaging, oral diagnosis and treatment, dental designs, and dental research. Machine learning is an emerging field of artificial intelligence research and practice in which computer agents are employed to improve perception, cognition, and action based on their ability to “learn”, for example through use of big data techniques. Its application within dentistry is designed to promote personalized and precision patient care, with enhancement of diagnosis and treatment planning. In this book, readers will find up-to-date information on different machine learning tools and their applicability in various dental specialties. The selected examples amply illustrate the opportunities to employ a machine learning approach within dentistry while also serving to highlight the associated challenges. Machine Learning in Dentistry will be of value for all dental practitioners and researchers who wish to learn more about the potential benefits of using machine learning techniques in their work. |
| 일반주제명 | Dentistry. Big data. Dentistry. Big Data. |
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