Understanding deep learning - challenges and prospects

被引:12
|
作者
Adnan, Niha [1 ]
Umer, Fahad [1 ]
机构
[1] Aga Khan Univ Hosp, Dept Surg, Karachi, Pakistan
关键词
Artificial Intelligence; Deep learning; Machine learning; Dentistry; Imaging; Neural networks; Convolutional neural network; Intraoral radiography; Object detection; Semantic segmentation; Instance segmentation; Big data; CONVOLUTIONAL NEURAL-NETWORKS; ARTIFICIAL-INTELLIGENCE;
D O I
10.47391/JPMA.AKU-12
中图分类号
R5 [内科学];
学科分类号
1002 ; 100201 ;
摘要
The developments in Artificial Intelligence have been on the rise since its advent. The advancements in this field have been the innovative research area across a wide range of industries, making its incorporation in dentistry inevitable. Artificial Intelligence techniques are making serious progress in the diagnostic and treatment planning aspects of dental clinical practice. This will ultimately help in the elimination of subjectivity and human error that are often part of radiographic interpretations, and will improve the overall efficiency of the process. The various types of Artificial Intelligence algorithms that exist today make the understanding of their application quite complex. The current narrative review was planned to make comprehension of Artificial Intelligence algorithms relatively straightforward. The focus was planned to be kept on the current developments and prospects of Artificial Intelligence in dentistry, especially Deep Learning and Convolutional Neural Networks in diagnostic imaging. The narrative review may facilitate the interpretation of seemingly perplexing research published widely in dental journals.
引用
收藏
页码:S66 / S70
页数:5
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