TY  - JOUR
VL  - 10
JF  - Healthcare (Switzerland)
A1  - AbuSalim, S.
A1  - Zakaria, N.
A1  - Islam, M.R.
A1  - Kumar, G.
A1  - Mokhtar, N.
A1  - Abdulkadir, S.J.
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85140640771&doi=10.3390%2fhealthcare10101892&partnerID=40&md5=5b0778d10a67b94b3e44dc5af71c2233
PB  - MDPI
SN  - 22279032
Y1  - 2022///
ID  - scholars16302
TI  - Analysis of Deep Learning Techniques for Dental Informatics: A Systematic Literature Review
N2  - Within the ever-growing healthcare industry, dental informatics is a burgeoning field of study. One of the major obstacles to the health care systemâ??s transformation is obtaining knowledge and insightful data from complex, high-dimensional, and diverse sources. Modern biomedical research, for instance, has seen an increase in the use of complex, heterogeneous, poorly documented, and generally unstructured electronic health records, imaging, sensor data, and text. There were still certain restrictions even after many current techniques were used to extract more robust and useful elements from the data for analysis. New effective paradigms for building end-to-end learning models from complex data are provided by the most recent deep learning technology breakthroughs. Therefore, the current study aims to examine the most recent research on the use of deep learning techniques for dental informatics problems and recommend creating comprehensive and meaningful interpretable structures that might benefit the healthcare industry. We also draw attention to some drawbacks and the need for better technique development and provide new perspectives about this exciting new development in the field. © 2022 by the authors.
IS  - 10
N1  - cited By 9
AV  - none
ER  -