eprintid: 16302 rev_number: 2 eprint_status: archive userid: 1 dir: disk0/00/01/63/02 datestamp: 2023-12-19 03:22:50 lastmod: 2023-12-19 03:22:50 status_changed: 2023-12-19 03:06:00 type: article metadata_visibility: show creators_name: AbuSalim, S. creators_name: Zakaria, N. creators_name: Islam, M.R. creators_name: Kumar, G. creators_name: Mokhtar, N. creators_name: Abdulkadir, S.J. title: Analysis of Deep Learning Techniques for Dental Informatics: A Systematic Literature Review ispublished: pub note: cited By 9 abstract: Within the ever-growing healthcare industry, dental informatics is a burgeoning field of study. One of the major obstacles to the health care system�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. date: 2022 publisher: MDPI official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85140640771&doi=10.3390%2fhealthcare10101892&partnerID=40&md5=5b0778d10a67b94b3e44dc5af71c2233 id_number: 10.3390/healthcare10101892 full_text_status: none publication: Healthcare (Switzerland) volume: 10 number: 10 refereed: TRUE issn: 22279032 citation: AbuSalim, S. and Zakaria, N. and Islam, M.R. and Kumar, G. and Mokhtar, N. and Abdulkadir, S.J. (2022) Analysis of Deep Learning Techniques for Dental Informatics: A Systematic Literature Review. Healthcare (Switzerland), 10 (10). ISSN 22279032