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Applications of deep learning in dentistry.

Stefano Corbella1, Shanmukh Srinivas2, Federico Cabitza3

  • 1Department of Biomedical, Surgical and Dental Sciences, Università degli Studi di Milano, Milan, Italy; IRCCS Istituto Ortopedico Galeazzi, Milan, Italy; Department of Oral Surgery, Institute of Dentistry, I. M. Sechenov First Moscow State Medical University, Moscow, Russia.

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Summary

Artificial intelligence (AI), specifically deep learning, shows promise in dental diagnostics using image analysis. However, current studies have significant limitations impacting reliability and real-world application.

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Area of Science:

  • Dentistry
  • Artificial Intelligence
  • Medical Imaging

Background:

  • Artificial intelligence (AI) is increasingly explored in medicine.
  • Deep learning (DL), a subset of AI, excels in image processing and shows potential as a diagnostic support tool in dentistry.

Purpose of the Study:

  • To review and critically evaluate existing dental literature on deep learning applications.
  • To identify methodological weaknesses and areas for future development in DL for dentistry.

Main Methods:

  • Systematic review of 28 studies on deep learning applications in dentistry.
  • Critical evaluation of study methodologies, including examiner reliability, training/testing data, and validation methods.

Main Results:

  • Deep learning applications in dentistry report high accuracy.
  • Many studies suffer from substantial limitations, including small sample sizes and inadequate validation, affecting external validity.

Conclusions:

  • While DL shows potential in dental diagnostics, current research has significant methodological flaws.
  • Future studies must address these limitations to establish the true clinical utility of DL in dentistry.