Exploring the Role of Convolutional Neural Networks (CNN) in Dental
  Radiography Segmentation: A Comprehensive Systematic Literature Review
Walid Brahmi Imen Jdey Fadoua Drira
In the field of dentistry, there is a growing demand for increased precision
in diagnostic tools, with a specific focus on advanced imaging techniques such
as computed tomography, cone beam computed tomography, magnetic resonance
imaging, ultrasound, and traditional intra-oral periapical X-rays. Deep
learning has emerged as a pivotal tool in this context, enabling the
implementation of automated segmentation techniques crucial for extracting
essential diagnostic data. This integration of cutting-edge technology
addresses the urgent need for effective management of dental conditions, which,
if left undetected, can have a significant impact on human health. The
impressive track record of deep learning across various domains, including
dentistry, underscores its potential to revolutionize early detection and
treatment of oral health issues. Objective: Having demonstrated significant
results in diagnosis and prediction, deep convolutional neural networks (CNNs)
represent an emerging field of multidisciplinary research. The goals of this
study were to provide a concise overview of the state of the art, standardize
the current debate, and establish baselines for future research. Method: In
this study, a systematic literature review is employed as a methodology to
identify and select relevant studies that specifically investigate the deep
learning technique for dental imaging analysis. This study elucidates the
methodological approach, including the systematic collection of data,
statistical analysis, and subsequent dissemination of outcomes. Conclusion:
This work demonstrates how Convolutional Neural Networks (CNNs) can be employed
to analyze images, serving as effective tools for detecting dental pathologies.
Although this research acknowledged some limitations, CNNs utilized for
segmenting and categorizing teeth exhibited their highest level of performance
overall.