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\author[1]{Walid Brahmi}%
\author[1]{Imen Jdey}%
\author[1]{Fadoua Drira}%
\affil[1]{* Research Groups in Intelligent Machines (REGIM Lab), University of Sfax, National Engineering School of Sfax (ENIS)}%


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  \date{May 06, 2026}


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\begin{abstract}
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. Results: In incorporating 45
studies, we identified selection criteria and research objectives,
addressing significant gaps in the existing literature. These studies
assist clinicians in examining dental conditions and classifying dental
structures, including caries detection and the identification of various
tooth types. We evaluated model performance, addressing the identified
gaps, using diverse metrics that we strive to list and explain.
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.%
\end{abstract}%



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