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Assessing the Efficacy of AI Segmentation in Diagnostics of Nine Supernumerary Teeth in a Pediatric Patient. | LitMetric

Assessing the Efficacy of AI Segmentation in Diagnostics of Nine Supernumerary Teeth in a Pediatric Patient.

Diagnostics (Basel)

Department of Surgery, Faculty of Medical Sciences, University of Kragujevac, 34000 Kragujevac, Serbia.

Published: November 2023

AI Article Synopsis

  • The case study discusses a unique instance of a child having nine extra teeth, exploring how AI and commercial tools can assist in dental analysis and segmentation.
  • AI, particularly convolutional neural networks (CNN), is becoming vital in dentistry for automating the detection of dental structures, which can streamline treatment planning.
  • However, human expertise remains essential, as no single tool currently meets all clinical needs, necessitating the use of multiple tools for effective practice.

Article Abstract

We present a very rare case of a child with nine supernumerary teeth to analyze the potential, benefits, and limitations of artificial intelligence, as well as two commercial tools for tooth segmentation. Artificial intelligence (AI) is increasingly finding applications in dentistry today, particularly in radiography. Special attention is given to models based on convolutional neural networks (CNN) and their application in automatic segmentation of the oral cavity and tooth structures. The integration of AI is gaining increasing attention, and the automation of the detection and localization of supernumerary teeth can accelerate the treatment planning process. Despite advancements in 3D segmentation techniques, relying on trained professionals remains crucial. Therefore, human expertise should remain key, and AI should be seen as a support rather than a replacement. Generally, a comprehensive tool that can satisfy all clinical needs in terms of supernumerary teeth and their segmentation is not yet available, so it is necessary to incorporate multiple tools into practice.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10706728PMC
http://dx.doi.org/10.3390/diagnostics13233563DOI Listing

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