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. 2023 Jan 6;27(5):e22spe5.
doi: 10.1590/2177-6709.27.5.e22spe5. eCollection 2023.

Decoding Deep Learning applications for diagnosis and treatment planning

Jean-Marc Retrouvey  1 , Richard Scott Conley  1
Affiliations

Decoding Deep Learning applications for diagnosis and treatment planning

Jean-Marc Retrouvey et al. Dental Press J Orthod. .

Abstract
in English, Portuguese

Introduction: Artificial Intelligence (AI), Machine Learning and Deep Learning are playing an increasingly significant role in the medical field in the 21st century. These recent technologies are based on the concept of creating machines that have the potential to function as a human brain. It necessitates the gathering of large quantity of data to be processed. Once processed with AI machines, these data have the potential to streamline and improve the capabilities of the medical field in diagnosis and treatment planning, as well as in the prediction and recognition of diseases. These concepts are new to Orthodontics and are currently limited to image processing and pattern recognition.

Objective: This article exposes and describes the different methods by which orthodontics may benefit from a more widespread adoption of these technologies.

Introdução:: Inteligência Artificial (AI, de Artificial Intelligence), Machine Learning (Aprendizado de máquinas) e Deep Learning (Aprendizado Profundo) possuem um papel significativo e crescente na área médica do século 21. Essas tecnologias recentes são baseadas no conceito de criar máquinas com potencial de funcionar como um cérebro humano. Isso demanda que uma grande quantidade de dados seja reunida para ser processada. Uma vez processados em máquinas com AI, esses dados têm o potencial de agilizar e potencializar as capacidades de diagnóstico e planejamento do tratamento nas áreas médicas, assim como no diagnóstico e prognóstico de doenças. Esses são conceitos novos na Ortodontia, que atualmente são subutilizados, limitando-se ao processamento de imagens e reconhecimento de padrões.

Objetivo:: O presente artigo expõe e descreve os diferentes métodos pelos quais os ortodontistas podem se beneficiar com o uso mais abrangente dessas tecnologias.

PubMed Disclaimer

Conflict of interest statement

The authors report no commercial, proprietary or financial interest in the products or companies described in this article.

Figures

Figure 1:
Figure 1:. Perceptron, or “artificial neuron”. From left to right: The input layer is used to import the data into the system. The weight (W) are values from 0 to 1 attributed to the input. The Sum Σ is given by the addition of the input multiplied by their respective weights. An activation function is then used to obtain the output.
Figure 2:
Figure 2:. Deep learning neural network.
Figure 3:
Figure 3:. A) Invisalign ClinCheck. B) Blender derived tooth movement from CBCT.
Figure 4:
Figure 4:. Diagnostic workflow to assist orthodontist in decision making.
Figure 5:
Figure 5:. A) CBCT with multiplanar view and 3D rendering. B) Enhanced CBCT image pre-segmentation, using the Drishti software ( The Australian National University, Canberra, Australia ).
Figure 6:
Figure 6:. OrthoCAD™ for tooth, used for measurements.
Figure 7:
Figure 7:. Segmentation of CBCT data using the Diagnocat™ software (Diagnocat Inc.) and deep learning algorithms.
Figure 8:
Figure 8:. Segmented maxilla showing the bony contours and the roots.
Figure 9:
Figure 9:. XYZ coordinates using DDP software.
Figure 10:
Figure 10:. Virtual articulator based on segmented CBCT data, using Blender add-on ( Cavycon ).
Figure 11:
Figure 11:. Digitally determined line of force of an impacted canine, created by a Python script in Blender software.
See this image and copyright information in PMC

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