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      Estado del arte de inteligencia artificial en ortodoncia. Revisión narrativa Translated title: State of the art on artificial intelligence in orthodontics. A narrative review

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          Abstract

          RESUMEN Introducción: Inteligencia artificial (IA) es la automatización de actividades vinculadas con procesos de pensamiento humano. En ortodoncia se han desarrollado sistemas que asistidos por IA pueden tomar decisiones terapéuticas y realizar análisis. No existe un compendio actualizado sobre el uso de IA en ortodoncia. Objetivos: Describir los usos de IA en ortodoncia de acuerdo con la literatura actual. Metodología: Se realizó una revisión narrativa en las bases Medline y SciELO mediante la búsqueda: (orthodont*) AND (“machine learning” OR “deep learning” OR “artificial intelligence” OR “neural network”). Resultados: Se obtuvieron 19 artículos que mostraron que IA se ha desarrollado en cinco áreas: 1) Cefalometría asistida por IA, donde la localización de puntos y análisis cefalométricos mostraron una precisión igual a ortodoncistas. 2) Localización de dientes no erupcionados en CBCT, con resultados similares entre IA y ortodoncistas. 3) Determinación de edad y maduración ósea de forma más eficiente apoyada por IA, que por métodos convencionales, 4) Análisis facial, donde la IA permite una evaluación objetiva del atractivo facial, con aplicaciones en diagnóstico y planificación quirúrgica. 5) Decisiones terapéuticas con IA, para determinar la necesidad de exodoncias y dientes que serán extraídos. Discusión: La IA se está incorporando aceleradamente en ortodoncia, por lo que debe conocerse conceptos y posibilidades que brinda. Conclusiones: Un número creciente de artículos sobre usos de IA en ortodoncia muestran resultados similares con IA a los obtenidos por especialistas. Sin embargo, la evidencia aún es poca y principalmente experimental, por lo que la IA debiera usarse cautelosamente en ortodoncia.

          Translated abstract

          ABSTRACT Introduction: Artificial Intelligence (AI) is the automation of activities related to human thought processes. In orthodontics, systems have been developed which, assisted by AI, can make therapeutic decisions and perform analyses. There is no updated compendium on the use of AI in orthodontics. Objectives: To describe the uses of AI in orthodontics according to the current literature. Methodology: A narrative review was performed in the Medline and SciELO bases by means of the following search: (orthodont*) AND (“machine learning” OR “deep learning” OR “artificial intelligence” OR “neural network”). Results: 19 articles were obtained, showing that AI has been developed in four areas: 1) IA assisted cephalometry, where localization of cephalometric points and cephalometric analysis showed equal accuracy than orthodontists. 2) Unerupted tooth localization with CBCT, with similar results between AI and orthodontists. 3) Determination of skeletal age, which is more efficient with AI than with conventional methods. 4) Facial analysis, where AI allows an objective evaluation of facial attractiveness with applications in diagnosis and surgical planning. 5) Therapeutic decisions with AI, to determine the need for exodontia and teeth to be extracted. Discussion: AI is being incorporated rapidly in orthodontics, so we must know concepts and possibilities that it gives us in orthodontics. Conclusions: An increasing number of articles refer to the uses of AI in orthodontics, with similar results to those obtained by specialists. However, the evidence is still scarce and mainly experimental, so AI should still be used with caution in orthodontics.

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          Most cited references34

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          A guide to deep learning in healthcare

          Here we present deep-learning techniques for healthcare, centering our discussion on deep learning in computer vision, natural language processing, reinforcement learning, and generalized methods. We describe how these computational techniques can impact a few key areas of medicine and explore how to build end-to-end systems. Our discussion of computer vision focuses largely on medical imaging, and we describe the application of natural language processing to domains such as electronic health record data. Similarly, reinforcement learning is discussed in the context of robotic-assisted surgery, and generalized deep-learning methods for genomics are reviewed.
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            Convolutional neural networks: an overview and application in radiology

            Abstract Convolutional neural network (CNN), a class of artificial neural networks that has become dominant in various computer vision tasks, is attracting interest across a variety of domains, including radiology. CNN is designed to automatically and adaptively learn spatial hierarchies of features through backpropagation by using multiple building blocks, such as convolution layers, pooling layers, and fully connected layers. This review article offers a perspective on the basic concepts of CNN and its application to various radiological tasks, and discusses its challenges and future directions in the field of radiology. Two challenges in applying CNN to radiological tasks, small dataset and overfitting, will also be covered in this article, as well as techniques to minimize them. Being familiar with the concepts and advantages, as well as limitations, of CNN is essential to leverage its potential in diagnostic radiology, with the goal of augmenting the performance of radiologists and improving patient care. Key Points • Convolutional neural network is a class of deep learning methods which has become dominant in various computer vision tasks and is attracting interest across a variety of domains, including radiology. • Convolutional neural network is composed of multiple building blocks, such as convolution layers, pooling layers, and fully connected layers, and is designed to automatically and adaptively learn spatial hierarchies of features through a backpropagation algorithm. • Familiarity with the concepts and advantages, as well as limitations, of convolutional neural network is essential to leverage its potential to improve radiologist performance and, eventually, patient care.
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              Artificial intelligence in medicine.

              Artificial Intelligence (AI) is a general term that implies the use of a computer to model intelligent behavior with minimal human intervention. AI is generally accepted as having started with the invention of robots. The term derives from the Czech word robota, meaning biosynthetic machines used as forced labor. In this field, Leonardo Da Vinci's lasting heritage is today's burgeoning use of robotic-assisted surgery, named after him, for complex urologic and gynecologic procedures. Da Vinci's sketchbooks of robots helped set the stage for this innovation. AI, described as the science and engineering of making intelligent machines, was officially born in 1956. The term is applicable to a broad range of items in medicine such as robotics, medical diagnosis, medical statistics, and human biology-up to and including today's "omics". AI in medicine, which is the focus of this review, has two main branches: virtual and physical. The virtual branch includes informatics approaches from deep learning information management to control of health management systems, including electronic health records, and active guidance of physicians in their treatment decisions. The physical branch is best represented by robots used to assist the elderly patient or the attending surgeon. Also embodied in this branch are targeted nanorobots, a unique new drug delivery system. The societal and ethical complexities of these applications require further reflection, proof of their medical utility, economic value, and development of interdisciplinary strategies for their wider application.
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                Author and article information

                Journal
                odonto
                Avances en Odontoestomatología
                Av Odontoestomatol
                Ediciones Avances, S.L. (Madrid, Madrid, Spain )
                0213-1285
                2340-3152
                December 2022
                : 38
                : 4
                : 156-163
                Affiliations
                [1] Talca Maule orgnameUniversidad de Talca Chile
                [2] Talca Maule orgnameUniversidad de Talca Chile
                Article
                S0213-12852022000400005 S0213-1285(22)03800400005
                10.4321/s0213-12852022000400005
                e6e402ba-085b-4d17-9966-26e3d0397600

                This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 International License.

                History
                : 09 July 2020
                : 26 July 2020
                Page count
                Figures: 0, Tables: 0, Equations: 0, References: 34, Pages: 8
                Product

                SciELO Spain

                Categories
                Artículos

                Artificial intelligence,cephalometry,orthodontics,cefalometría,ortodoncia,Inteligencia artificial

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