Demystifying artificial intelligence and deep learning in dentistry
Braz. oral res. (Online)
;
35: e094, 2021. graf
Article
Dans Anglais
| LILACS, BBO
| ID: biblio-1285723
ABSTRACT
Abstract Artificial intelligence (AI) is a general term used to describe the development of computer systems which can perform tasks that normally require human cognition. Machine learning (ML) is one subfield of AI, where computers learn rules from data, capturing its intrinsic statistical patterns and structures. Neural networks (NNs) have been increasingly employed for ML complex data. The application of multilayered NN is referred to as "deep learning", which has been recently investigated in dentistry. Convolutional neural networks (CNNs) are mainly used for processing large and complex imagery data, as they are able to extract image features like edges, corners, shapes, and macroscopic patterns using layers of filters. CNN algorithms allow to perform tasks like image classification, object detection and segmentation. The literature involving AI in dentistry has increased rapidly, so a methodological guidance for designing, conducting and reporting studies must be rigorously followed, including the improvement of datasets. The limited interaction between the dental field and the technical disciplines, however, remains a hurdle for applicable dental AI. Similarly, dental users must understand why and how AI applications work and decide to appraise their decisions critically. Generalizable and robust AI applications will eventually prove helpful for clinicians and patients alike.
Texte intégral:
Disponible
Indice:
LILAS (Amériques)
Sujet Principal:
Intelligence artificielle
/
Apprentissage profond
Type d'étude:
Guide de pratique
/
Étude pronostique
Limites du sujet:
Humains
langue:
Anglais
Texte intégral:
Braz. oral res. (Online)
Thème du journal:
Dentisterie
Année:
2021
Type:
Article
Pays d'affiliation:
Brésil
/
Allemagne
Institution/Pays d'affiliation:
Charité - Universitätsmedizin Berlin/DE
/
Universidade Federal do Rio Grande do Sul/BR
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