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Radiation Dosimetry, Artificial Intelligence and Digital Twins: Old Dog, New Tricks.
Currie, Geoffrey M; Rohren, Eric M.
Afiliação
  • Currie GM; Charles Sturt University, NSW, Australia; Baylor College of Medicine, TX. Electronic address: gcurrie@csu.edu.au.
  • Rohren EM; Charles Sturt University, NSW, Australia; Baylor College of Medicine, TX.
Semin Nucl Med ; 53(3): 457-466, 2023 05.
Article em En | MEDLINE | ID: mdl-36379728
ABSTRACT
Developments in artificial intelligence, particularly convolutional neural networks and deep learning, have the potential for problem solving that has previously confounded human intelligence. Accurate prediction of radiation dosimetry pre-treatment with scope to adjust dosing for optimal target and non-target tissue doses is consistent with striving for improved the outcomes of precision medicine. The combination of artificial intelligence and production of digital twins could provide an avenue for an individualised therapy doses and enhanced outcomes in theranostics. While there are barriers to overcome, the maturity of individual technologies (i.e. radiation dosimetry, artificial intelligence, theranostics and digital twins) places these approaches within reach.
Assuntos

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Inteligência Artificial / Redes Neurais de Computação Tipo de estudo: Prognostic_studies Limite: Humans Idioma: En Revista: Semin Nucl Med Ano de publicação: 2023 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Inteligência Artificial / Redes Neurais de Computação Tipo de estudo: Prognostic_studies Limite: Humans Idioma: En Revista: Semin Nucl Med Ano de publicação: 2023 Tipo de documento: Article