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Artificial Intelligence Solutions for Analysis of X-ray Images.
Adams, Scott J; Henderson, Robert D E; Yi, Xin; Babyn, Paul.
Afiliação
  • Adams SJ; Department of Medical Imaging, Royal University Hospital, 7235University of Saskatchewan, Saskatoon, Canada.
  • Henderson RDE; Department of Medical Imaging, Royal University Hospital, 7235University of Saskatchewan, Saskatoon, Canada.
  • Yi X; Department of Medical Imaging, Royal University Hospital, 7235University of Saskatchewan, Saskatoon, Canada.
  • Babyn P; Department of Medical Imaging, Royal University Hospital, 7235University of Saskatchewan, Saskatoon, Canada.
Can Assoc Radiol J ; 72(1): 60-72, 2021 Feb.
Article em En | MEDLINE | ID: mdl-32757950
Artificial intelligence (AI) presents a key opportunity for radiologists to improve quality of care and enhance the value of radiology in patient care and population health. The potential opportunity of AI to aid in triage and interpretation of conventional radiographs (X-ray images) is particularly significant, as radiographs are the most common imaging examinations performed in most radiology departments. Substantial progress has been made in the past few years in the development of AI algorithms for analysis of chest and musculoskeletal (MSK) radiographs, with deep learning now the dominant approach for image analysis. Large public and proprietary image data sets have been compiled and have aided the development of AI algorithms for analysis of radiographs, many of which demonstrate accuracy equivalent to radiologists for specific, focused tasks. This article describes (1) the basis for the development of AI solutions for radiograph analysis, (2) current AI solutions to aid in the triage and interpretation of chest radiographs and MSK radiographs, (3) opportunities for AI to aid in noninterpretive tasks related to radiographs, and (4) considerations for radiology practices selecting AI solutions for radiograph analysis and integrating them into existing IT systems. Although comprehensive AI solutions across modalities have yet to be developed, institutions can begin to select and integrate focused solutions which increase efficiency, increase quality and patient safety, and add value for their patients.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Inteligência Artificial / Radiografia / Radiografia Torácica / Interpretação de Imagem Radiográfica Assistida por Computador / Doenças Musculoesqueléticas / Pneumopatias Tipo de estudo: Diagnostic_studies Limite: Humans Idioma: En Revista: Can Assoc Radiol J Assunto da revista: RADIOLOGIA Ano de publicação: 2021 Tipo de documento: Article País de afiliação: Canadá

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Inteligência Artificial / Radiografia / Radiografia Torácica / Interpretação de Imagem Radiográfica Assistida por Computador / Doenças Musculoesqueléticas / Pneumopatias Tipo de estudo: Diagnostic_studies Limite: Humans Idioma: En Revista: Can Assoc Radiol J Assunto da revista: RADIOLOGIA Ano de publicação: 2021 Tipo de documento: Article País de afiliação: Canadá