Benchmarking the diagnostic test accuracy of certified AI products for screening pulmonary tuberculosis in digital chest radiographs: Preliminary evidence from a rapid review and meta-analysis.

Hua, David; Nguyen, Khang; Petrina, Neysa; Young, Noel; Cho, Jin-Gun; Yap, Adeline; Poon, Simon K

Hua, David; Nguyen, Khang; Petrina, Neysa; Young, Noel; Cho, Jin-Gun; Yap, Adeline; Poon, Simon K.

Afiliação

Hua D; School of Computer Science, The University of Sydney, Australia; Sydney Law School, The University of Sydney, Australia.
Nguyen K; School of Computer Science, The University of Sydney, Australia.
Petrina N; School of Computer Science, The University of Sydney, Australia.
Young N; Lumus Imaging, Australia; Western Sydney Local Health District, Australia.
Cho JG; Sydney Medical School, The University of Sydney, Australia; Lumus Imaging, Australia; Western Sydney Local Health District, Australia.
Yap A; School of Computer Science, The University of Sydney, Australia.
Poon SK; School of Computer Science, The University of Sydney, Australia; Western Sydney Local Health District, Australia. Electronic address: simon.poon@sydney.edu.au.

Int J Med Inform ; 177: 105159, 2023 09.

Article em En | MEDLINE | ID: mdl-37549498

Assuntos

Benchmarking; Tuberculose Pulmonar; Humanos; Sensibilidade e Especificidade; Tuberculose Pulmonar/diagnóstico por imagem; Pulmão; Testes Diagnósticos de Rotina

Palavras-chave

Artificial intelligence; Chest X-rays; Clinical evaluation; Diagnostic test accuracy; Pulmonary tuberculosis; Screening

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Tuberculose Pulmonar / Benchmarking Tipo de estudo: Diagnostic_studies / Screening_studies / Systematic_reviews Limite: Humans Idioma: En Revista: Int J Med Inform Assunto da revista: INFORMATICA MEDICA Ano de publicação: 2023 Tipo de documento: Article País de afiliação: Austrália

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