Dose Optimization Using a Deep Learning Tool in Various CT Protocols for Urolithiasis: A Physical Human Phantom Study.

Shim, Jae Hun; Choi, Se Young; Chang, In Ho; Park, Sung Bin

Shim, Jae Hun; Choi, Se Young; Chang, In Ho; Park, Sung Bin.

Afiliación

Shim JH; Department of Urology, Chung-Ang University Hospital, Chung-Ang University College of Medicine, Seoul 06973, Republic of Korea.
Choi SY; Department of Urology, Chung-Ang University Hospital, Chung-Ang University College of Medicine, Seoul 06973, Republic of Korea.
Chang IH; Department of Urology, Chung-Ang University Hospital, Chung-Ang University College of Medicine, Seoul 06973, Republic of Korea.
Park SB; Department of Radiology, Chung-Ang University Hospital, Chung-Ang University College of Medicine, Seoul 06973, Republic of Korea.

Medicina (Kaunas) ; 59(9)2023 Sep 17.

Article en En | MEDLINE | ID: mdl-37763796

Asunto(s)

Aprendizaje Profundo; Urolitiasis; Humanos; Urolitiasis/diagnóstico por imagen; Procesos Mentales; Tomografía Computarizada por Rayos X

Palabras clave

deep learning; phantoms, imaging; radiation dosage; tomography, X-ray computed; urolithiasis

Texto completo

Imprimir

XML

PubMed Links

Buscar en Google

Texto completo: 1 Banco de datos: MEDLINE Asunto principal: Urolitiasis / Aprendizaje Profundo Tipo de estudio: Prognostic_studies Límite: Humans Idioma: En Revista: Medicina (Kaunas) Asunto de la revista: MEDICINA Año: 2023 Tipo del documento: Article

Texto completo

Imprimir

XML

PubMed Links

Buscar en Google