Formación en Radiodiagnóstico Archives

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Formación en Radiodiagnóstico

Bone Age Assessment Using Artificial Intelligence in Korean Pediatric Population: A Comparison of Deep-Learning Models Trained With Healthy Chronological and Greulich-Pyle Ages as Labels

Korean J Radiol. 2023 Nov;24(11):1151-1163.

Kim PH, Yoon HM, Kim JR, Hwang JY, Choi JH, Hwang J, Lee J, Sung J, Jung KH, Bae B, Jung AY, Cho YA, Shim WH, Bak B, Lee JS. Bone Age

Towards AI-augmented radiology education: a web-based application for perception training in chest X-ray nodule detection

Br J Radiol (2023) 10.1259/bjr.20230299

Borgbjerg J, Thompson JD, Salte IM, Frøkjær JB.

Dynamic chest radiography for pulmonary vascular diseases: Clinical applications and correlation with other imaging modalities

Jpn J Radiol 42, 126–144 (2024)

Yamasaki, Y., Kamitani, T., Sagiyama, K. et al.

How to prepare for a bright future of radiology in Europe.

Insights into Imaging

Minerva Becker

MRI-based artificial intelligence to predict infection following total hip arthroplasty failure.

La radiologia medica

Albano D, Gitto S, Messina C, Serpi F, Salvatore C, Castiglioni I, Zagra L, De Vecchi E, Sconfienza LM.

Comités

Áreas de conocimiento

Radiología Pediátrica

Radiología Musculoesquelética

Radiología Intervencionista

Radiología de Urgencias

Radiología de la Mama

Radiología de Abdomen

Radiología Cardiotorácica

Neurorradiología

Gestión y Calidad en Radiodiagnóstico

Formación en Radiodiagnóstico

Área de Ultrasonidos

Sociedades participantes

SEUS

SERVEI

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SERME

SERAU

SENR

SEICAT

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