Letter to editor on the article "A non-invasive, automated diagnosis of Menière's disease using radiomics and machine learning on conventional magnetic resonance imaging: a multicentric, case-controlled feasibility study" by van der Lubbe Mfja et al.

Naganawa, Shinji; Ito, Rintaro; Taoka, Toshiaki; Yoshida, Tadao; Sone, Michihiko

Naganawa, Shinji; Ito, Rintaro; Taoka, Toshiaki; Yoshida, Tadao; Sone, Michihiko.

Afiliação

Naganawa S; Department of Radiology, Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Shouwa-ku, Nagoya, 466-8550, Japan. naganawa@med.nagoya-u.ac.jp.
Ito R; Department of Radiology, Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Shouwa-ku, Nagoya, 466-8550, Japan.
Taoka T; Department of Innovative Biomedical Visualization (iBMV), Nagoya University Graduate School of Medicine, Nagoya, Japan.
Yoshida T; Department of Radiology, Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Shouwa-ku, Nagoya, 466-8550, Japan.
Sone M; Department of Innovative Biomedical Visualization (iBMV), Nagoya University Graduate School of Medicine, Nagoya, Japan.

Radiol Med ; 127(4): 458-459, 2022 04.

Article em En | MEDLINE | ID: mdl-35325371

Assuntos

Doença de Meniere; Estudos de Viabilidade; Humanos; Aprendizado de Máquina; Imageamento por Ressonância Magnética; Espectroscopia de Ressonância Magnética; Doença de Meniere/diagnóstico

Palavras-chave

Endolymphatic hydrops; Machine learning; Magnetic resonance imaging; Meniere's disease

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Doença de Meniere Tipo de estudo: Clinical_trials / Diagnostic_studies Limite: Humans Idioma: En Revista: Radiol Med Ano de publicação: 2022 Tipo de documento: Article País de afiliação: Japão

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