Nikitha Murali

Department of Radiology and Biomedical Imaging

Yale School of Medicine

BIOGRAPHY

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PUBLICATIONS

Predicting Treatment Response to Intra-arterial Therapies for Hepatocellular Carcinoma with the Use of Supervised Machine Learning-An Artificial Intelligence Concept.

Journal of vascular and interventional radiology : JVIR 06, 2018 | Pubmed ID: 29548875

INSTITUTIONS

Yale School of Medicine

JOVE ARTICLES

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Aaron Abajian¹,

Nikitha Murali¹,

Lynn Jeanette Savic^1,2,

Fabian Max Laage-Gaupp¹,

Nariman Nezami¹,

James S. Duncan³,

Todd Schlachter¹,

MingDe Lin⁴,

Jean-François Geschwind⁵,

Julius Chapiro¹

¹Department of Radiology and Biomedical Imaging, Yale School of Medicine,

²Department of Diagnostic and Interventional Radiology, Universitätsmedizin Charité Berlin,

³Department of Biomedical Engineering, Yale School of Engineering and Applied Science,

⁴, Philips Research North America,

⁵, Prescience Labs

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Nikitha Murali

.css-o250i3{font-size:18px;font-family:Open Sans,sans-serif;line-height:21.6px;}Department of Radiology and Biomedical Imaging

Yale School of Medicine

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Department of Radiology and Biomedical Imaging