• Steven J Esses, Xiaoguang Lu, Tiejun Zhao, Krishna Shanbhogue, Bari Dane, Mary Bruno, and Hersh Chandarana.
• Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York, USA.
• J Magn Reson Imaging. 2018 Mar 1; 47 (3): 723-728.

PurposeTo develop and test a deep learning approach named Convolutional Neural Network (CNN) for automated screening of T2 -weighted (T2 WI) liver acquisitions for nondiagnostic images, and compare this automated approach to evaluation by two radiologists.Materials And MethodsWe evaluated 522 liver magnetic resonance imaging (MRI) exams performed at 1.5T and 3T at our institution between November 2014 and May 2016 for CNN training and validation. The CNN consisted of an input layer, convolutional layer, fully connected layer, and output layer. 351 T2 WI were anonymized for training. Each case was annotated with a label of being diagnostic or nondiagnostic for detecting lesions and assessing liver morphology. Another independently collected 171 cases were sequestered for a blind test. These 171 T2 WI were assessed independently by two radiologists and annotated as being diagnostic or nondiagnostic. These 171 T2 WI were presented to the CNN algorithm and image quality (IQ) output of the algorithm was compared to that of two radiologists.ResultsThere was concordance in IQ label between Reader 1 and CNN in 79% of cases and between Reader 2 and CNN in 73%. The sensitivity and the specificity of the CNN algorithm in identifying nondiagnostic IQ was 67% and 81% with respect to Reader 1 and 47% and 80% with respect to Reader 2. The negative predictive value of the algorithm for identifying nondiagnostic IQ was 94% and 86% (relative to Readers 1 and 2).ConclusionWe demonstrate a CNN algorithm that yields a high negative predictive value when screening for nondiagnostic T2 WI of the liver.Level Of Evidence2 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018;47:723-728.© 2017 International Society for Magnetic Resonance in Medicine.

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