To determine whether we could train convolutional neural network (CNN) models de novo with a small dataset, a total of 596 normal and abnormal ankle cases were collected and processed. Single- and multiview models were created to determine the effect of multiple views. Data augmentation was performed during training. The Inception V3, Resnet, and Xception convolutional neural networks were constructed utilizing the Python programming language with Tensorflow as the framework. Training was performed using single radiographic views. Measured output metrics were accuracy, positive predictive value (PPV), negative predictive value (NPV), sensitivity, and specificity. Model outputs were evaluated using both one and three radiographic views. Ensembles were created from a combination of CNNs after training. A voting method was implemented to consolidate the output from the three views and model ensemble. For single radiographic views, the ensemble of all 5 models produced the best accuracy at 76%. When all three views for a single case were utilized, the ensemble of all models resulted in the best output metrics with an accuracy of 81%. Despite our small dataset size, by utilizing an ensemble of models and 3 views for each case, we achieved an accuracy of 81%, which was in line with the accuracy of other models using a much higher number of cases with pre-trained models and models which implemented manual feature extraction.

No abstract

Medical coding and billing processes in the United States are complex, cumbersome and poorly understood by radiologists. Despite the direct implications of radiology documentation on reimbursement, trainees and practicing radiologists typically receive limited relevant training. This article summarizes the payer structure including the state-based Children's Health Insurance Programs, discusses the essential processes by which radiologists request and receive reimbursement, details the mechanisms of coding diagnoses using International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) codes and imaging services using Current Procedural Terminology (CPT) and Healthcare Common Procedure Coding System (HCPCS) codes, and explores reimbursement and coding-related issues specific to pediatric radiology. Appropriate documentation, informed by knowledge of coding, billing and reimbursement fundamentals, facilitates appropriate payment for clinically relevant services provided by pediatric radiologists.