Ankle Fracture Detection Utilizing a Convolutional Neural Network Ensemble Implemented with a Small Sample, De Novo Training, and Multiview Incorporation.

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.

From imaging to reimbursement: what the pediatric radiologist needs to know about health care payers, documentation, coding and billing.

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.

Quantitative diffusion-weighted magnetic resonance imaging assessment of chemotherapy treatment response of pediatric osteosarcoma and Ewing sarcoma malignant bone tumors.

OBJECTIVE: Assessment of tumor response to chemotherapy is essential in managing malignant pediatric bone tumors prior to resection. SUBJECTS AND METHODS: Pre-chemotherapy and post-chemotherapy osteosarcoma and Ewing sarcoma cases (n=18) were analyzed with apparent diffusion coefficient (ADC) values measured by two readers. RESULTS: Treated tumors demonstrated significantly greater ADC values compared to untreated tumors (p<0.001). Intraclass correlation coefficients ranged between 0.858 and 0.935. No significant tumor volume differences were observed. Regression analysis demonstrated average ADC as the best predictor of treatment. CONCLUSIONS: Our study suggests that ADC values may be useful for evaluating chemotherapeutic response of malignant pediatric bone tumors.

Hyperdense endobronchial mass - a rare manifestation of metastatic melanoma.

Parenchymal lung metastases are common in malignant melanoma. However, endobronchial metastatic melanoma is uncommon. In this report, we describe a patient with a hyperdense endobronchial mass diagnosed as a melanoma metastasis. We review the imaging findings of pulmonary metastatic melanoma and discuss the differential diagnosis of an endobronchial mass in adults.