Machine learning for the prediction of pathologic pneumatosis intestinalis.

Background: The radiographic finding of pneumatosis intestinalis can indicate a spectrum of underlying processes ranging from a benign finding to a life-threatening condition. Although radiographic pneumatosis intestinalis is relatively common, there is no validated clinical tool to guide surgical management.

Methods: Using a retrospective cohort of 300 pneumatosis intestinalis cases from a single institution, we developed 3 machine learning models for 2 clinical tasks: (1) the distinction of benign from pathologic pneumatosis intestinalis cases and (2) the determination of patients who would benefit from an operation.

Development and validation of a five-factor score for prediction of pathologic pneumatosis.

Background: The significance of pneumatosis intestinalis (PI) remains challenging. While certain clinical scenarios are predictive of transmural ischemia, risk models to assess the presence of pathologic PI are needed. The aim of this study was to determine what patient factors at the time of radiographic diagnosis of PI predict the risk for pathologic PI.

Deep Learning Pre-training Strategy for Mammogram Image Classification: an Evaluation Study.

In this work, we assess how pre-training strategy affects deep learning performance for the task of distinguishing false-recall from malignancy and normal (benign) findings in digital mammography images. A cohort of 1303 breast cancer screening patients (4935 digital mammogram images in total) was retrospectively analyzed as the target dataset for this study. We assessed six different convolutional neural network model structures utilizing four different imaging datasets (total > 1.