Purpose To assess the generalizability of a deep learning pneumothorax detection model
on datasets from multiple external institutions and examine patient and acquisition factors …

Rationale and Objectives To compare the performance of pneumothorax deep learning
detection models trained with radiologist versus natural language processing (NLP) labels …

Purpose Prompt diagnosis and quantitation of pneumothorax impact decisions pertaining to
patient management. The purpose of our study was to develop and evaluate the accuracy of …

Background Pneumothorax can precipitate a life-threatening emergency due to lung
collapse and respiratory or circulatory distress. Pneumothorax is typically detected on chest …

Background Automatically detecting and quantifying pneumothorax on chest computed
tomography (CT) may impact clinical decision-making. Machine learning methods published …

Purpose To validate a machine learning model trained on an open source dataset and
subsequently optimize it to chest X-rays with large pneumothoraces from our institution …

Background Accurate detection of pneumothorax on chest radiographs, the most common
complication of percutaneous transthoracic needle biopsies (PTNBs), is not always easy in …

Pneumothorax is a relatively common disease, but in some cases, it may be difficult to find
with chest radiography. In this paper, we propose a novel method of detecting …

Objectives To retrospectively evaluate the diagnostic performance of a convolutional neural
network (CNN) model in detecting pneumothorax on chest radiographs obtained after …

Background Deep learning (DL), a subset of artificial intelligence (AI), has been applied to
pneumothorax diagnosis to aid physician diagnosis, but no meta-analysis has been …