vRad Presents at RSNA on Artificial Intelligence Algorithm that Detects Pneumoperitoneum on CT Scans

MINNEAPOLIS, Minn., December 1, 2020 – vRad (Virtual Radiologic), announced that Robert J. Harris, Ph.D., vRad Data Scientist / Machine Learning Engineer, will make a scientific presentation, “Automated Segmentation and Worklist Prioritization of Free Air in CT Images Using a Convolutional Neural Network,” at the Radiological Society of North America Annual Meeting on December 2. The full session details can be found here.

The presentation highlights how vRad engineers and radiologists developed an artificial intelligence algorithm that accurately detects pneumoperitoneum on abdominal CT scans. vRad uses this algorithm in real-time on incoming CT scans to detect this potentially critical condition, saving patients valuable time between scanning and treatment.

Pneumoperitoneum, the presence of free gas in the peritoneal cavity, can be a sign of critical pathology such as bowel perforation or trauma. Pneumoperitoneum is often diagnosed with abdominal CT and early detection is important to a patient’s outcome. Our institution processes approximately 3,300 abdominal CT studies per day, of which 1.3% are positive for pneumoperitoneum. We hypothesized that a convolutional neural network could be trained to detect pneumoperitoneum in prospective patients in order to expedite patient care. Natural language processing (NLP) of radiology CT reports was used retrospectively to identify body CT studies containing pneumoperitoneum and other types of free air that were annotated by a Board Certified radiologist to train a convolutional neural network.

This model was then integrated with our teleradiology pipeline to screen prospective patients for free air n real-time, with NLP of the subsequent radiology report used as ground truth. The model achieved an AUC of 0.906 on a test dataset. Over a two-week period, for prospective patients, the model had a sensitivity of 45.8% and a specificity of 93.8% for pneumoperitoneum and similar statistics for other types of free air. To our knowledge, this is the first use of machine learning to identify pneumoperitoneum on CT images and perform worklist prioritization for patients based on its presence.