5 papers
A theory of ROC analysis of rule-out and rule-in diagnostics with applications to mammography data
Michelle Mastrianni, Kwok Lung Fan, Yee Lam Elim Thompson +7
Multiple diagnostic tests are frequently used to determine the presence of a disease condition in patients. In this paper, we use bivariate copulas to examine the properties of rec…
Knowledge-based anomaly detection for identifying network-induced shape artifacts
Rucha Deshpande, Tahsin Rahman, Miguel Lago +6
Synthetic data provides a promising approach to address data scarcity for training machine learning models; however, adoption without proper quality assessments may introduce artif…
A Quantitative Framework to Predict Wait-Time Impacts Due to AI-Triage Devices in a Multi-AI, Multi-Disease Workflow
Michelle Mastrianni, Rucha Deshpande, Frank W. Samuelson +1
The deployment of multiple AI-triage devices in radiology departments has grown rapidly, yet the cumulative impact on patient wait-times across different disease conditions remains…
Impact of AI-Triage on Radiologist Report Turnaround Time: Real-World Time-Savings and Insights from Model Predictions
Yee Lam Elim Thompson, Jonathan Fergus, Jonathan Chung +4
Objective: To quantify the impact of workflow parameters on time-savings in report turnaround time (TAT) due to an AI-triage device that prioritized pulmonary embolism (PE) in ches…
Use of Expected Utility (EU) to Evaluate Artificial Intelligence-Enabled Rule-Out Devices for Mammography Screening
Kwok Lung Fan, Yee Lam Elim Thompson, Weijie Chen +2
Background: An artificial intelligence (AI)-enabled rule-out device may autonomously remove patient images unlikely to have cancer from radiologist review. Many published studies e…