arXiv subjects

Yee Lam Elim Thompson

Publications and source records attributed to Yee Lam Elim Thompson.

5 recordsLinked to original sources

arXiv2026

A theory of ROC analysis of rule-out and rule-in diagnostics with applications to mammography data

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 receiver operating characteristic (ROC) curves formed when two correlated diagnostic tests are used together to rule-out ("believe the negative") and rule-in ("believe the positive") patients for disease. We use this theory to analyze three mammography data sets where AI devices are applied to reduce radiologists' workload or improve diagnostic performance. Our analysis shows with generality that increasing the radiologist-AI correlation for diseased cases enhances the area under the ROC curve (AUC) of a radiologist-AI rule-out curve, whereas decreasing correlation for non-diseased cases has a similar effect. The opposite trends hold for rule-in scenarios. Applications to clinical mammography data show that projected empirical radiologist performance under a rule-out or rule-in scenario is consistent with the theory.

stat.ME↗

arXiv2025

A Quantitative Framework to Predict Wait-Time Impacts Due to AI-Triage Devices in a Multi-AI, Multi-Disease Workflow

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 poorly understood. This research develops a comprehensive mathematical and simulation framework to quantify wait-time trade-offs when multiple AI-triage devices operate simultaneously in a clinical workflow. We created multi-QuCAD, a software tool that models complex multi-AI, multi-disease scenarios using queueing theory principles, incorporating realistic clinical parameters including disease prevalence rates, radiologist reading times, and AI performance characteristics from FDA-cleared devices. The framework was verified through four experimental scenarios ranging from simple two-disease workflows to complex nine-disease systems, comparing preemptive versus non-preemptive scheduling disciplines and priority versus hierarchical triage protocols. Analysis of brain imaging workflows demonstrated that while AI-triage devices significantly reduce wait-times for target conditions, they can substantially delay diagnosis of non-targeted, yet urgent conditions. The study revealed that hierarchical protocol generally provides more wait-time savings for the highest-priority conditions compared to the priority protocol, though at the expense of more delays to lower-priority patients with other time-sensitive conditions. The quantitative framework presented provides essential insights for orchestrating multi-AI deployments to maximize overall patient time-saving benefits while minimizing unintended delay for other important patient populations.

math.NA↗

arXiv2025

Impact of AI-Triage on Radiologist Report Turnaround Time: Real-World Time-Savings and Insights from Model Predictions

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 chest CT pulmonary angiography (CTPA) exams. Methods: This retrospective study analyzed 11252 adult CTPA exams conducted for suspected PE at a single tertiary academic medical center. Data was divided into two periods: pre-AI and post-AI. For PE-positive exams, TAT - defined as the duration from patient scan completion to the first preliminary report completion - was compared between the two periods. Time-savings were reported separately for work-hour and off-hour cohorts. To characterize radiologist workflow, 527234 records were retrieved from the PACS and workflow parameters such as exam inter-arrival time and radiologist read-time extracted. These parameters were input into a computational model to predict time-savings following deployment of an AI-triage device and to study the impact of workflow parameters. Results: The pre-AI dataset included 4694 chest CTPA exams with 13.3% being PE-positive. The post-AI dataset comprised 6558 exams with 16.2% being PE-positive. The mean TAT for pre-AI and post-AI during work hours are 68.9 [95% CI" 55.0, 82.8] and 46.7 [38.1, 55.2] minutes respectively, and those during off-hours are 44.8 [33.7, 55.9] and 42.0 [33.6, 50.3] minutes. Clinically-observed time-savings during work hours (22.2 [95% CI: 5.85, 38.6] minutes) were significant (p=0.004), while off-hour (2.82 [-11.1, 16.7] minutes) were not (p=0.345). Observed time-savings aligned with model predictions (29.6 [95% range: 23.2, 38.1] minutes for work hours; 2.10 [1.76, 2.58] minutes for off-hours). Discussion: Consideration and quantification of clinical workflow contribute to an accurate assessment of the expected time-savings in TAT following deployment of an AI-triage device.

cs.PF↗

arXiv2024

Use of Expected Utility (EU) to Evaluate Artificial Intelligence-Enabled Rule-Out Devices for Mammography Screening

Background: An artificial intelligence (AI)-enabled rule-out device may autonomously remove patient images unlikely to have cancer from radiologist review. Many published studies evaluate this type of device by retrospectively applying the AI to large datasets and use sensitivity and specificity as the performance metrics. However, these metrics have fundamental shortcomings because they are bound to have opposite changes with the rule-out application of AI. Method: We reviewed two performance metrics to compare the screening performance between the radiologist-with-rule-out-device and radiologist-without-device workflows: positive/negative predictive values (PPV/NPV) and expected utility (EU). We applied both methods to a recent study that reported improved performance in the radiologist-with-device workflow using a retrospective U.S. dataset. We then applied the EU method to a European study based on the reported recall and cancer detection rates at different AI thresholds to compare the potential utility among different thresholds. Results: For the U.S. study, neither PPV/NPV nor EU can demonstrate significant improvement for any of the algorithm thresholds reported. For the study using European data, we found that EU is lower as AI rules out more patients including false-negative cases and reduces the overall screening performance. Conclusions: Due to the nature of the retrospective simulated study design, sensitivity and specificity can be ambiguous in evaluating a rule-out device. We showed that using PPV/NPV or EU can resolve the ambiguity. The EU method can be applied with only recall rates and cancer detection rates, which is convenient as ground truth is often unavailable for non-recalled patients in screening mammography.

stat.ME↗

arXiv2023

Evaluation of wait time saving effectiveness of triage algorithms

In the past decade, Artificial Intelligence (AI) algorithms have made promising impacts to transform healthcare in all aspects. One application is to triage patients' radiological medical images based on the algorithm's binary outputs. Such AI-based prioritization software is known as computer-aided triage and notification (CADt). Their main benefit is to speed up radiological review of images with time-sensitive findings. However, as CADt devices become more common in clinical workflows, there is still a lack of quantitative methods to evaluate a device's effectiveness in saving patients' waiting times. In this paper, we present a mathematical framework based on queueing theory to calculate the average waiting time per patient image before and after a CADt device is used. We study four workflow models with multiple radiologists (servers) and priority classes for a range of AI diagnostic performance, radiologist's reading rates, and patient image (customer) arrival rates. Due to model complexity, an approximation method known as the Recursive Dimensionality Reduction technique is applied. We define a performance metric to measure the device's time-saving effectiveness. A software tool is developed to simulate clinical workflow of image review/interpretation, to verify theoretical results, and to provide confidence intervals of the performance metric we defined. It is shown quantitatively that a triage device is more effective in a busy, short-staffed setting, which is consistent with our clinical intuition and simulation results. Although this work is motivated by the need for evaluating CADt devices, the framework we present in this paper can be applied to any algorithm that prioritizes customers based on its binary outputs.

stat.AP↗