Applying queueing theory to evaluate wait-time-savings of triage algorithms.

In the past decade, artificial intelligence (AI) algorithms have made promising impacts in many areas of healthcare. One application is AI-enabled prioritization software known as computer-aided triage and notification (CADt). This type of software as a medical device is intended to prioritize reviews of radiological images with time-sensitive findings, thus shortening the waiting time for patients with these findings.

Out-of-Distribution Detection and Radiological Data Monitoring Using Statistical Process Control.

Machine learning (ML) models often fail with data that deviates from their training distribution. This is a significant concern for ML-enabled devices as data drift may lead to unexpected performance. This work introduces a new framework for out of distribution (OOD) detection and data drift monitoring that combines ML and geometric methods with statistical process control (SPC).

Evaluating Machine Learning-Based MRI Reconstruction Using Digital Image Quality Phantoms.

Quantitative and objective evaluation tools are essential for assessing the performance of machine learning (ML)-based magnetic resonance imaging (MRI) reconstruction methods. However, the commonly used fidelity metrics, such as mean squared error (MSE), structural similarity (SSIM), and peak signal-to-noise ratio (PSNR), often fail to capture fundamental and clinically relevant MR image quality aspects. To address this, we propose evaluation of ML-based MRI reconstruction using digital image quality phantoms and automated evaluation methods.

Improving the efficiency and accuracy of cardiovascular magnetic resonance with artificial intelligence-review of evidence and proposition of a roadmap to clinical translation.

Background: Cardiovascular magnetic resonance (CMR) is an important imaging modality for the assessment of heart disease; however, limitations of CMR include long exam times and high complexity compared to other cardiac imaging modalities. Recently advancements in artificial intelligence (AI) technology have shown great potential to address many CMR limitations. While the developments are remarkable, translation of AI-based methods into real-world CMR clinical practice remains at a nascent stage and much work lies ahead to realize the full potential of AI for CMR.

An FDA Guide on Indications for Use and Device Reporting of Artificial Intelligence-Enabled Devices: Significance for Pediatric Use.

Radiology has been a pioneer in adopting artificial intelligence (AI)-enabled devices into the clinic. However, initial clinical experience has identified concerns of inconsistent device performance across different patient populations. Medical devices, including those using AI, are cleared by the FDA for their specific indications for use (IFUs).

Regulatory considerations for medical imaging AI/ML devices in the United States: concepts and challenges.

Purpose: To introduce developers to medical device regulatory processes and data considerations in artificial intelligence and machine learning (AI/ML) device submissions and to discuss ongoing AI/ML-related regulatory challenges and activities.

Approach: AI/ML technologies are being used in an increasing number of medical imaging devices, and the fast evolution of these technologies presents novel regulatory challenges. We provide AI/ML developers with an introduction to U.

Real-World Performance of Large Vessel Occlusion Artificial Intelligence-Based Computer-Aided Triage and Notification Algorithms-What the Stroke Team Needs to Know.

Purpose: To evaluate the real-world performance of two FDA-approved artificial intelligence (AI)-based computer-aided triage and notification (CADt) detection devices and compare them with the manufacturer-reported performance testing in the instructions for use.

Materials And Methods: Clinical performance of two FDA-cleared CADt large-vessel occlusion (LVO) devices was retrospectively evaluated at two separate stroke centers. Consecutive "code stroke" CT angiography examinations were included and assessed for patient demographics, scanner manufacturer, presence or absence of CADt result, CADt result, and LVO in the internal carotid artery (ICA), horizontal middle cerebral artery (MCA) segment (M1), Sylvian MCA segments after the bifurcation (M2), precommunicating part of cerebral artery, postcommunicating part of the cerebral artery, vertebral artery, basilar artery vessel segments.

US regulatory considerations for low field magnetic resonance imaging systems.

Although there has been a resurgence of interest in low field magnetic resonance imaging (MRI) systems in recent years, low field MRI is not a new concept. FDA has a long history of evaluating the safety and effectiveness of MRI systems encompassing a wide range of field strengths. Many systems seeking marketing authorization today include new technological features (such as artificial intelligence), but this does not fundamentally change the regulatory paradigm for MR systems.

Multiparametric Quantitative Imaging Biomarker as a Multivariate Descriptor of Health: A Roadmap.

Multiparametric quantitative imaging biomarkers (QIBs) offer distinct advantages over single, univariate descriptors because they provide a more complete measure of complex, multidimensional biological systems. In disease, where structural and functional disturbances occur across a multitude of subsystems, multivariate QIBs are needed to measure the extent of system malfunction. This paper, the first Use Case in a series of articles on multiparameter imaging biomarkers, considers multiple QIBs as a multidimensional vector to represent all relevant disease constructs more completely.

Multiparametric Data-driven Imaging Markers: Guidelines for Development, Application and Reporting of Model Outputs in Radiomics.

This paper is the fifth in a five-part series on statistical methodology for performance assessment of multi-parametric quantitative imaging biomarkers (mpQIBs) for radiomic analysis. Radiomics is the process of extracting visually imperceptible features from radiographic medical images using data-driven algorithms. We refer to the radiomic features as data-driven imaging markers (DIMs), which are quantitative measures discovered under a data-driven framework from images beyond visual recognition but evident as patterns of disease processes irrespective of whether or not ground truth exists for the true value of the DIM.

Multiparametric Quantitative Imaging in Risk Prediction: Recommendations for Data Acquisition, Technical Performance Assessment, and Model Development and Validation.

Combinations of multiple quantitative imaging biomarkers (QIBs) are often able to predict the likelihood of an event of interest such as death or disease recurrence more effectively than single imaging measurements can alone. The development of such multiparametric quantitative imaging and evaluation of its fitness of use differs from the analogous processes for individual QIBs in several key aspects. A computational procedure to combine the QIB values into a model output must be specified.

Multiparametric Quantitative Imaging Biomarkers for Phenotype Classification: A Framework for Development and Validation.

This manuscript is the third in a five-part series related to statistical assessment methodology for technical performance of multi-parametric quantitative imaging biomarkers (mp-QIBs). We outline approaches and statistical methodologies for developing and evaluating a phenotype classification model from a set of multiparametric QIBs. We then describe validation studies of the classifier for precision, diagnostic accuracy, and interchangeability with a comparator classifier.

A Framework for Evaluating the Technical Performance of Multiparameter Quantitative Imaging Biomarkers (mp-QIBs).

Multiparameter quantitative imaging incorporates anatomical, functional, and/or behavioral biomarkers to characterize tissue, detect disease, identify phenotypes, define longitudinal change, or predict outcome. Multiple imaging parameters are sometimes considered separately but ideally are evaluated collectively. Often, they are transformed as Likert interpretations, ignoring the correlations of quantitative properties that may result in better reproducibility or outcome prediction.

Challenges in ensuring the generalizability of image quantitation methods for MRI.

Image quantitation methods including quantitative MRI, multiparametric MRI, and radiomics offer great promise for clinical use. However, many of these methods have limited clinical adoption, in part due to issues of generalizability, that is, the ability to translate methods and models across institutions. Researchers can assess generalizability through measurement of repeatability and reproducibility, thus quantifying different aspects of measurement variance.

MRI-related FDA adverse event reports: A 10-yr review.

Purpose: To provide an overview of the types of adverse events reported to the US Food and Drug Administration (US FDA) for magnetic resonance (MR) systems over a 10-yr period.

Methods: Two reviewers independently reviewed adverse events reported to FDA for MR systems from 1 January 2008 to 31 December 2017 and manually categorized events into eight event types. Thermal events were further subcategorized by probable cause.

Recommendations towards standards for quantitative MRI (qMRI) and outstanding needs.

5 Technical Efficacy: Stage 5 J. Magn. Reson.

Response to Standardized MR Terminology and Reporting of Implants and Devices as Recommended by the American College of Radiology Subcommittee on MR Safety.

The U.S. Food and Drug Administration (FDA) continually works toward the goal of safety.

Method to create regional mechanical dyssynchrony maps from short-axis cine steady-state free-precession images.

Purpose: To develop a robust method to assess regional mechanical dyssynchrony from cine short-axis MR images. Cardiac resynchronization therapy (CRT) is an effective treatment for patients with heart failure and evidence of left-ventricular (LV) dyssynchrony. Patient response to CRT is greatest when the LV pacing lead is placed in the most dyssynchronous segment.

Cardiovascular magnetic resonance at 3.0 T: current state of the art.

There are advantages to conducting cardiovascular magnetic resonance (CMR) studies at a field strength of 3.0 Telsa, including the increase in bulk magnetization, the increase in frequency separation of off-resonance spins, and the increase in T1 of many tissues. However, there are significant challenges to routinely performing CMR at 3.

Role of MRI in patient selection for CRT.

Magnetic resonance imaging has great potential for aiding in the selection of patients who will respond to CRT. MRI is the only imaging tool that can simultaneously assess mechanical dyssynchrony, determine the amount and location of myocardial scar tissue, and map the location of cardiac venous anatomy-three important factors in predicting a patient's response to CRT. The goal of this manuscript is to review the MRI methods that can be used in the selection of patients for CRT.

Cross-correlation delay to quantify myocardial dyssynchrony from phase contrast magnetic resonance (PCMR) velocity data.

Purpose: To apply cross-correlation delay (XCD) analysis to myocardial phase contrast magnetic resonance (PCMR) tissue velocity data and to compare XCD to three established "time-to-peak" dyssynchrony parameters.

Materials And Methods: Myocardial tissue velocity was acquired using PCMR in 10 healthy volunteers (negative controls) and 10 heart failure patients who met criteria for cardiac resynchronization therapy (positive controls). All dyssynchrony parameters were computed from PCMR velocity curves.