(212 days)
Cleerly LABS is a web-based software application that is intended to be used by trained medical professionals as an interactive tool for viewing and analyzing cardiac computed tomography (CT) data for determining the presence and extent of coronary plaques (i.e. atherosclerosis) and stenosis in patients who underwent Coronary Computed Tomography (CCTA) for evaluation of CAD or suspected CAD. This software post processes CT images obtained using any Computed Tomography (CT) scanner. The software provides tools for the measurement and visualization of coronary arteries.
The software is not intended to replace the skill and judgment of a qualified medical practitioner and should only be used by people who have been appropriately trained in the software's functions, capabilities and limitations. Users should be aware that certain views make use of interpolated data. This is data that is created by the software based on the original data set. Interpolated data may give the appearance of healthy tissue in situations where pathology that is near or smaller than the scanning resolution may be present.
Cleerly LABS is a post-processing web-based software application that enables trained medical professionals to analyze 2D/3D coronary images acquired from Coronary Computed Tomography Angiography (CCTA) scans. The software is a post-processing tool that aids in determining treatment paths for patients suspected of having coronary artery disease (CAD).
Cleerly LABS utilizes machine learning and simple rule-based mathematical calculation components which are performed on the backend of the software. The software applies deep learning methodology to identify high quality images, segment and label coronary arteries, and segment lumen and vessel walls. 2D and 3D images are presented to the user for review and manual editing. This segmentation is designed to improve efficiency for the user, and help shorten tedious, timeconsuming manual tasks.
The user is then able to edit the suggested segmentation as well as adjust plaque thresholds, demarcate stenosis, stents, and chronic total occlusions (CTOs) as well as select dominance and indicate coronary anomalies. Plaque, stenosis, and vessel measurements are output based on the combination of user-editable segmentation and user-placed stenosis, stent, and CTO markers. These outputs are mathematical calculations and are not machine-learning based.
Cleerly LABS provides a visualization of the Cleerly LABS analysis in the CORONARY Report. The CORONARY Report uses data previously acquired from the Cleerly LABS image analysis to generate a visually interactive and comprehensive report that details the atherosclerosis and stenosis findings of the patient. This report is not intended to be the final report (i.e., physician report) used in patient diagnosis and treatment. Cleerly Labs provides the ability to send the text report page of the CORONARY Report to the user's PACS system.
Cleerly LABS software does not perform any functions that could not be accomplished by a trained user with manual tracing methods or other commercially available software. Rather, it represents a more robust semiautomatic software intended to enhance the performance of time-intensive, potentially error-prone, manual tasks, thereby improving efficiency for medical professionals in the assessment of coronary artery disease (CAD).
The provided FDA 510(k) summary for Cleerly LABS (v2.0) indicates that no new clinical testing was conducted to demonstrate safety or effectiveness for this submission (K242338), as the non-clinical testing was deemed sufficient. The submission primarily focuses on demonstrating substantial equivalence to a previously cleared predicate device, Cleerly LABS v2.0 (K202280), primarily due to modifications in product labeling, workflow, and minor technological enhancements, without changes to the underlying algorithms or mathematical calculations.
Therefore, the document does not contain details about specific acceptance criteria, device performance, sample sizes for test sets, data provenance, expert adjudication methods, MRMC studies, standalone performance, or ground truth establishment for a new clinical study. Instead, it refers to the sufficiency of previous non-clinical testing and substantial equivalence to the predicate device.
Given this, I will extract information related to the overall performance claims and testing mentioned, emphasizing that these refer to the previous evaluation or software testing for this specific submission, rather than a new clinical study.
1. Table of Acceptance Criteria and Reported Device Performance
The document does not provide a specific table of quantitative acceptance criteria and corresponding reported device performance metrics for a new clinical study. It states that "Results of testing re-confirmed that the software requirements fulfilled the pre-defined acceptance criteria." However, these specific criteria and results are not detailed in this summary.
2. Sample Size Used for the Test Set and Data Provenance
Not applicable for a new clinical study in this submission. The document states, "No clinical testing was conducted to demonstrate safety or effectiveness as the device's non-clinical testing was sufficient to support the intended use of the device." For software evaluation, it states "multiple pre-production environments using simulated data and in production for release verification." No specific sample sizes for these internal software tests or data provenance are provided.
3. Number of Experts Used to Establish the Ground Truth for the Test Set and Qualifications of Those Experts
Not applicable for a new clinical study in this submission. Ground truth establishment for previous studies or internal validation is not detailed.
4. Adjudication Method for the Test Set
Not applicable for a new clinical study in this submission.
5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done, and the Effect Size of How Much Human Readers Improve with AI vs Without AI Assistance
No MRMC comparative effectiveness study was mentioned or performed for this submission. The device is described as "more robust semiautomatic software intended to enhance the performance of time-intensive, potentially error-prone, manual tasks, thereby improving efficiency for medical professionals." However, no specific effect size or improvement metrics are provided for human readers with or without AI assistance in this document.
6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) Was Done
The device is described as a "web-based software application that is intended to be used by trained medical professionals as an interactive tool" and "not intended to replace the skill and judgment of a qualified medical practitioner." It also mentions that "The software applies deep learning methodology to identify high quality images, segment and label coronary arteries, and segment lumen and vessel walls." While the core functions are supported by AI, the tool is semi-automatic with user review and editing capabilities. The document doesn't explicitly state if a standalone performance study (without human interaction) was performed for regulatory submission, but rather focuses on its role as an interactive tool for professionals.
7. The Type of Ground Truth Used
Not explicitly stated for the "software evaluation activities" mentioned. For the underlying algorithms (which were unchanged from the predicate), the document implies that expert review and manual editing are part of the process, suggesting expert consensus or reference standards may have been used in the original development.
8. The Sample Size for the Training Set
No information on the sample size for the training set is provided in this document.
9. How the Ground Truth for the Training Set Was Established
No information on how the ground truth for the training set was established is provided in this document.
§ 892.2050 Medical image management and processing system.
(a)
Identification. A medical image management and processing system is a device that provides one or more capabilities relating to the review and digital processing of medical images for the purposes of interpretation by a trained practitioner of disease detection, diagnosis, or patient management. The software components may provide advanced or complex image processing functions for image manipulation, enhancement, or quantification that are intended for use in the interpretation and analysis of medical images. Advanced image manipulation functions may include image segmentation, multimodality image registration, or 3D visualization. Complex quantitative functions may include semi-automated measurements or time-series measurements.(b)
Classification. Class II (special controls; voluntary standards—Digital Imaging and Communications in Medicine (DICOM) Std., Joint Photographic Experts Group (JPEG) Std., Society of Motion Picture and Television Engineers (SMPTE) Test Pattern).