(11 days)
QLAB Quantification software is a software application package. It is designed to view and quantify image data acquired on Philips Medical Systems ultrasound products.
The Parametric Imaging feature is an addition to the existing Cardiac 3DQ Advanced Plug-in already described on the iE33 510(k) submission K042540. The QLAB 5.0 3DQA plug-in Parametric Imaging provides the user with easy-to-use color-coded representations of regional left-ventricular (LV) segmental Timing and Excursion parameters displayed on the standard AHA/ASE 17-segment Bull's-eye display. The Parametric display may be used in assisting the clinician to visualize directly LV regional function in a user-friendly format.
This document does not contain information about acceptance criteria or a study proving device performance against such criteria. It is a 510(k) premarket notification for a software device, and primarily focuses on administrative details, device description, and a claim of substantial equivalence to a predicate device.
Therefore, I cannot populate the table or answer the specific questions regarding acceptance criteria and performance study details. The provided text explicitly states there are "No performance standards for PACS systems or components have been issued under the authority of Section 514" for this device category.
Here's how I would respond if such information were present:
Table of Acceptance Criteria and Reported Device Performance:
Not available in the provided text.
Sample sized used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective):
Not available in the provided text.
Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g. radiologist with 10 years of experience):
Not available in the provided text.
Adjudication method (e.g. 2+1, 3+1, none) for the test set:
Not available in the provided text.
If a multi reader multi case (MRMC) comparative effectiveness study was done, If so, what was the effect size of how much human readers improve with AI vs without AI assistance:
Not available in the provided text.
If a standalone (i.e. algorithm only without human-in-the-loop performance) was done:
Not available in the provided text.
The type of ground truth used (expert concensus, pathology, outcomes data, etc):
Not available in the provided text.
The sample size for the training set:
Not available in the provided text.
How the ground truth for the training set was established:
Not available in the provided text.
§ 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).