(127 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.
QLAB 8.1 is a software application that is available cither as a stand-alone product that can function on a standard PC, a dedicated workstation, and on-board Philips' ultrasound systems. It can be used for the on-line and off-line review and quantification of ultrasound studies.
QLAB 8.1 introduces a new plug-in into the stand-alone version of the product: Elastography Analysis (EA).
Elastography Analysis (EA) displays 2D images and clastograms acquired with the elastography imaging mode feature on an ultrasound system. EA provides a tool for comparing the size of a lesion on the 2D image and on the elastogram and enables the user to determine the ratio of two regions of interest (ROI), referred to as Size Compare.
This document describes the QLAB 8.1 Quantification Software with an additional EA plug-in.
1. Table of Acceptance Criteria and Reported Device Performance
The provided document (K110414) is a 510(k) premarket notification for a Class II medical device. It primarily demonstrates substantial equivalence to a predicate device rather than providing a detailed performance study with specific acceptance criteria and outcome metrics for a novel technology.
The document states:
- "No performance standards for PACS systems or components have been issued under the authority of Section 514."
- "Software development for the QLAB software follows documented processes for software design, verification and validation testing."
- "A risk assessment has been completed to identify potential design hazards that could cause an error or injury based on the use of the quantification results. Appropriate steps have been taken to control all identified risks for this type of image display and quantification product."
Given the nature of a 510(k) submission for a post-processing software that views and quantifies existing image data, the "acceptance criteria" are typically related to the software's functional correctness, accuracy of measurements compared to ground truth or predicate devices, robustness, and usability. However, specific quantitative acceptance criteria (e.g., accuracy thresholds, precision targets) and detailed performance results based on a clinical trial are not explicitly provided in the excerpt.
The reported device performance is implicitly stated by asserting substantial equivalence to the Size Compare feature on the Philips iU22 Ultrasound System (K093563). This implies that the EA plug-in's "Size Compare" functionality for comparing lesion sizes on 2D images and elastograms performs similarly to the predicate device.
Example of how a table might look if specific acceptance criteria were provided (hypothetical, as not explicitly stated in the document):
| Performance Metric (Hypothetical) | Acceptance Criteria (Hypothetical) | Reported Device Performance (Implied) |
|---|---|---|
| Measurement Accuracy of ROI (Size Compare) | ≤ 5% deviation from reference measurements on a phantom/dataset | Substantially equivalent to predicate device (Philips iU22 Ultrasound System's Size Compare feature, K093563). |
| Software Functionality | All specified features (e.g., 2D image display, elastogram display, ROI selection, ratio calculation) operate as intended without critical errors. | Verified through documented software design, verification, and validation testing. |
| Software Robustness | Handles various image formats and user inputs without crashing or producing incorrect outputs. | Verified through documented software design, verification, and validation testing. |
2. Sample Size Used for the Test Set and Data Provenance
The document does not explicitly state the sample size used for any specific test set related to the performance of the QLAB 8.1 EA plug-in, nor does it specify the data provenance (e.g., country of origin, retrospective or prospective). The 510(k) focuses on substantial equivalence through comparison of technological characteristics and intended use, and internal software verification/validation rather than a separate clinical performance study with a defined test set.
3. Number of Experts Used to Establish Ground Truth for the Test Set and Their Qualifications
The document does not provide details on the establishment of ground truth for a test set, nor does it mention the number or qualifications of experts involved. This information would typically be found in a clinical study report, which is not part of this 510(k) summary. Given the device's function as a quantification software, ground truth for measurements might involve phantom studies, established geometric measurements, or expert consensus on clinical images, but these specifics are absent.
4. Adjudication Method for the Test Set
Since a specific test set with expert-established ground truth is not detailed in the document, an adjudication method (such as 2+1, 3+1, or none) for such a test set is not described.
5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study
The document does not describe a Multi-Reader Multi-Case (MRMC) comparative effectiveness study. The submission focuses on the software's standalone functionality and its substantial equivalence to a predicate device, not on assessing how human readers improve with AI assistance. Therefore, no effect size for human reader improvement with AI assistance is reported.
6. Standalone (Algorithm Only Without Human-in-the-Loop) Performance
Yes, the QLAB 8.1 Quantification Software (including the EA plug-in) is primarily described as a standalone product. It is a "software application package" designed to "view and quantify image data" and "can be used for the on-line and off-line review and quantification of ultrasound studies." The EA plug-in "enables the user to determine the ratio of two regions of interest (ROI), referred to as Size Compare." This implies a standalone algorithm providing measurements, which a user then interprets or utilizes. The performance assessment, as implicitly described, is of the software's ability to provide these measurements accurately.
7. Type of Ground Truth Used
The specific type of ground truth used for verifying the QLAB 8.1 EA plug-in is not explicitly stated in the document. For quantification software, ground truth could involve:
- Phantom measurements: Using objects of known dimensions imaged by ultrasound.
- Manual measurements by experts: A consensus or single expert measurement on images, cross-referenced with other modalities like pathology or MRI if available for the specific application.
- Pathology: For lesion characterization, but the document primarily discusses size comparison.
The excerpt only states that "Software development for the QLAB software follows documented processes for software design, verification and validation testing," which would involve some form of ground truth or reference data.
8. Sample Size for the Training Set
The document does not provide information about a specific training set or its sample size. This is common for predicate-based 510(k) submissions, especially for software performing quantification rather than complex AI inference requiring large training datasets. If the software uses machine learning techniques, the training data details would typically be included, but given the 2011 submission date and description, it appears to be a rule-based or conventional image processing application.
9. How Ground Truth for the Training Set Was Established
Since no specific training set is mentioned, the method for establishing its ground truth is also not described 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).