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510(k) Data Aggregation

    K Number
    K120525
    Date Cleared
    2012-03-09

    (16 days)

    Product Code
    Regulation Number
    892.2050
    Reference & Predicate Devices
    Why did this record match?
    Reference Devices :

    K110414

    AI/MLSaMDIVD (In Vitro Diagnostic)TherapeuticDiagnosticis PCCP AuthorizedThirdpartyExpeditedreview
    Intended Use

    QLAB Quantification Software is a software application package. It is designed to view and quantify image data acquired on Philips Medical Systems ultrasound products.

    Device Description

    OLAB Quantification software is available either as a stand-alone product that can function on a standard.PC, on board a dedicated workstation, or on-board Philips' ultrasound systems. It can be used by trained healthcare professionals for the on-line and off-line review and quantification of ultrasound studies in healthcare facilities/hospitals.

    The QLAB Quantification software application package is designed to view and quantify image data acquired on Philips ultrasound products. Cardiac Motion Quantification (CMQ) is a plug-in included in Philips QLAB Quantification software.

    The CMO plug-in is an application within OLAB intended to provide cardiac motion quantification. OLAB Quantification software is intended for use in healthcare facilities/hospitals by trained healthcare professionals.

    OLAB CMO modifications were implemented to provide clients with improved reproducibility and consistency between users, as well as to provide users with a reduction of workflow steps. The modifications described in this Special 510(k) submission do not alter the intended use of the OLAB Quantification software with the CMO plug-in.

    AI/ML Overview

    Here's a breakdown of the acceptance criteria and study information for the QLAB CMQ Plug-in Modifications, based on the provided text:

    1. Table of Acceptance Criteria and Reported Device Performance

    Acceptance Criteria (Modified CMQ vs. Unmodified CMQ)Reported Device Performance
    Improved workflow: fewer mouse clicks for typical assessmentVerification and validation testing concluded the modification achieved this.
    Improved workflow: decreased average time for typical assessmentVerification and validation testing concluded the modification achieved this.
    Decreased intra-observer variability of assessmentsVerification and validation testing concluded the modification achieved this.
    Decreased inter-observer variability of assessmentsVerification and validation testing concluded the modification achieved this.
    Safe and effective release, no new risks introducedVerification and validation testing concluded the modification achieved this.
    Meets all defined reliability requirements and performance claimsTesting demonstrated this.

    2. Sample Size and Data Provenance

    The document does not explicitly state the sample size used for the test set or the data provenance (e.g., country of origin of the data, retrospective or prospective). It mentions "Philips verification and validation processes" and "system level tests, performance tests, and safety testing from hazard analysis," but lacks specific details on the datasets used in these tests.

    3. Number of Experts and Qualifications

    The document does not specify the number of experts used to establish the ground truth for the test set or their qualifications. It states that the device "can be used by trained healthcare professionals," implying expert users, but doesn't detail their involvement in the testing.

    4. Adjudication Method

    The document does not describe any specific adjudication method (e.g., 2+1, 3+1, none) for establishing ground truth or evaluating the test set.

    5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study

    The document does not describe a multi-reader multi-case (MRMC) comparative effectiveness study. It focuses on the improvements of the modified CMQ plugin over the unmodified CMQ plugin in terms of workflow and variability, rather than comparing it to human-only performance or quantifying an effect size of human improvement with AI assistance.

    6. Standalone (Algorithm Only) Performance

    The device itself is a "plug-in" within a larger software suite (QLAB). While it's a "quantification software" that can function "either as a stand-alone product that can function on a standard PC, on board a dedicated workstation, or on-board Philips' ultrasound systems," the performance claims are related to its function within the QLAB environment, and specifically the modifications made to the CMQ plug-in. The provided text doesn't explicitly detail a standalone algorithm-only performance study contrasting its output against a ground truth without human interaction beyond the general claims of decreased intra-observer and inter-observer variability. The "modifications ... were implemented to provide clients with improved reproducibility and consistency between users," which implies human interpretation is still integral, but the device assists this process.

    7. Type of Ground Truth

    The document explicitly states the intent to "provide clients with improved reproducibility and consistency between users, as well as to provide users with a reduction of workflow steps" and to achieve "Decreased intra-observer variability of assessments; and Decreased inter-observer variability of assessments." This strongly suggests that the ground truth for evaluating these improvements was based on expert consensus or comparative measurements performed by experts, where the goal was to minimize the deviation of measurements between and within experts, facilitated by the software. It does not mention pathology or outcomes data as the primary ground truth for the specific performance claims of this modification.

    8. Sample Size for the Training Set

    The document does not include any information about the sample size used for a training set. As this is a "Special 510(k) Premarket Notification" for modifications to an already cleared device, it's possible that the core algorithm was trained previously, and this submission focuses on validation of the changes.

    9. How Ground Truth for Training Set was Established

    Since no training set information is provided, there is no description of how ground truth for a training set was established.

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