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

The Philips QLAB Advanced Quantification Software System (QLAB) is designed to view and quantify image data acquired on Philips ultrasound systems. QLAB is available either as a stand-alone product that can function on a standard PC, a dedicated workstation, and on-board Philips' ultrasound systems.

The purpose of this Traditional 510(K) Pre-market Notification is to introduce a new to introduce the new 3D Auto MV cardiac quantification application to the Philips QLAB Advanced Quantification Software, which was most recently cleared under K191647. The latest QLAB software version (launching at version 15.0) will include the new Q-App 3D Auto MV, which integrates the segmentation engine of the cleared QLAB HeartModel Q-App (K181264) and the TomTec-Arena 4D MV Assessment application (K150122) thereby providing a dynamic Mitral Valve clinical quantification tool.

The document describes the QLAB Advanced Quantification Software System and its new 3D Auto MV cardiac quantification application.

Here's an analysis of the acceptance criteria and study information:

1. Table of Acceptance Criteria and Reported Device Performance:

The document does not explicitly state acceptance criteria in a quantitative table format (e.g., "accuracy must be > 90%"). Instead, it states that the device was tested to "meet the defined requirements and performance claims." The performance is demonstrated by the non-clinical verification and validation testing, and the 3D Auto MV Algorithm Training and Validation Study.

The document provides a comparison table (Table 1 on page 6-7) that highlights the features and a technical comparison to predicate devices, but this table does not present quantitative performance against specific acceptance criteria for the new 3D Auto MV feature. It lists parameters that the new application will measure, such as:

• Saddle Shaped Annulus Area (cm²)
• Saddle Shaped Annulus Perimeter (cm)
• Total Open Coaptation Area (cm²)
• Anterior Closure Line Length (cm)
• Posterior Closure Line Length (cm)

However, it does not provide reported performance values for these parameters from the validation study against any predefined acceptance criteria. The statement is that "All other measurements are identical to the predicate 4D MV-Assessment application," implying a level of equivalence, but without specific data.

2. Sample Size Used for the Test Set and Data Provenance:

The document mentions that Non-clinical V&V testing also included the 3D Auto MV Algorithm Training and the subsequent Validation Study performed for the proposed 3D Auto MV clinical application. However, it does not specify the sample size used for this validation study (i.e., the test set). The data provenance (e.g., country of origin, retrospective or prospective) is also not specified.

3. Number of Experts Used to Establish Ground Truth for the Test Set and Their Qualifications:

This information is not provided in the document.

4. Adjudication Method for the Test Set:

This information is not provided in the document.

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

The document does not indicate that a MRMC comparative effectiveness study was done. It focuses on the software's performance and substantial equivalence to predicate devices, not on how human readers' performance might improve with its assistance.

6. Standalone (Algorithm Only Without Human-in-the-Loop Performance) Study:

The document describes the 3D Auto MV Q-App as a "semi-automatic tool" and states that the "User is able to edit, accept, or reject the initial landmark proposals of the mitral valve anatomical locations." This suggests that a purely standalone (algorithm-only) performance study, without any human-in-the-loop interaction, would not be fully representative of its intended use. The validation study presumably evaluates its performance within this semi-automatic workflow, but specific details are lacking.

7. Type of Ground Truth Used:

The document describes the 3D Auto MV application integrating the machine-learning derived segmentation engine of the QLAB HeartModel and the TOMTEC-Arena TTA2 4D MV-Assessment application. The ground truth for the training of the HeartModel (and subsequently the 3D Auto MV) would typically involve expert annotations of anatomical structures. However, the specific type of ground truth used for the validation study mentioned ("3D Auto MV Algorithm Training and the subsequent Validation Study") is not explicitly stated. Given the context of cardiac quantification, it would most likely be based on expert consensus or expert-derived measurements from the imaging data itself.

8. Sample Size for the Training Set:

The document mentions "3D Auto MV Algorithm Training" but does not specify the sample size used for the training set.

9. How the Ground Truth for the Training Set Was Established:

The document states that the 3D Auto MV Q-App "integrates the segmentation engine of the cleared QLAB HeartModel Q-App (K181264)". For HeartModel, it says: "The HeartModel Q-App provides a semi-automatic 3D anatomical border detection and identification of the heart chambers for the end-diastole (ED) and end-systole (ES) cardiac phases." And for its contour generation: "3D surface model is created semi-automatically without user interaction. User is required to edit, accept, or reject the contours before proceeding with the workflow."

This implies that the training of the HeartModel's segmentation engine (and inherited by 3D Auto MV) was likely based on expert-derived or expert-validated anatomical annotations/contours, which would have been used to establish the "ground truth" for the machine learning algorithm. However, explicit details on how this ground truth was established for the training data (e.g., number of annotators, their qualifications, adjudication methods) are not provided for this specific submission (K200974). It simply references the cleared HeartModel Q-App (K181264).

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

Philips QLAB Advanced Quantification software (QLAB) is designed to view and quantify image data acquired on Philips ultrasound systems. QLAB is available either as a stand-alone product that can function on a standard PC, a dedicated workstation, and on-board Philips' ultrasound systems.

The subject QLAB 3D Auto RV application integrates the segmentation engine of the cleared QLAB HeartModel (K181264) and the TomTec-Arena 4D RV-function (cleared under K150122) thereby providing a dynamic Right Ventricle clinical functionality. The proposed 3D Auto RV application is based on the automatic segmentation technology of HeartModel applied to the Right Ventricle, and uses machine learning algorithms to identify the endocardial contours of the Right Ventricle.

Here's a summary of the acceptance criteria and the study details for the QLAB Advanced Quantification Software 13.0, specifically for its 3D Auto RV application:

1. Table of Acceptance Criteria and Reported Device Performance

2. Sample Size and Data Provenance

• Test Set Sample Size: Not explicitly stated for either the internal validation study or the external published study.
• Data Provenance:
  • Internal Validation Study: "Test datasets were segregated from training data sets." No explicit country of origin is mentioned. It is implied to be retrospective as it uses "data sets."
  • External Published Study: Not specified, but it's an "external study published in the Journal of the American Society of Echocardiography."

3. Number of Experts and Qualifications for Ground Truth (Test Set)

• Internal Validation Study: Not specified. However, the comparison is primarily against a "predicate 4D RV" which would have its own established methodology. The "healthcare professional" is mentioned in the context of user evaluation.
• External Published Study: Not specified. The ground truth method is cross-modality CMR, implying a reference standard rather than expert consensus on the test images themselves.

4. Adjudication Method (Test Set)

• Internal Validation Study: Not explicitly stated. The comparison is against the predicate device's measurements.
• External Published Study: Not explicitly stated. Ground truth was established by cross-modality Cardiac Magnetic Resonance (CMR).

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

• The document does not explicitly describe a formal MRMC comparative effectiveness study where human readers' performance with and without AI assistance was evaluated. The text mentions that "the healthcare professional was able to successfully determine which contours required revision and was capable of revising," which suggests a human-in-the-loop scenario, but a comparative effectiveness study with effect size is not reported.

6. Standalone (Algorithm Only) Performance

• Yes, a standalone performance evaluation of the algorithm is implied. The internal validation study reports "RV end diastolic volume error rates below 15% for every data set tested compared to the predicate 4D RV," and RMSE values for volume and EF. The external study also reports the 3D Auto RV's performance against CMR. While user interaction for editing is a feature, the initial segmentation engine and its quantification are evaluated in a standalone manner before potential revision.

7. Type of Ground Truth Used

• Internal Validation Study: The primary comparison for quantitative metrics (volumes, EF) is against the "predicate 4D RV" (TomTec-Arena 4D RV-function, K150122). This suggests the predicate's measurements served as a reference.
• External Published Study: Cross-modality Cardiac Magnetic Resonance (CMR) was considered the gold standard ("Ground truth in this study was considered to be the cross-modality CMR").

8. Sample Size for the Training Set

• Not explicitly stated for the machine learning algorithm. The document only mentions that "Test datasets were segregated from training data sets."

9. How Ground Truth for the Training Set Was Established

• Not explicitly detailed. The device description states the 3D Auto RV application "uses machine learning algorithms to identify the endocardial contours of the Right Ventricle." It also mentions "Algorithm Training procedure is same between the subject and the predicate HeartModel." For HeartModel (the segmentation engine's predecessor for LV), expert-defined contours on extensive datasets would typically be used for training, but this is not explicitly stated for the RV training.

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

Philips QLAB Advanced Quantification software (QLAB) is designed to view and quantify image data acquired on Philips ultrasound systems. QLAB is available either 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 off-line review and quantification of ultrasound studies. QLAB software provides basic and advanced quantification capabilities across a family of PC and cart based platforms. OLAB software functions through O-App modules, each of which provides specific capabilities. QLAB builds upon a simple and thoroughly modular design to provide smaller and more easily leveraged products.

The provided document describes the FDA 510(k) clearance for Philips Healthcare's QLAB Advanced Quantification Software 13.0, primarily focusing on its substantial equivalence to previously cleared predicate devices. The modifications in QLAB 13.0 involve integrating existing TomTec-Arena applications (AutoSTRAIN LV, AutoSTRAIN LA, AutoSTRAIN RV) into the Philips QLAB platform.

However, the document does not contain specific details about acceptance criteria or a dedicated study design that proves the device meets specific performance criteria. Instead, it relies on the concept of substantial equivalence to predicate devices that have already undergone prior clearance.

Based on the information provided, here's what can be extracted and what is missing:

Key Takeaways from the Document:

• Device: QLAB Advanced Quantification Software 13.0
• Purpose: To view and quantify image data acquired on Philips ultrasound systems.
• Modifications: Integration of AutoStrain LV, LA, and RV modules from TomTec-Arena (previously cleared under K150122) with "workflow improvements."
• Regulatory Pathway: 510(k) premarket notification, based on substantial equivalence.
• Clinical Testing: "QLAB 13.0 does not introduce new indications for use, modes, or features relative to the predicate (K181264) that require clinical testing." This explicitly states that no new clinical study was performed for this specific 510(k) submission.
• Performance Data: Relies on "Verification and software validation data" and "Design Control activities" (Requirements Review, Design Review, Risk Management, Software Verification and Validation) to support substantial equivalence.

Therefore, it's not possible to provide the requested information regarding acceptance criteria and a study proving the device meets those criteria, as such a study (with the specified details) was explicitly stated as not required and not performed for this 510(k) submission.

The document justifies its clearance based on the following:

1. The new functionalities (AutoStrain LV, LA, RV) are derived from applications (TomTec-Arena AutoSTRAIN and 2D CPA) that were already cleared under K150122.
2. The current modifications primarily focus on integrating these existing functionalities into the QLAB platform and making "workflow improvements."
3. The intended use remains the same as the predicate device.
4. The manufacturer performed non-clinical performance testing including software verification and validation, design control activities, and risk management to ensure the modified software performs safely and effectively relative to the predicate device and meets defined requirements.

If a hypothetical scenario were to involve a new device or a significant change requiring a de novo clearance or a more involved 510(k) where clinical performance needed to be demonstrated, the requested information would be crucial. However, for this specific 510(k) for QLAB 13.0, the provided document indicates that the performance evaluation was based on demonstrating equivalence, not on new clinical performance studies with acceptance criteria for the new features.

To answer your prompt directly, given the provided text, the answer to most of your questions is that this information is not present because a new comparative effectiveness study or standalone performance study with new ground truth establishment was explicitly deemed unnecessary due to the nature of the submission (integration of already cleared components and "workflow improvements").

Here's a breakdown of the requested information, indicating what is not available from this document due to the nature of the 510(k) submission:

1. A table of acceptance criteria and the reported device performance

• Not available in the provided document. The submission relies on substantial equivalence to predicate devices, not on demonstrating new performance against defined acceptance criteria for the integrated features. The document states: "QLAB 13.0 does not introduce new indications for use, modes, or features relative to the predicate (K181264) that require clinical testing."

2. 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 document. No specific clinical test set data is described for QLAB 13.0. The "Verification and software validation data" mentioned are non-clinical, likely internal testing using synthetic data, simulated data, or existing clinical data from the development of the predicate/reference devices, but details are not provided.

3. 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 document. No new ground truth establishment process is described for QLAB 13.0 as no new clinical study was conducted.

4. Adjudication method (e.g. 2+1, 3+1, none) for the test set

• Not available in the provided document. No new clinical test set is described.

5. 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 document, and explicitly stated as not required/done. The document explicitly states: "QLAB 13.0 does not introduce new indications for use, modes, or features relative to the predicate (K181264) that require clinical testing." Therefore, no MRMC study was performed as part of this 510(k) submission.

6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done

• Not available in the provided document. Similarly, no new standalone performance study for the integrated algorithms is described beyond the assertion that the underlying algorithms (from K150122) were previously cleared.

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc)

• Not available in the provided document. As no new clinical study requiring ground truth was conducted for QLAB 13.0, this information is not provided. The ground truth for the original cleared components (TomTec-Arena AutoSTRAIN and 2D CPA) would have been established at their time of clearance (K150122), but those details are not in this document.

8. The sample size for the training set

• Not available in the provided document. This document describes a 510(k) clearance for a software update integrating existing, cleared algorithms. It doesn't detail the training data for the original development of those algorithms.

9. How the ground truth for the training set was established

• Not available in the provided document. (See point 8).

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

Philips QLAB Advanced Quantification software (QLAB) is designed to view and quantify image data acquired on Philips ultrasound systems. QLAB is available either 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 off-line review and quantification of ultrasound studies.

QLAB software provides basic and advanced quantification capabilities across a family of PC and cart based platforms. QLAB software functions through Q-App modules, each of which provides specific capabilities.

QLAB builds upon a simple and thoroughly modular design to provide smaller and more easily leveraged products.

Philips Ultrasound is submitting this 510(k) to address QLAB 11.0 modifications which include:

• Dynamic Heart Model (DHM) an enhancement to the Heart Model Quantification ● application that provides tracking of the entire cardiac cycle
• QLAB functionality upgraded to the HSDP Platform 2 from the HSDP Platform 1 ●
• O-Store Shared central database supporting multiple clients. .

The document provided is a 510(k) premarket notification for the Philips QLAB Advanced Quantification Software. It states that the submission is for modifications to an existing device (QLAB 10.8 K171314) and does not introduce new indications, modes, features, or technologies that require clinical testing. Therefore, there is no detailed study described that definitively calculates specific acceptance criteria and device performance metrics in the traditional sense of a clinical trial for a novel device.

However, based on the information provided, we can infer the approach to acceptance criteria and "performance" from the perspective of software verification and validation for modifications to an already cleared device.

1. Table of Acceptance Criteria and Reported Device Performance

Since this is a submission for modifications to an existing cleared device, the "acceptance criteria" revolve around ensuring the modified software functions as intended and does not negatively impact the safety and effectiveness of the previously cleared predicate device. Performance is demonstrated through software verification and validation against internal requirements.

2. Sample Size Used for the Test Set and Data Provenance

The document does not specify a "test set" in the context of patient data or clinical images for evaluating the diagnostic performance of the algorithms. Instead, the testing described is focused on software verification and validation. This typically involves:

• Test Cases: Software testing would involve a suite of test cases designed to cover all functionalities, new and existing, and boundary conditions. The number of these test cases is not specified.
• Data Provenance: The document does not mention the use of patient data for performance evaluation in terms of diagnostic accuracy. The testing is focused on the software's functional and technical aspects. Since this is an upgrade to an existing quantification software, it is likely that existing image data (possibly de-identified, potentially from various sources including internal datasets or public datasets for software testing purposes) would have been used to validate the functions of the application, but this is not explicitly stated. The document strongly emphasizes that no new indications or technologies requiring clinical testing are introduced.

3. Number of Experts Used to Establish the Ground Truth for the Test Set and Qualifications of Those Experts

Given that no clinical testing requiring a "ground truth" established by external experts is detailed, this information is not provided. The "ground truth" for software verification and validation is defined by the product's functional and technical requirements.

4. Adjudication Method for the Test Set

Not applicable, as no external expert adjudication for a "test set" (in the clinical sense) is described.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done

No. The document explicitly states: "QLAB 11.0 introduces no new indications for use, modes, features, or technologies relative to the predicate device (QLAB 10.8 K171314) that require clinical testing." Therefore, an MRMC study comparing human readers with and without AI assistance was not performed.

6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) Was Done

The QLAB Advanced Quantification Software is described as a "software application package" designed to "view and quantify image data." It functions as an "off-line review and quantification" tool. While its primary function is quantification, the context implies it's a tool used by a human to assist in diagnosis or assessment. The mention of "tracking of the entire cardiac cycle" and "expanding the measurements" for the Dynamic Heart Model suggests algorithmic quantification, but it is not presented as a standalone diagnostic AI system that operates without human review or interaction. The performance data focuses on the software fulfilling its functional requirements within the existing framework of the predicate device.

7. The Type of Ground Truth Used

The "ground truth" for the software verification and validation activities is based on the defined software requirements and specifications. This is a functional "ground truth" rather than a clinical ground truth (like pathology, expert consensus on patient outcomes). The goal was to demonstrate that the software modifications (Dynamic Heart Model, HSDP Platform 2, Q-Store) work as designed.

8. The Sample Size for the Training Set

No training set is mentioned. This submission is for modifications to quantification software, not a de novo AI model that requires training on a dataset. The "Dynamic Heart Model" is described as an "enhancement" to an existing application providing "tracking" and "expanding measurements," suggesting algorithmic improvements rather than a new discriminative AI model requiring a separate training set.

9. How the Ground Truth for the Training Set Was Established

Not applicable, as no training set for a de novo AI model is mentioned.

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QLAB Advanced Quantification Software is a software application package. It is designed to view and quantify image data acquired on Philips Healthcare Ultrasound systems.

Philips QLAB Advanced Quantification software (OLAB) is designed to view and quantify image data acquired on Philips ultrasound products. QLAB is available either 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 off-line review and quantification of ultrasound studies.

QLAB software provides basic and advanced quantification capabilities across a family of PC and cart based platforms. QLAB software functions through Q-App modules, each of which provides specific capabilities.

The provided FDA 510(k) summary for Philips' QLAB Advanced Quantification Software (K171314) focuses on modifications to existing Q-Apps (a2DQ and aCMQ/CMQ Stress) and primarily addresses software verification and validation, rather than a clinical study establishing new acceptance criteria or device performance through a comparative effectiveness study.

Therefore, much of the requested information (such as specific performance metrics, sample sizes for test sets, expert qualifications, adjudication methods, and MRMC study details) is not explicitly detailed in this document in the typical format of a clinical performance study. The document emphasizes equivalence to a predicate device and internal testing.

However, based on the provided text, here's an attempt to answer the questions, highlighting where information is not available:

Acceptance Criteria and Device Performance Study Details

1. Table of Acceptance Criteria and Reported Device Performance

The document does not specify quantitative acceptance criteria or a "reported device performance" in terms of clinical metrics (e.g., sensitivity, specificity, accuracy) from a comparative study. Instead, the acceptance is based on the device meeting its defined requirements and performance claims during internal software verification and validation.

Acceptance Criteria (Implied from the document): The modified QLAB a2DQ and aCMQ/CMQ Stress Q-Apps are safe and effective and introduce no new risks, meeting defined requirements and performance claims validated through internal processes.

Reported Device Performance:

• The modifications to the a2DQ and aCMQ/CMQ Stress Q-Apps were tested in accordance with Philips internal processes.
• Verification and software validation data support the proposed modified QLAB a2DQ/aCMQ/CMQ Stress software relative to the currently marketed unmodified QLAB software.
• Testing demonstrated that the proposed QLAB Advanced Quantification Software, with modified Q-Apps, meets defined requirements and performance claims.

2. Sample size used for the test set and the data provenance

• Sample Size: Not specified. The document refers to "software verification and validation data," but does not provide details on the number of cases or images used in this testing.
• Data Provenance: Not specified. It only mentions "Philips internal processes" for testing. Specifics like country of origin or retrospective/prospective nature of data are not mentioned.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts

• This information is not provided. The document focuses on software validation and does not detail an expert-based ground truth establishment process for a clinical test set.

4. Adjudication method for the test set

• This information is not provided, as the nature of the "test set" described is for software verification/validation rather than a clinical adjudication process.

5. 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

• No, a multi-reader multi-case (MRMC) comparative effectiveness study is not mentioned in this document. The submission focuses on device equivalence and software modifications, not an assessment of human reader improvement with AI assistance.

6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done

• The document implies that "Software Verification and Validation testing" was performed for the algorithms. While it doesn't explicitly state "standalone performance study," the entire context of "software only device" and "modification to software application package" suggests that the functional testing would inherently be of the algorithm's performance. However, there are no specific performance metrics (e.g., accuracy, precision) reported for this standalone performance.

7. The type of ground truth used

• The document does not explicitly state the type of "ground truth" in a clinical sense (e.g., pathology, outcomes data, expert consensus). Given it's a software modification submission, the "ground truth" for validation would likely be based on established reference values or measurements within the existing QLAB system, against which the modified algorithms were compared for consistent and accurate computation in "Requirements Review," "Design Review," "Risk Management," and "Software Verification and Validation" activities.

8. The sample size for the training set

• Not applicable/Not specified. The document describes modifications to existing software ("QLAB builds upon a simple and thoroughly modular design"). It does not describe the development of a de novo AI algorithm that would typically involve a separate "training set." The focus is on the verification of modified functionalities within an existing proven system.

9. How the ground truth for the training set was established

• Not applicable/Not specified, as no training set for a new AI algorithm is discussed.

Summary of Document Focus:

This FDA 510(k) summary is for a software modification to an existing device (QLAB Advanced Quantification Software). The primary goal is to demonstrate "substantial equivalence" to a predicate device and to show that the modifications do not introduce new safety or effectiveness risks. The "study" referenced is internal software verification and validation, not a clinical trial or comparative effectiveness study. Therefore, the details requested for clinical performance metrics, reader studies, and explicit ground truth establishment for clinical data sets are largely absent from this particular type of submission.

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