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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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The PercuNav system is a stereotaxic accessory for computed tomography (CT), magnetic resonance (MR), ultrasound (US), and positron emission tomography (PET). CT, Ultrasound, PET, and MR may be fused in various combinations, such as CT with MR, MR with ultrasound, and so on. It may include instrumentation to display the simulated image of a tracked insertion tool such as a biopsy needle or probe on a computer monitor screen that shows images of the target organs and the current and the projected future path of the interventional instrument. The PercuNav system is intended for treatment planning and guidance for clinical, interventional, or diagnostic procedures. The PercuNav system also supports an image-free mode in which the proximity of the interventional device is displayed relative to another device.

The PercuNav system is intended to be used in interventional and diagnostic procedures in a clinical setting. The PercuNav system is also intended for use in clinical interventions to determine the proximity of one device relative to another.

Example procedures include, but are not limited to, the following:

• · Image fusion for diagnostic clinical examinations and procedures
• · Soft tissue biopsies (liver, lung, kidney, breast, pancreas, bladder, adrenal glands, lymph node, mesentery, and so on.)
• · Soft tissue ablation (liver, kidney, breast, pancreas, lung, and so on)
• Bone ablations
• Bone biopsies
• · Nerve blocks and pain management
• Drainage placements
• Tumor resections

The proposed PercuNav provides image-guided diagnostic and intervention that enables fusion of diagnostic images and guidance of tracked instruments to physician-defined targets. The target can be indicated either pre-procedurally or intra-procedurally, either using images or relative to an indicated position on the patient.

The proposed PercuNav provides real-time, three-dimensional visualization and navigation tools for all stages of diagnosis and intervention, including pre-procedure planning and procedure navigation. The system transforms two-dimensional patient images into dynamic representations that can be fused with live ultrasound or other previously acquired images. Those two-dimensional patient images, or scan sets, are derived from Ultrasound, CT, PET, PET/CT, and MRI. The resulting dynamic representation supports diagnostic review and instrument navigation.

The PercuNav system performs spatial mapping from one image space to another image space or from image space to physical space (registration), allowing the physician to correlate scan sets with each other and to the patient. The system facilitates minimally invasive diagnostic and interventional procedures.

Features include the following:

• Multiple Applications: The PercuNav system supports multiple applications and can be used for ablations, biopsies, and other diagnostic and guidance procedures.
• Multiple Modalities: The PercuNav system works with images from multiple modalities, including but not limited to CT, MR, PET, and ultrasound.

The Philips PercuNav Image Fusion and Interventional Navigation system, as described in the provided 510(k) summary, adds automatic MR/ultrasound registrations as a new technological characteristic compared to its predicate device. The performance data presented focuses on the accuracy of these new auto-registration features.

Here's a breakdown of the acceptance criteria and the study proving the device meets them:

1. Table of Acceptance Criteria and Reported Device Performance

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

The document states that "The accuracy test was done for the liver vessel and liver surface auto registrations." However, it does not specify the sample size used for this testing.

Regarding data provenance, the document does not explicitly state the country of origin of the data. Given Philips Ultrasound Inc. is in Bothell, WA, USA, and the FDA is a U.S. regulatory body, it's highly probable the data is primarily from the United States. The study is a non-clinical performance test, and it's implied to be retrospective as it's a test of the developed feature, not a prospective clinical trial.

3. Number of Experts Used to Establish Ground Truth and Qualifications

The document does not specify the number of experts used or their qualifications for establishing ground truth. The ground truth for the accuracy test is implicitly the "existing manual registration method available on the currently cleared PercuNav (K132087)." This suggests that the accuracy of the manual registration was used as the benchmark against which the auto-registrations were compared, rather than a separate expert-derived ground truth.

4. Adjudication Method for the Test Set

The document does not describe any explicit adjudication method. The comparison is between the automated process and an existing manual process, implying the manual process provided the reference.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was done

No, an MRMC comparative effectiveness study was not done. The document explicitly states: "The proposed PercuNav did not require clinical study, since substantial equivalence to the currently marketed predicate devices... was demonstrated with the following attributes... Non-clinical performance testing." The testing described is a non-clinical accuracy test of the auto-registration feature itself, not a study of how human readers improve with AI assistance.

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

Yes, a standalone (algorithm only) performance study was done for the auto-registration features. The accuracy test directly assessed the performance of the auto-registration algorithm by comparing it to the manual registration method. This is an algorithm-only test, as there is no human decision-making loop described for the output of the auto-registration.

7. The Type of Ground Truth Used

The ground truth used for the accuracy test was the established accuracy of the "existing manual registration method available on the currently cleared PercuNav (K132087)." This implies that the accuracy of this manual method served as the reference against which the automated method was deemed "as good as." This is not expert consensus from independent readers, pathology, or outcomes data. It is a comparison to a previously validated manual system.

8. The Sample Size for the Training Set

The document does not specify the sample size used for the training set for the auto-registration algorithms.

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

The document does not explicitly state how the ground truth for the training set was established. Given the nature of the device (image fusion and navigation), it is highly probable that the training data's "ground truth" for registration would involve:

• Careful manual registration performed by trained operators/engineers.
• Potentially, phantoms with known geometric properties.
• Utilizing the established accuracy of the predicate device's manual registration technology.

However, this is inferred, as the document provides no specific details on the training data or its ground truth establishment.

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