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Q-Station is application software intended to manage, view, analyze, and report qualitative and quantitative image data from ultrasound exams. It is designed to host optional advanced analysis applications via QLAB integration and provides integrated tools that allow users to manually assess and score cardiac wall motion and export images and/or exams and reports. Q-Station can view DICOM images of non-ultrasound images such as CT, MR, NM, CR, MG, XA, PET, RT, and X-Ray modalities for reference viewing. It supports connectivity to ultrasound systems, PACS and other DICOM storage repositories.

Q-Station is designed to manage post-acquisition ultrasound images and other data, for the purposes of diagnosing the patient's condition. This includes using Q-Station on a PC to review images and measurements sent from an ultrasound acquisition device, analyze 3D and other data with QLAB. Q-Station is used to review various ultrasound exam types, including Adult echo, General Imaging, Stress echo, Vascular, and TEE. In addition, Q-Station can be used for reference viewing of non-ultrasound DICOM images. Q-Station can be used to add interpretive findings, key images, measurements and calculations and other comments that create reports that can be shared with other clinicians. During this review, users may also use Q-Station to import and export exams, print reports, and anonymize images for export. Q-Station supports QLAB Q-Apps for advanced analysis (K132165).

Here's a breakdown of the acceptance criteria and the study information for the Philips Q-Station (K140808) based on the provided text:

Important Note: The provided document is a 510(k) summary, which focuses on demonstrating substantial equivalence to predicate devices rather than proving performance against specific quantitative acceptance criteria in a traditional efficacy study. As such, the information you requested regarding numerical performance metrics, sample sizes for test sets, expert involvement for ground truth, and comparative effectiveness studies (MRMC) is not present in this type of regulatory submission. The submission explicitly states "The subject of this premarket submission, Q-Station 3.0 software did not require clinical studies to support substantial equivalence."

Therefore, many of the requested fields will state "Not Applicable" or "Not Provided" in the table below, as the submission relies on verification and validation activities rather than formal clinical studies with statistical acceptance criteria.

1. Table of Acceptance Criteria and Reported Device Performance

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

• Sample Size for Test Set: Not provided. The submission states that "The subject of this premarket submission, Q-Station 3.0 software did not require clinical studies to support substantial equivalence." Testing involved "system level tests, performance tests, and safety testing from risk analysis," implying internal validation rather than a formal test set of patient data.
• Data Provenance: Not provided (not applicable as clinical studies were not performed).

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

• Not applicable/Not provided. Clinical studies with expert-established ground truth were not conducted.

4. Adjudication Method for the Test Set

• Not applicable/Not provided. Clinical studies with adjudication were not conducted.

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. An MRMC comparative effectiveness study was not done. The device is a Picture Archiving and Communications Systems Workstation, and this type of study is not relevant to demonstrating its substantial equivalence for its stated functions of viewing, analysis, and reporting.

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

• Not explicitly described as a standalone algorithm performance study. The device itself is software for managing, viewing, and analyzing images, implicitly involving human interaction. The validation focused on the software's functionality, reliability, and safety when used with a human.

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

• Not applicable/Not provided. For the internal verification and validation, ground truth would likely refer to expected software behavior based on product specifications and design requirements, rather than a clinical ground truth like pathology or expert consensus.

8. The sample size for the training set

• Not applicable/Not provided. This device is described as software for managing, viewing, and analyzing existing image data, rather than an AI/ML algorithm that requires a "training set" in the conventional sense. Its "analysis packages" are "essentially the same as those included with the EPIQ ultrasound system," suggesting pre-existing modules rather than newly trained AI.

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

• Not applicable/Not provided, as there is no mention of a training set for an AI/ML algorithm.

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