(27 days)
Q-Station software is a software application package. It is designed to manage, view and report image data acquired by Ultrasound systems and cardiac waveform data from Philips Stress Vue ECG systems. Q-Station offers support for QLAB plug-ins for analysis, quantification and reporting of data from ultrasound systems.
O-Station is workstation software designed for managing, viewing and reporting qualitative and quantitative image data from ultrasound exams. It includes advanced analysis via QLAB integration (QLAB 8.0) and provides integrated tools that allow users to manually assess and score cardiac wall motion and export images and/or exams and reports. It supports connectivity to ultrasound systems, PACS, other DICOM storage repositories, and Philips Stress Vue ECG systems to aid clinicians in diagnostic activity. Q-Station supports QLAB Plug-ins.
The provided 510(k) summary for Q-Station 1.0 focuses on demonstrating substantial equivalence to a predicate device (Xcelera K061995) rather than on specific clinical performance metrics with pre-defined acceptance criteria.
The submission states that:
- "No performance standards for PACS systems or components have been issued under the authority of Section 514."
- "The Q-Station software has been designed to comply with the following voluntary standards: NEMA PS 3.1 - 3.18 (2008), Digital Imaging and Communications in Medicine (DICOM) Set and IEC/ISO 10918-1:1994 Technical Corrigendum 1:2005, Information technology - Digital compression and coding of continuous-tone still images."
- "Software development for the Q-Station 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."
Therefore, the submission does not include a study that defines explicit acceptance criteria for diagnostic performance (e.g., sensitivity, specificity, accuracy) and then provides data to prove the device meets these criteria in the way new AI/CADe devices typically do. Instead, the focus is on compliance with standards and internal software development processes to mitigate risks and achieve substantial equivalence.
Given the information provided, it is not possible to complete the requested table and details for acceptance criteria and a study proving those criteria were met. The document describes a regulatory submission process based on demonstrating substantial equivalence and compliance with general software/DICOM standards, not a specific clinical performance study with predefined metrics.
Here's a breakdown of what can be inferred or what is explicitly not available based on the provided text:
1. A table of acceptance criteria and the reported device performance
| Acceptance Criteria | Reported Device Performance |
|---|---|
| Not explicit (device is compared to predicate based on functionality and compliance with standards rather than specific performance metrics) | Not provided in a quantitative, performance-based manner within the document for diagnostic accuracy, sensitivity, specificity, etc. |
| Compliance with NEMA PS 3.1 - 3.18 (DICOM) | Stated compliance with this standard. |
| Compliance with IEC/ISO 10918-1:1994 (JPEG compression) | Stated compliance with this standard. |
| Functionality similar to predicate device (Xcelera K061995) for managing, viewing, reporting, and QLAB integration. | Device description outlines these functionalities, implying they function similarly to the predicate. |
| Risk assessment indicating identified risks are controlled. | Stated that "Appropriate steps have been taken to control all identified risks." |
| No new issues of safety or effectiveness are raised. | Stated as a conclusion. |
2. Sample size used for the test set and the data provenance (e.g., country of origin of the data, retrospective or prospective)
- Not provided. The document does not describe a clinical performance test set. The validation mentioned refers to software verification and validation, not clinical data evaluation for diagnostic performance.
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts
- Not applicable / Not provided. No specific test set with ground truth established by experts is mentioned for assessing diagnostic performance.
4. Adjudication method (e.g., 2+1, 3+1, none) for the test set
- Not applicable / Not provided. No specific test set with adjudication is mentioned.
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. The document does not mention any MRMC study. Q-Station is described as a workstation software for managing, viewing, and reporting image data, including advanced analysis via QLAB integration and tools for manual assessment and scoring. It's not presented as an AI/CADe assistance tool in the context of improving human reader performance.
6. If a standalone (i.e., algorithm only without human-in-the-loop performance) was done
- No. The document describes a workstation for human interaction with imaging data, including manual assessment. It does not present a standalone algorithm for diagnostic tasks.
7. The type of ground truth used (expert consensus, pathology, outcomes data, etc)
- Not applicable / Not provided. No specific ground truth for diagnostic performance assessment is mentioned.
8. The sample size for the training set
- Not applicable / Not provided. The device is described as software for managing, viewing, and reporting image data, not explicitly as a machine learning/AI device requiring a "training set" in the conventional sense of AI model development for diagnostic tasks.
9. How the ground truth for the training set was established
- Not applicable / Not provided. (See point 8).
In summary, this 510(k) submission details a software workstation that functions as a tool for clinicians to view, manage, and analyze ultrasound and ECG data. Its regulatory pathway relies on demonstrating substantial equivalence to existing predicate devices and compliance with relevant industry standards (DICOM, JPEG), alongside internal software verification and validation processes. It does not present data from clinical performance studies against specific diagnostic acceptance criteria.
§ 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).