(14 days)
Software contained in the PET Application Suite process, analyze, display, and quantify medical images/data. The PET and CT images may be registered and displayed in a "fused" (overlaid in the same spatial orientation) format to provide combined metabolic and anatomical data at different angles. Trained professionals use the images in:
- The evaluation, detection and diagnosis of lesions, disease and organ function such as cancer. cardiovascular disease, and neurological disorders.
- The detection, localization, and staging of tumors and diagnosing cancer patients.
- Treatment planning and interventional radiology procedures.
The PET Application Suite includes software that provides a quantified analysis of regional cerebral activity from PET images.
Cardiac imaging software provides functionality for the quantification of cardiology images and datasets including but not limited to myocardial perfusion for the display of wall motion and quantification of left-ventricular function parameters from gated myocardial perfusion studies and for the 3D alignment of coronary artery images from CT coronary angiography onto the myocardium.
The NexStar Liftoff PET Application Software Suite (referred to as NexStar or Liftoff within the submission) is software used to process, analyze and display medical images and may be sold with Philips nuclear medicine PET/CT Systems or systems marketed by Philips. The PET Software Application Suite is a full suite of applications, including both review and processing.
The NexStar Liftoff PET Application Software Suite is basically the same as the processing and reconstruction software cleared with the predicate device (GEMINI TF, K052640), with the extension of Image Fusion Software to include Metabolic Analysis and Cardiac Realignment.
NexStar software is a Windows®-based suite of image display and processing applications and is deployable on hardware platforms, which meet the minimum requirements needed to run the software.
The provided text is a 510(k) summary for the Philips Medical Systems' NexStar Liftoff PET Application Software Suite. It primarily focuses on demonstrating substantial equivalence to a predicate device (GEMINI Raptor System, K052640) rather than presenting a detailed study proving the device meets specific acceptance criteria in terms of clinical performance.
Therefore, much of the requested information regarding acceptance criteria and performance study details is not available in the provided document. The document explicitly states: "No performance standards have been developed for process and display applications." and "No performance standards have been developed for process and display applications." (repeated)
Here's a breakdown of what can and cannot be answered based on the provided text:
1. Table of Acceptance Criteria and Reported Device Performance
| Acceptance Criteria | Reported Device Performance |
|---|---|
| Not specified for clinical performance. | The submission does not specify quantifiable clinical acceptance criteria for sensitivity, specificity, accuracy, or similar performance metrics for lesion detection, diagnosis, or quantification. |
| Substantial Equivalence: The primary "acceptance criterion" for this 510(k) was to demonstrate that the NexStar Liftoff PET Application Software Suite is substantially equivalent to its predicate device (GEMINI Raptor System, K052640) in terms of intended use and technological characteristics. | The FDA reviewed the submission and determined that the device is substantially equivalent to the predicate device, allowing it to be marketed. The differences (deployment on various hardware platforms, enhancements to processing applications, extension of Image Fusion Software to include Metabolic Analysis and Cardiac Realignment) were deemed not to raise new questions of safety or effectiveness. |
2. Sample Size Used for the Test Set and Data Provenance
- Sample Size: Not specified. The document does not describe a clinical test set with human cases for performance evaluation. The evaluation was primarily a comparison of technical characteristics to a predicate device.
- Data Provenance: Not applicable. No clinical data set is described for testing.
3. Number of Experts Used to Establish Ground Truth for the Test Set and Qualifications of Those Experts
- Number of Experts: Not applicable. No clinical test set requiring ground truth establishment by experts is described.
- Qualifications of Experts: Not applicable.
4. Adjudication Method for the Test Set
- Adjudication Method: Not applicable. No clinical test set requiring adjudication is described.
5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done
- MRMC Study: No, an MRMC comparative effectiveness study was not mentioned or described in the provided document. The document focuses on technical equivalence to a predicate device rather than comparative human-AI performance.
- Effect Size of Human Readers Improve with AI vs. Without AI Assistance: Not applicable, as no such study was described.
6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) Was Done
- Standalone Study: No, a standalone performance study with quantifiable metrics like sensitivity, specificity, or accuracy for the algorithm itself was not described in the provided document. The submission is for application software that processes, analyzes, and displays images for use by trained professionals, implying a human-in-the-loop scenario, but no specific performance study, standalone or otherwise, is detailed.
7. The Type of Ground Truth Used
- Type of Ground Truth: Not applicable. No clinical performance study requiring a specific type of ground truth (e.g., pathology, outcomes data, expert consensus) is described. The "ground truth" for this submission was essentially the established safety and effectiveness of the predicate device, to which this device claimed substantial equivalence in its updates and changes.
8. The Sample Size for the Training Set
- Sample Size: Not specified. The document does not mention or describe a training set for machine learning or AI algorithms. The "software" described focuses on processing, analysis, and display, which typically relies on established algorithms and image processing techniques rather than a large, continuously-trained machine learning model in the context of a 2008 submission.
9. How the Ground Truth for the Training Set Was Established
- Ground Truth Establishment: Not applicable, as no training set requiring ground truth for machine learning was described.
§ 892.1200 Emission computed tomography system.
(a)
Identification. An emission computed tomography system is a device intended to detect the location and distribution of gamma ray- and positron-emitting radionuclides in the body and produce cross-sectional images through computer reconstruction of the data. This generic type of device may include signal analysis and display equipment, patient and equipment supports, radionuclide anatomical markers, component parts, and accessories.(b)
Classification. Class II.