(50 days)
The system is intended to produce cross-sectional images of the body by computer reconstruction of x-ray transmission data taken at different angles and planes, including Axial, Cine, Helical (Volumetric), Cardiac, and Gated acquisitions. These images may be obtained either with or without contrast. This device may include signal analysis and display equipment, patient and equipment supports, components and accessories.
This device may include data and image processing to produce images in a variety of trans-axial and reformatted planes. Further the images can be post processed to produce additional imaging planes or analysis results.
The system is indicated for head, whole body, cardiac and vascular X-ray Computed Tomography applications in patients of all ages.
The device output is a valuable medical tool for the diagnosis of disease, trauma, or abnormality and for planning, guiding, and monitoring therapy.
The Revolution Ascend is a head and whole-body CT system composed of a gantry, patient table, operator console with a host computer, power distribution unit, and interconnecting cables. The system also includes image acquisition and reconstruction hardware/software, general system software, accompanying documents, and associated accessories/interconnections. The system has a 75 cm gantry bore and 64-row detector.
Revolution Ascend generates cross-sectional images of the body by computer reconstruction of x-ray transmission data taken at different angles and planes, including Axial, Cine, Helical (Volumetric), Cardiac, and Gated acquisitions modes.
A design change has been made to the Revolution Ascend with an alternative detector scintillator material prompting this premarket notification. While this change is being made, the design and manufacturing is such that the system performance remains identical to its unmodified predicate. The proposed device carries over all the features, options and specifications of the predicate device, including the Deep Learning Iterative Recon (DLIR) cleared via K212067 without change.
This document is a 510(k) Premarket Notification Summary for the Revolution Ascend CT system. The purpose of this submission is to demonstrate that the proposed device, with a change in detector scintillator material, is substantially equivalent to a legally marketed predicate device. Therefore, the acceptance criteria and study design are focused on proving this equivalence rather than establishing the de novo performance of an AI algorithm or a new medical device.
Based on the provided document, here's a description of the acceptance criteria and the study that proves the device meets them:
1. A table of acceptance criteria and the reported device performance
The document doesn't provide a direct table of specific numerical acceptance criteria for image quality metrics. Instead, the acceptance criteria are implicitly stated as demonstrating equivalence to the predicate device, Revolution Ascend (K203169), across various performance aspects.
| Acceptance Criteria (Implied) | Reported Device Performance |
|---|---|
| Overall System Performance (General IQ Performance): Demonstrating performance in accordance with IEC 61223-3-5 Ed. 2. | Successfully completed. |
| Comparable Image Quality Performance (IQ Equivalence): Demonstrating image quality equivalence using standard IQ, QA phantoms for typical conditions between the proposed device (Revolution Ascend with Merc40H detector) and the predicate device (Revolution Ascend with Merc40L detector). | Successfully completed. "Non-clinical bench test results demonstrated the subject device performs equivalently to the predicate device." |
| Re-substantiation of DLIR Performance (if applicable): Confirming the imaging performance associated with the cleared Deep Learning Iterative Reconstruction (DLIR) (K212067) on the subject device Revolution Ascend remains unchanged. | Successfully completed. "The proposed device carries over all the features, options and specifications of the predicate device, including the Deep Learning Iterative Recon (DLIR) cleared via K212067 without change." "Re-substantiation of the imaging performance associated with the cleared DLIR(K212067) on the subject device Revolution Ascend." |
| Compliance with Regulatory Standards: Adherence to relevant IEC, NEMA, and 21 CFR Subchapter J performance standards. | Compliant. "Revolution Ascend with the modified detector remains compliant with IEC 60601-1 Ed. 3.1 and associated collateral and particular standards, NEMA XR25, XR26, XR28, and 21 CFR Subchapter J performance standards." "The Revolution Ascend has completed testing and in compliance with AAMI/ANSI ES 60601-1 and IEC60601-1 Ed. 3.1 and its associated collateral and particular standards, 21 CFR Subchapter J, and NEMA standards XR 25, XR 26, and XR 28." |
| Safety and Effectiveness: Demonstrating that the device is as safe and effective as the predicate. | Concluded to be as safe and effective. "GE Healthcare believes that the Revolution Ascend is as safe and effective, and performs in a substantially equivalent manner to the unmodified predicate device Revolution Ascend (K203169)." |
2. Sample size used for the test set and the data provenance
The document explicitly states that the testing was non-clinical bench testing using "standard IQ, QA phantoms." It does not involve human patient data or a specific "test set" in the context of clinical studies. Therefore, sample size in terms of patient cases is not applicable here.
- Data Provenance: Not applicable as it's non-clinical bench testing with phantoms.
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts
Not applicable. As the testing was non-clinical bench testing using phantoms and established metrics (e.g., IEC standards, NEMA standards), the "ground truth" is based on the known physical properties and performance characteristics of the phantoms and the objective measurements derived from them, rather than expert interpretation of patient images.
4. Adjudication method for the test set
Not applicable. Since the testing is non-clinical bench testing with phantoms and objective measurements, there is no need for expert adjudication of image findings.
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. This submission is for a hardware change (detector scintillator material) in a CT system, not for a new AI-powered diagnostic device or a modification to an existing AI feature (DLIR is carried over without change). Therefore, an MRMC comparative effectiveness study regarding human reader performance with/without AI assistance is outside the scope of this particular 510(k) submission. The document explicitly states the DLIR was "cleared via K212067 without change," implying its performance was evaluated in that separate submission.
6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done
No. This submission is for a CT scanner system that includes hardware and software. It's not for a standalone algorithm. The "Deep Learning Image Reconstruction (DLIR)" component referenced is a reconstruction algorithm within the CT system, and its standalone performance likely would have been assessed in its original 510(k) clearance (K212067). This submission focuses on demonstrating that the change in detector material does not degrade the performance of the overall system, including features like DLIR.
7. The type of ground truth used (expert consensus, pathology, outcomes data, etc)
For the non-clinical testing, the "ground truth" is based on objective phantom measurements and established engineering metrics as defined by standards like IEC 61223-3-5 Ed. 2. This is not clinical ground truth (e.g., pathology, expert consensus on disease diagnosis). The goal is to demonstrate physical and image quality equivalence.
8. The sample size for the training set
Not applicable. This submission is about a hardware change in an already cleared CT system and is not for training a new AI algorithm. The DLIR component, which involves deep learning, would have had a training set in its original development and clearance (K212067), but details for that are not provided in this document as it's "carried over without change."
9. How the ground truth for the training set was established
Not applicable. As above, this pertains to the development of the DLIR algorithm (likely cleared in K212067), not the current submission for a detector material change.
§ 892.1750 Computed tomography x-ray system.
(a)
Identification. A computed tomography x-ray system is a diagnostic x-ray system intended to produce cross-sectional images of the body by computer reconstruction of x-ray transmission data from the same axial plane taken at different angles. This generic type of device may include signal analysis and display equipment, patient and equipment supports, component parts, and accessories.(b)
Classification. Class II.