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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 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 proposed device Revolution Ascend Sliding is a head and whole-body CT system composed of a gantry, transporter, operator console with a host computer, power distribution unit, and interconnection cables. The system also includes image acquisition and reconstruction hardware/software, general system software, accompanying documents, and associated accessories/interconnections.

Revolution Ascend Sliding generates cross-sectional images of the body by computer reconstruction of xray transmission data taken at different angles and planes, including Axial, Cine, Helical (Volumetric), Cardiac, and Gated acquisitions modes.

The provided text does NOT include details about acceptance criteria and the study that proves the device meets the acceptance criteria in the format requested.

Here's why and what information IS available:

This device is a Computed Tomography (CT) X-ray System, specifically a hardware modification. The 510(k) summary focuses on demonstrating substantial equivalence to a predicate device (Revolution Ascend, K213938) based on engineering design, performance, and image quality specifications. It's not an AI/CADe device that would typically have the kind of performance metrics (sensitivity, specificity, AUROC) and associated study designs you're asking for.

Therefore, most of the requested fields cannot be filled from this document.

Here's what information I can extract and why other fields are not applicable:

• A table of acceptance criteria and the reported device performance: This detail is not provided. The document states that the device "maintains the identical/equivalent performance and image quality specifications" to its predicate and that "Non-clinical bench test results demonstrated the subject device performs equivalently to the predicate device." However, specific numerical acceptance criteria and reported performance values for those criteria are not listed.
• Sample sized used for the test set and the data provenance: Not applicable in the context of this document. This is not a study evaluating diagnostic performance on a dataset of patient cases. The testing mentioned is "engineering design V &V and bench testing."
• Number of experts used to establish the ground truth for the test set and the qualifications of those experts: Not applicable. No ground truth establishment by experts is described for a diagnostic performance study.
• Adjudication method (e.g. 2+1, 3+1, none) for the test set: Not applicable.
• 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: Not applicable. This is not an AI-assisted diagnostic device.
• If a standalone (i.e. algorithm only without human-in-the-loop performance) was done: Not applicable.
• The type of ground truth used (expert consensus, pathology, outcomes data, etc): Not applicable.
• The sample size for the training set: Not applicable. No training set is mentioned as this isn't an AI/ML device.
• How the ground truth for the training set was established: Not applicable.

What the document does state about testing and compliance:

• Testing Philosophy: The device was tested through "engineering design V &V and bench testing" to demonstrate substantial equivalence to the predicate device.
• Compliance: The device is "in compliance with AAMI/ANSI ES 60601-1 and IEC60601-1 Ed. 3.2 and its associated collateral and particular standards, 21 CFR Subchapter J, and NEMA standards XR 25, XR 26, and XR 28."
• Image Quality Testing: Image quality testing was done "in accordance with IEC 61223-3-5 ed.2 to demonstrate the overall system performance in a standardized and referenceable manner."
• Clinical Testing: "The Revolution Ascend Sliding CT system can be fully tested on the engineering bench thus no additional clinical testing was required." This indicates that the regulatory body agreed that bench testing was sufficient to demonstrate safety and effectiveness for this type of device modification.
• Quality Assurance Measures: Includes "Risk Analysis and Control, Required Reviews, Design Reviews, Testing on unit level (Module verification), Integration testing (System verification), Performance testing (Verification), Safety testing (Verification)."

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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.

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.

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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 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 CT system is head and whole body CT system incorporating the same basic fundamental operating principles as the predicate device. It is composed of a gantry, patient table, operator console, host computer, and power distribution unit (PDU), and interconnecting cables. The system also includes image acquisition and reconstruction hardware/software, general system software, accompanying documents, and associated accessories, interconnections. Its materials and construction are identical to our existing marketed products.

Identical to the predicate, 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. Revolution Ascend's Intended Use and Indications for Use remain identical to those of the predicate device.

Revolution Ascend includes virtually all the available features of the predicate device Revolution Maxima. Compared to the predicate, the changes incorporated into Revolution Ascend are primarily to introduce a widended bore gantry for easy handling of large patient, trauma examinations, interventional procedures and radiotherapy planning, and addition of other existing features already available from GE's other CT systems. These ported features include Auto Pilot workflow enabled by Deep learning based patient Auto Positioning, Intelligent Protocoling enabled by Machine Learning, Smart Plan and Auto Prescription all integrated into the modern software platform and GUI adopted from Revolution CT, and cardiac feature Auto Gating and as well as Interventional feature 3D Guidance.

The provided text describes a 510(k) premarket notification for a Computed Tomography (CT) system, Revolution Ascend, seeking substantial equivalence to a predicate device, Revolution Maxima. This document primarily focuses on demonstrating the new device's equivalence to an already cleared device rather than proving its performance against a new set of clinical acceptance criteria through a standalone study with human readers or specific AI performance metrics.

Therefore, the information required for a comprehensive answer regarding acceptance criteria and a study proving a device meets these criteria (especially for a medical AI/CADe device) is largely not present in this document. The submission is for a new iteration of a CT scanner, not a novel AI-powered diagnostic tool requiring specific clinical performance validation for its AI components against a defined ground truth.

However, I can extract the information that is implicitly or explicitly stated, and highlight where the requested information is absent or not applicable to this type of submission.

Acceptance Criteria and Device Performance (Implicit):

Since this is a 510(k) for substantial equivalence to a predicate CT system, the "acceptance criteria" are primarily that the new device, Revolution Ascend, performs as safely and effectively as the predicate device, Revolution Maxima, and other previously cleared GE CT systems for specific features. The performance is assessed through non-clinical bench testing, image quality (IQ) and dose evaluation using phantoms, and verification/validation testing.

Regarding the Study Proving the Device Meets Acceptance Criteria:

The document describes non-clinical testing for substantial equivalence, not a clinical study designed to prove new performance claims or the efficacy of novel AI features in a clinical setting with human readers.

1. Sample size used for the test set and the data provenance:

   • Test Set Sample Size: No specific number of "cases" or "patients" for a clinical test set is mentioned. The testing involves "standard IQ, QA and ACR phantoms for standard conditions as well as challenging conditions such as with phantoms simulating large patients" and "3D guidance test with phantoms simulating interventional conditions." This indicates laboratory/bench testing using physical phantoms, not a dataset of patient images.
   • Data Provenance: Not applicable as clinical data are not the primary basis for performance evaluation in this submission. The tests are "non-clinical bench test results."

2. Number of experts used to establish the ground truth for the test set and the qualifications of those experts:

   • Not applicable. The "ground truth" for non-clinical phantom testing involves established physical properties, measurements, and engineering specifications, not expert clinical interpretation.

3. Adjudication method (e.g. 2+1, 3+1, none) for the test set:

   • Not applicable as no human interpretation or adjudication of a test set is described.

4. 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 MRMC study was done, nor is it described. This submission is for a CT system, not an AI/CADe device requiring direct clinical performance evaluation in synergy with human readers. While the device includes "Intelligent Protocoling enabled by Machine Learning" and "Auto Positioning by Deep Learning," these appear to be workflow/control features, not diagnostic AI features needing MRMC studies for reader performance improvement for a 510(k) submission.

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

   • No standalone performance study of a diagnostic algorithm is detailed. The performance assessment is focused on the CT system's image quality and dose output, verified through phantom studies and engineering testing, ensuring it's equivalent to the predicate.

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

   • For the non-clinical testing, the "ground truth" is based on the known physical properties of the phantoms, established metrics for image quality and dose (e.g., in accordance with IEC 61223-3-5), and design specifications. There's no clinical ground truth (e.g., pathology, expert consensus) involved.

7. The sample size for the training set:

   • The document mentions "Intelligent Protocoling enabled by Machine Learning" and "Auto Positioning by Deep Learning." However, it does not provide any details about the training data size, composition, or provenance for these AI features. As these are described as "workflow features" and integral to the CT system's operation (rather than standalone diagnostic AI tools with independent performance claims), such detail is typically not required for a 510(k) of a CT scanner. They are presented as existing, ported features or minor enhancements that don't alter the fundamental operating principles or intended use.

8. How the ground truth for the training set was established:

   • Not described/provided in the document. (See point 7).

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