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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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The Deep Learning Image Reconstruction option is a deep learning based reconstruction method intended to produce cross-sectional images of the head and whole body by computer reconstruction of X-ray transmission data taken at different angles and planes, including Axial, Helical (Volumetric), and Cardiac acquisitions, for all ages.

Deep Learning Image Reconstruction can be used for head, whole body, cardiac, and vascular CT applications.

Deep Learning Image Reconstruction is an image reconstruction method that uses a dedicated Deep Neural Network (DNN) that has been designed and trained specifically to generate CT lmages to give an image appearance, as shown on axial NPS plots, similar to traditional FBP images while maintaining the performance of ASiR-V in the following areas: image noise (pixel standard deviation), low contrast detectability, high-contrast spatial resolution, and streak artifact suppression.

The images produced are branded as "TrueFidelity™ CT Images". Reconstruction times with Deep Learning Image Reconstruction support a normal throughput for routine CT.

Deep Learning Image Reconstruction was trained specifically on the Revolution CT family of systems (K163213, K133705). The deep learning technology is integrated into the scanner's existing raw data-based image reconstruction chain to produce DICOM compatible "TrueFidelity™ CT Images".

The system allows user selection of three strengths of Deep Learning Image Recon: Low, Medium or High. The strength selection will vary with individual users' preferences and experience for the specific clinical need.

Acceptance Criteria and Device Performance for Deep Learning Image Reconstruction (K183202)

The Deep Learning Image Reconstruction (DLIR) device, developed by GE Medical Systems, LLC, was evaluated for substantial equivalence to its predicate device, ASiR-V, as part of its 510(k) submission (K183202). The acceptance criteria for the DLIR are implicitly defined by its performance being equivalent to or better than the predicate device across various image quality metrics relevant to CT imaging.

1. Table of Acceptance Criteria and Reported Device Performance

The acceptance criteria for DLIR are based on maintaining the performance of ASiR-V in key imaging characteristics while achieving an image appearance similar to traditional FBP images. The "reported device performance" refers to the demonstrated performance of DLIR relative to ASiR-V during non-clinical and clinical testing, confirming it met the unspoken expectation of non-inferiority or improvement.

2. Sample Size for the Test Set and Data Provenance

The clinical reader study used 60 retrospectively collected clinical cases. The raw data from these cases were reconstructed with both ASiR-V and Deep Learning Image Reconstruction. The data provenance is not explicitly stated in terms of country of origin but is implied to be from standard clinical practice given the retrospective collection of cases.

3. Number of Experts and Qualifications for Ground Truth Establishment (Test Set)

Nine board-certified radiologists were used for the clinical reader study (test set). Their qualifications included:

• Expertise in specialty areas aligning with the anatomical region of each case.
  • Three radiologists specialized in body and extremity anatomy.
  • Three radiologists specialized in head/neck anatomy.
  • Three radiologists specialized in cardiac/vascular anatomy.

A single board-certified radiologist performed a final evaluation of low contrast and small lesions in the abdominal and pelvis region.

4. Adjudication Method (Test Set)

Each image in the clinical reader study was read by 3 different radiologists independently. These radiologists provided an assessment of image quality related to diagnostic use according to a a 5-point Likert Scale. There is no explicit mention of an adjudication process (e.g., 2+1, 3+1), but for the direct comparison part, readers were asked to compare ASiR-V and DLIR images directly.

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study

Yes, a multi-reader multi-case (MRMC) study was performed. The study involved 9 radiologists reading 60 cases reconstructed with both ASiR-V and DLIR.
The exact effect size of how much human readers improve with AI (DLIR) vs. without AI (ASiR-V, as it's also an advanced reconstruction) assistance is not explicitly quantified in terms of specific metrics like diagnostic accuracy improvement or reading time reduction. However, the study's results are stated to "support substantial equivalence and performance claims." Readers were also asked to directly compare ASIR-V and Deep Learning Image Reconstruction images according to three key metrics of image quality preference – image noise texture, image sharpness, and image noise texture homogeneity, implying a preference or at least equivalence for DLIR.

6. Standalone (Algorithm Only) Performance

Yes, standalone (algorithm only) performance was done as part of the engineering bench testing. This included objective measurements of various image quality metrics using identical raw datasets on a GE Revolution CT, then applying DLIR or ASiR-V reconstruction. The results from this testing demonstrated the algorithm's performance in:

• Low Contrast Detectability (LCD)
• Image Noise (pixel standard deviation)
• High-Contrast Spatial Resolution (MTF)
• Streak Artifact Suppression
• Spatial Resolution, longitudinal (FWHM)
• Low Contrast Detectability/resolution (statistical)
• Noise Power Spectrum (NPS) and Standard Deviation of noise
• CT Number Uniformity
• CT Number Accuracy
• Contrast to Noise (CNR) ratio
• Artifact analysis

7. Type of Ground Truth Used

• Non-Clinical Testing: Ground truth for non-clinical testing was established via physical phantoms (e.g., MITA/FDA low contrast phantoms, uniform phantoms, quality assurance phantoms with tungsten wire, oval uniform polyethylene phantoms with embedded objects) and model observers for objective measurements.
• Clinical Testing: Ground truth for the clinical reader study was based on expert consensus/opinion from board-certified radiologists using a 5-point Likert scale for image quality assessment and for direct comparison of image quality preference attributes. A final evaluation by one board-certified radiologist confirmed diagnostic quality against established clinical standards.

8. Sample Size for the Training Set

The document states that Deep Learning Image Reconstruction was trained specifically on the Revolution CT family of systems (K163213, K133705). It also mentions that the Deep Neural Network (DNN) "models the scanned object using information obtained from extensive phantom and clinical data." However, an exact sample size (number of images or cases) for the training set is not provided in the provided text.

9. How the Ground Truth for the Training Set was Established

The ground truth for the training set, which involved "extensive phantom and clinical data," was established through the inherent characteristics of CT imaging data. For example, for phantom data, the known physical properties and structures within the phantoms serve as ground truth. For clinical data, the "ground truth" for the training process would implicitly be derived from high-quality, typically higher-dose or reference-standard reconstructions (e.g., traditional FBP or fully iterative reconstructions) that the DNN aims to emulate or improve upon, often by learning to remove noise while preserving diagnostic information. The DNN was designed to generate CT images to give an image appearance, as shown on axial NPS plots, similar to traditional FBP images while maintaining the performance of ASiR-V.

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