Search Results

This device is indicated to acquire and display cross-sectional volumes of the whole body, to include the head. The Aquilion Exceed LB has the capability to provide volume sets. These volume sets can be used to perform specialized studies, using indicated software/hardware, by a trained and qualified physician.

AiCE (Advanced Intelligent Clear-IQ Engine) is a noise reduction algorithm that improves image quality and reduces image noise by employing Deep Convolutional Network methods for abdomen, pelvis, lung, extremities, head and inner ear applications.

Aquilion Exceed LB (TSX-202A/3) V10.6 with AiCE-i (Advanced intelligent Clear-IQ Engineintegrated) is a whole body multi-slice helical CT scanner, consisting of a gantry, couch and a console used for data processing and display. This device captures cross sectional volume data sets used to perform specialized studies, using indicated software, by a trained and qualified physician. This system is based upon the technology and materials of previously marketed Canon CT systems.

In addition, the subject device incorporates the latest reconstruction technology, AiCE-i (Advanced intelligent Clear-IQ Engine - integrated), intended to reduce image noise and improve image quality by utilizing Deep Convolutional Network methods. These methods can more fully explore the statistical properties of the signal and noise. By learning to differentiate structure from noise, the algorithm produces fast, high quality CT reconstruction. The AiCE algorithm has not been modified or retrained since the previous clearance in the predicate, K192832.

The provided text is a 510(k) summary for the Aquilion Exceed LB (TSX-202A/3) V10.6 with AiCE-i, a Computed Tomography (CT) system. The document focuses on demonstrating substantial equivalence to a predicate device (Aquilion Prime SP with AiCE-i, K192832) primarily through bench testing and phantom studies, rather than a clinical multi-reader, multi-case (MRMC) study with human subjects. Therefore, many of the requested criteria related to clinical study design and human reader performance are not directly addressed in this submission.

Here's a breakdown based on the provided information:

1. A table of acceptance criteria and the reported device performance

The acceptance criteria are implicitly defined by demonstrating "substantially equivalent or improved performance relative to the predicate device" in various image quality metrics and dose reduction, based on phantom studies. Quantitative targets are stated for some metrics.

2. Sample size used for the test set and the data provenance

The testing described is primarily bench testing utilizing phantoms. Therefore, the "sample size" refers to the number of phantom acquisitions and measurements, not patient data. The provenance is internal to the manufacturer's testing facility, as it's not a clinical study. The data is prospective in the sense that the tests were specifically conducted for this submission.

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

Not applicable for this submission. The ground truth for phantom studies is established by the physical properties of the phantom and known input parameters. No human experts were used for ground truth establishment as it was not a clinical reading study.

4. Adjudication method for the test set

Not applicable. Since the measurements are quantitative from phantom studies, human adjudication is not part of the process.

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, a multi-reader, multi-case comparative effectiveness study involving human readers was not reported in this 510(k) summary. The submission explicitly states: "Representative clinical images were not necessary to demonstrate substantial equivalence of the subject device." The focus was on engineering performance demonstrated through phantom studies.

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

Yes, the performance of the AiCE algorithm (as integrated into the CT system, referred to as "AiCE-i") was evaluated in a standalone manner using phantom studies. The results in the table above demonstrate the algorithm's impact on image quality metrics and noise reduction. The AiCE algorithm itself was not modified or retrained since its previous clearance (K192832), indicating its performance characteristics are presumed stable.

7. The type of ground truth used

The ground truth used for these performance tests was phantom-based. This includes:

• Physical properties of the phantoms (e.g., known material densities, lesion sizes, contrast values).
• Quantitative measurements derived from the phantom scans (e.g., comparing reconstructed values to known phantom values).
• Comparison to gold standards for image quality metrics (e.g., ideal MTF, NPS curves).

8. The sample size for the training set

The document states that "The AiCE algorithm has not been modified or retrained since the previous clearance in the predicate, K192832." This means the training of the AiCE algorithm itself was done prior to the submission for the predicate device. The actual sample size for the training data is not provided in this specific 510(k) summary (K203042). It would have been part of the K192832 submission.

9. How the ground truth for the training set was established

The document does not detail how the ground truth for the training data of the AiCE algorithm was established, as the algorithm itself was not retrained for this submission. This information would typically be found in the original submission for the AiCE algorithm (K192832). Generally, for deep learning algorithms in medical imaging, ground truth for training data is established through a combination of expert consensus, high-quality reference scans, or other validated methods, but this is not specified here.

Ask a specific question about this device