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This device is indicated to acquire and display cross sectional volumes of the whole body, to include the head. The Aquilion Serve SP 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, cardiac, extremities, head and inner ear applications.

Aquilion Serve SP (TSX-307B/1) V1.3 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.

Aquilion Serve SP (TSX-307B/1) V1.3 is equipped with SilverBeam Filter which is a beam shaping filter that leverages the photon-attenuating properties of silver to selectively remove low energy photons from a polychromatic X-ray beam, leaving an energy spectrum optimized for high contrast CT applications.

The provided text is a 510(k) summary for a Computed Tomography (CT) system (Aquilion Serve SP), which focuses on demonstrating substantial equivalence to a predicate device rather than providing a detailed clinical study for a new AI algorithm's performance against acceptance criteria.

While the document mentions AiCE (Advanced Intelligent Clear-IQ Engine) as a noise reduction algorithm that improves image quality by employing Deep Convolutional Network methods, it does not provide the specifics of the study that proves this particular feature meets acceptance criteria. The performance testing section primarily describes bench testing using phantoms to assess image quality metrics and dose reduction, comparing the new device's overall performance to that of the predicate, not specifically the AI algorithm's standalone or human-in-the-loop performance.

Therefore, many of the requested details about the study proving the device (specifically its AI component, AiCE) meets acceptance criteria cannot be extracted from this text. The acceptance criteria and performance data provided are for the CT system as a whole, mainly from phantom testing.

However, based on the limited information related to performance testing in this 510(k) summary, here's what can be inferred and what is missing:

Acceptance Criteria and Device Performance (Inferred from Bench Testing Section):

The document states: "It was concluded that the performance of TSX-307B (Serve SP) was improved and/or substantially equivalent to the predicate device as demonstrated by the results of the testing." This indicates the general acceptance criterion was "improved and/or substantially equivalent" performance when compared to the predicate device, specifically across various image quality metrics and dose reduction.

Missing Information (Crucial for AI Algorithm Performance):

The provided text does not contain the detailed information for a clinical study specifically evaluating the AiCE (Advanced Intelligent Clear-IQ Engine) AI algorithm against acceptance criteria. The summary focuses on the overall CT system's substantial equivalence to a predicate.

Therefore, the following points cannot be answered from the provided text:

2. Sample size used for the test set and the data provenance: Not described for a study specifically on AiCE. The performance testing described is bench testing using phantoms.
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts: Not applicable as no clinical test set using expert ground truth is described for AiCE.
4. Adjudication method (e.g., 2+1, 3+1, none) for the test set: Not applicable as no clinical test set is described.
5. If a multi-reader multi-case (MRMC) comparative effectiveness study was done: Not mentioned. The focus is on technical equivalence and phantom-based image quality.
6. If a standalone (i.e., algorithm only without human-in-the-loop performance) was done: The document states AiCE improves image quality and reduces noise. This implies a standalone capability for image processing, but no specific study or metrics for this standalone performance (e.g., diagnostic accuracy on a dataset) are detailed. The "bench testing" is related to the overall CT system.
7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.): Not mentioned for any AI-specific performance. The stated "ground truth" for the overall CT system's performance is derived from physical phantom measurements.
8. The sample size for the training set: Not mentioned. It's only stated that AiCE uses Deep Convolutional Network methods.
9. How the ground truth for the training set was established: Not mentioned.

In summary: The provided 510(k) summary focuses on demonstrating the substantial equivalence of the Aquilion Serve SP CT system to a predicate device, primarily through bench testing using phantoms and comparing general technical specifications. While it mentions the inclusion of an AI-powered noise reduction algorithm (AiCE), the document does not detail specific studies or acceptance criteria for this AI component's performance, either standalone or in a human-in-the-loop setting, which would typically involve clinical data, expert readers, and specific accuracy metrics.

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This device is indicated to acquire and display cross sectional volumes of the whole body, to include the head. The Aquilion Serve has the capability to provide 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 Neural Network methods for abdomen, pelvis, lung, cardiac, extremities, head and inner ear applications.

Aquilion Serve (TSX-307A/1) V1.2 with AiCE-i (Advanced intelligent Clear-IQ Engine-integrated) 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/hardware, by a trained and qualified physician. This system is based upon the technology and materials of previously marketed Canon CT systems.

The provided text describes the regulatory clearance of the Aquilion Serve (TSX-307A/1) V1.2 with AiCE-i, a Computed Tomography X-ray System, by the FDA. The submission outlines comparisons to a predicate device and various performance tests. However, the document focuses on demonstrating substantial equivalence to a predicate device rather than presenting a formal study with acceptance criteria and a detailed breakdown of results for a specific performance claim of the device's AI component (AiCE) in improving human reader performance.

Several key pieces of information requested, particularly regarding clinical studies focused on AI's impact on human readers, are not explicitly present in the provided text. The document describes technical performance comparisons of the AiCE algorithm (noise reduction, image quality metrics) and clinical verification of diagnostic quality, but not a multi-reader multi-case (MRMC) study on human performance with and without AiCE assistance.

Therefore, the following response will extract the information available and indicate where the requested information is not provided in the supplied text.

Acceptance Criteria and Study Proving Device Meets Criteria

The primary objective of this submission is to demonstrate substantial equivalence of the Aquilion Serve (TSX-307A/1) V1.2 with AiCE-i to a predicate device (Aquilion Lightning (TSX-036A/7) V10.2 with AiCE-i). The "acceptance criteria" can therefore be inferred as demonstrating equivalent or improved performance in various image quality metrics and functional aspects compared to the predicate device, or demonstrating image quality that is diagnostically acceptable. There is no specific acceptance criteria table provided in the context of an "AI effect" on reader performance.

Here's a breakdown of the requested information based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

As there isn't a single "AI acceptance criteria" table for human performance, the table below summarizes the objective image quality performance comparisons the manufacturer conducted to support their claims about AiCE. The "acceptance criteria" is implicitly "equivalent or improved performance" compared to the baseline (FBP, AIDR 3D, or predicate device).

2. Sample Size Used for the Test Set and Data Provenance

• Test Set Sample Size: The document does not specify a distinct "test set" sample size in terms of number of patient cases for the clinical image review. It mentions "Representative body, cardiac, chest, head, and extremity diagnostic images." For phantom studies, specific phantom types (e.g., MITA - FDA LCD Body phantom, 24cm and 32cm water phantoms) are mentioned but not a "sample size" in terms of repeated measurements, though implied through quantitative reporting.
• Data Provenance: Not explicitly stated. The document refers to "volunteer assessment" for automatic scan planning and "clinical images" for diagnostic quality review. The location (country of origin), and whether the data was retrospective or prospective, are not provided.

3. Number of Experts Used to Establish Ground Truth for the Test Set and Qualifications

• Number of Experts: For the clinical image review, the document states "reviewed by an American Board-Certified Radiologist." This implies a single expert.
• Qualifications of Experts: "American Board-Certified Radiologist". No mention of years of experience.

4. Adjudication Method for the Test Set

• For the "clinical images," it states "reviewed by an American Board-Certified Radiologist," indicating a single reader, so no adjudication method is described or implied.
• For phantom studies, the ground truth is objective measurements (e.g., from phantoms), not requiring expert adjudication.

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

• No MRMC study is described in the provided text. The document focuses on technical image quality metrics and a single radiologist's review of diagnostic quality, not a comparative study showing human readers' improvement with or without AI assistance.

6. Standalone (Algorithm Only) Performance

• Yes, standalone performance was assessed for the AiCE algorithm through various phantom and objective image quality metric evaluations (e.g., CNR, MTF, NPS, LCD, noise reduction claims). These evaluate the algorithm's output (image quality characteristics) without human interpretation in the loop.

7. Type of Ground Truth Used

• Phantom-based objective ground truth: For most image quality metrics (CNR, CT number accuracy, uniformity, SSP, MTF, SD, NPS, LCD, spatial resolution, dose reduction claims), physical phantoms were used, and the "ground truth" was derived from objective measurements on these phantoms.
• Expert Consensus/Pathology/Outcomes Data:
  • For the "Clinical Images" evaluation, the ground truth was the subjective assessment of diagnostic quality by a single American Board-Certified Radiologist. This is a form of expert assessment, though not explicitly a "consensus" by multiple experts.
  • No pathology or outcomes data is mentioned as ground truth in this submission for the AI (AiCE) component.

8. Sample Size for the Training Set

• Not provided. The document states that AiCE uses "Deep Convolutional Neural Network methods," implying a training phase, but gives no details on the training dataset size.

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

• Not provided. The document does not detail how the ground truth for the training data used by the Deep Convolutional Neural Network (AiCE) was established. This information would typically be found in more detailed technical specifications or a different section of a submission.

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This device is indicated to acquire and display cross-sectional volumes of the whole body, to include the head.

The Aquilion Lightning has the capability to provide 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 that improves image quality and reduces image noise by employing Deep Convolutional Network methods for abdomen,pelvis, lung, cardiac, extremities, head and inner ear applications.

The Aquilion Lightning (TSX-036A/7) V10.2 with AiCE-i is an 80-row CT system that is a whole body, multi-slice helical CT scanner, consisting of a gantry, couch and console used for data processing and display. This device captures cross sectional volume data sets used to perform specialized studies, using indicated software/hardware, 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 (DCNN) methods. This reconstruction algorithm is predicated on AiCE reconstruction algorithm previously 510(k) cleared per K192832 on the Canon CT scanner Aquilion Prime SP (TSX-303B/8) V10.2 with AiCE-i, which serves as a reference predicate for this submission. The DCNN 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 for every patient.

The provided text describes a 510(k) premarket notification for the "Aquilion Lightning (TSX-036A/7) V10.2 with AiCE-i" system. The acceptance criteria and the study proving the device meets them are outlined, primarily focusing on phantom studies rather than clinical performance for the new AiCE-i feature.

Here's a breakdown of the requested information:

1. Table of acceptance criteria and the reported device performance

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

The document does not mention clinical test sets or human subject data. All performance claims for the AiCE-i feature are based on phantom studies. Therefore, there is no information about data provenance (country of origin, retrospective/prospective) or sample size in terms of patient numbers.

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

Not applicable. The evaluation was based on phantom studies and quantitative measurements, not expert interpretation of clinical images.

4. Adjudication method for the test set

Not applicable, as the evaluation was based on phantom studies and quantitative measurements, not expert interpretation requiring adjudication.

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 MRMC study was done or reported. The document explicitly states: "It was determined that representative clinical images were not necessary to demonstrate substantial equivalence of the subject device." The focus was on demonstrating the technical performance of the AiCE-i algorithm using phantoms.

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

Yes, the performance evaluation primarily focused on the standalone performance of the AiCE-i algorithm as applied to phantom images. The reported improvements in spatial resolution, low contrast detectability, and noise reduction are algorithm-only metrics.

7. The type of ground truth used

The ground truth used was based on phantom measurements. These are objective, reproducible physical properties measured from the phantom, rather than expert consensus, pathology, or outcomes data.

8. The sample size for the training set

The document does not specify the sample size for the training set used for the Deep Convolutional Network (DCNN) methods of AiCE-i. It mentions that AiCE-i is predicated on the AiCE reconstruction algorithm previously cleared under K192832, implying it leverages that established training.

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

The document does not explicitly state how the ground truth for the training set was established. It describes AiCE as "learning to differentiate structure from noise" using DCNN methods. Typically, for such deep learning models, the training ground truth involves pairs of noisy and "clean" or "reference" images, where the "clean" image serves as the ground truth. This could be generated through various methods, such as extremely low-noise scans, statistical models, or simulations, but the specific method is not detailed here.

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