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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 Prime 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.

The Aquilion Prime SP TSX-303B/1 is an 80-row CT System that is intended to acquire and display cross-sectional volumes of the whole body, including the head. This system is based upon the technology and materials of previously marketed Toshiba CT systems.

The provided text describes a 510(k) submission for the Toshiba Aquilion Prime SP, TSX-303B/1, v8.4. It outlines modifications to a previously cleared CT system. While the document mentions various performance evaluations and studies, it does not contain specific acceptance criteria tables nor detailed study designs that definitively "prove" the device meets acceptance criteria in the format of a typical peer-reviewed clinical study. Instead, it focuses on demonstrating substantial equivalence to a predicate device through engineering and performance testing.

However, I can extract and infer information about the testing and performance as described in the document.

Missing Information:

• A clear table of acceptance criteria for specific performance metrics. The document describes improvements but doesn't explicitly state "acceptance criteria" values met.
• Detailed sample sizes for all tests.
• Specific data provenance for all tests (e.g., country of origin, retrospective/prospective).
• Number and qualifications of experts for all ground truth establishment.
• Adjudication methods.
• MRMC comparative effectiveness study details (effect size of human reader improvement with AI).
• Standalone algorithm performance (the device is a CT system, not an algorithm in the AI sense).
• Sample size for the training set.
• How ground truth for the training set was established.

Based on the provided text, here's what can be extracted and inferred:

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

The document does not explicitly present a table of acceptance criteria. Instead, it describes performance improvements and that the modified system "demonstrates equivalent or slightly improved image quality characteristics." The performance evaluations are primarily focused on physical parameters and dose reduction, not diagnostic accuracy in the way an AI algorithm might be assessed against clinical endpoints.

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

• Sample Size for Physical Performance Tests: Not explicitly stated. The tests involved "model observer studies" using MITA-FDA LCD Head and MITA-FDA LCD Body phantoms, implying a phantom-based test set rather than patient data.
• Sample Size for Image Review: "Representative diagnostic images" were obtained. The exact number is not specified.
• Data Provenance: Not specified. Phantoms for performance tests. Clinical images for diagnostic quality assessment (implicitly from a clinical setting, but no country of origin or retrospective/prospective status is mentioned).

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

• Number of Experts: One.
• Qualifications of Expert: An "American Board Certified Radiologist." Further details on experience (e.g., years) are not provided.
• Role: This radiologist "reviewed" the "representative diagnostic images" to confirm they were of "diagnostic quality."

4. Adjudication method for the test set

• Adjudication Method: Not applicable or not specified in detail. The document states a single American Board Certified Radiologist reviewed images. There is no mention of consensus or multi-reader adjudication for this informal review of diagnostic quality. For the quantitative performance metrics (dose reduction, LCD, noise), these were based on phantom studies and model observer analysis, not human reader 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

• MRMC Study: No. The document does not describe a MRMC comparative effectiveness study. This submission is for a CT system itself, not an AI-assisted diagnostic tool in the typical sense of showing improved human reader performance. The "AI" mentioned (AIDR 3D Enhanced, SEMAR) refers to image processing algorithms within the CT system to improve image quality or reduce artifacts, not a separate AI application for diagnosis or interpretation assistance that would warrant an MRMC study comparing human readers with and without its aid.

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

• Standalone Performance: Yes, in a way. The "performance testing" of the modified system, including spatial resolution, CT number, noise properties, LCD, and CNR, as well as the quantitative dose reduction and LCD/noise improvement studies using phantoms and model observers, represent a standalone evaluation of the system's technical image quality parameters. These are inherent algorithmic and hardware performance metrics of the CT scanner, not dependent on human interpretation for their measurement.

7. The type of ground truth used

• For Quantitative Performance: Model observer studies using MITA-FDA LCD Head and MITA-FDA LCD Body phantoms. These phantoms represent a controlled, objective ground truth for physical image quality parameters.
• For Diagnostic Quality: The subjective assessment of an "American Board Certified Radiologist" confirming images were of "diagnostic quality." This is expert opinion/consensus for a qualitative judgment rather than a definitive "ground truth" like pathology.

8. The sample size for the training set

• Training Set Sample Size: Not applicable / Not provided. This document describes a 510(k) submission for a CT scanner, not a machine learning algorithm that requires a "training set" in the conventional sense. While there might be internal development and validation data, it's not discussed as a distinct "training set" within this regulatory context.

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

• Ground Truth Establishment for Training Set: Not applicable / Not provided, as there is no described training set for an AI algorithm in the context of this submission.

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This device is a digital radiography/fluoroscopy system used in a diagnostic interventional angiography configuration. The system is indicated for use in diagnostic and angiographic procedures for blood vessels in the heart, brain, abdomen and lower extremities.

INFX-8000V, V6.40, is an X-ray system that is capable of radiographic and fluoroscopic studies and is used in an interventional setting. The system consists of a C-arm, which is equipped with an X-ray tube, beam limiter and X-ray receptor, X-ray controller, computers with system and processing software, and a patient radiographic table. This system offers an optional hybrid (aSi/CMOS) 12 inch flat panel detector (TFP-1200C) to provide high definition (HD) imaging.

This document describes the premarket notification (510(k)) for the Toshiba Medical Systems Corporation's INFX-8000V, V6.40, an image-intensified fluoroscopic X-ray system. The submission aims to demonstrate substantial equivalence to a predicate device (INFX-8000V, V6.35, K162614).

Acceptance Criteria and Device Performance:

The document doesn't explicitly list "acceptance criteria" in a typical quantitative clinical trial sense with specific metrics for disease detection or diagnostic accuracy (e.g., sensitivity, specificity). Instead, the acceptance criteria for this 510(k) submission revolve around demonstrating substantial equivalence to a cleared predicate device. This is achieved by proving that the modified device's performance is equal to or better than the predicate device, especially regarding imaging performance, and that the changes do not introduce new safety issues or alter the intended use.

The reported device performance presented focuses on technical and physical characteristics, rather than diagnostic efficacy with human-in-the-loop studies.

Here's a table summarizing the implicit acceptance criteria based on the information provided and the reported device performance:

Study Information:

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

   • The document describes "Bench Testing" conducted to compare the modified system to the predicate device. However, it does not specify the sample size for this test set (e.g., number ofphantoms, or measurements taken).
   • The data provenance is not explicitly stated in terms of country of origin or whether it's retrospective or prospective. It's implied to be internal testing conducted by Toshiba Medical Systems Corporation, likely in Japan (given TMSC's location). This type of testing is generally prospective in nature for a 510(k) submission, where new measurements are taken with the modified device.

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

   • This submission focuses on technical and physical performance metrics (spatial resolution, contrast, etc.) rather than diagnostic accuracy involving human interpretation. Therefore, there is no mention of experts being used to establish a "ground truth" for interpretations of images from the test set. The ground truth for technical performance would be established by physical measurements and engineering specifications, not expert consensus on clinical images.

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

   • No adjudication method is mentioned as the testing performed was bench testing on physical and technical performance, not human-in-the-loop diagnostic accuracy studies.

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 comparative effectiveness study was performed or is mentioned. This device is an X-ray system, not an AI-powered diagnostic tool. The submission is for a hardware modification (new detector) and supporting software updates to an existing X-ray system. Therefore, it does not involve AI assistance for human readers in the diagnostic process.

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

   • No standalone algorithm performance study was done. This is a physical imaging system, not a software algorithm that performs diagnostic analysis. The "bench testing" evaluated the system's inherent imaging capabilities.

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

   • The "ground truth" for the tests performed was based on objective physical measurements and engineering specifications for parameters like spatial resolution, low contrast resolution, dynamic range, artifacts, and contrast. It was not based on expert clinical consensus, pathology, or outcomes data, as those are typically relevant for diagnostic accuracy claims.

7. The sample size for the training set:

   • This submission describes a modification to an existing X-ray imaging system. There is no mention of a "training set" in the context of machine learning or AI. The system's operation is based on known physics and engineering principles, not a learned algorithm from a training dataset.

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

   • As there is no training set for a machine learning algorithm, this question is not applicable.

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