Search Results

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. These volume sets can be used to perform specialized studies, using indicated software/hardware, by a trained and qualified physician.

The Aquilion Lightning, TSX-036A/1, v8.4 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 Aquilion Lightning, TSX-036A/1, V8.4 CT system. As such, it focuses on demonstrating substantial equivalence to a predicate device rather than presenting a detailed study with specific acceptance criteria and performance metrics for a novel AI-powered diagnostic device.

Therefore, much of the requested information, particularly regarding acceptance criteria for diagnostic performance, sample sizes for test sets in an AI context, expert ground truth establishment, MRMC studies, and standalone AI performance, is not present in this document because it describes a computed tomography x-ray system, not an AI software.

However, I can extract information related to the device's technical specifications and how its performance was assessed for regulatory clearance.

Here's a breakdown of the available information based on your request:

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

The document doesn't define specific quantitative "acceptance criteria" in the typical sense of a diagnostic performance study (e.g., sensitivity, specificity thresholds). Instead, it states that the device was evaluated against performance metrics relevant to CT image quality and found to be "substantially equivalent" to the predicate.

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

• Test Set Sample Size: Not specified in terms of a patient cohort. The testing involved "representative diagnostic images" and phantom studies.
• Data Provenance: Not explicitly stated but implies images were generated by the device itself and likely from standard clinical scenarios (retrospective or prospective is not specified).

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 Experts: An "American Board Certified Radiologist."

4. Adjudication method for the test set:

Not applicable/specified. The document states a single American Board Certified Radiologist reviewed representative diagnostic images. There is no mention of a multi-reader adjudication process for establishing ground truth for a test set in the context of diagnostic performance.

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 document does not mention an MRMC comparative effectiveness study, nor does it discuss AI assistance for human readers. This device is an imaging system, not an AI diagnostic tool.

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

No. This is a CT imaging system. The performance assessment relates to the image acquisition and display capabilities, not a standalone algorithm.

7. The type of ground truth used:

• For CT Image Quality metrics (phantom studies): The ground truth is the physical properties of the phantoms and established CT physics principles for measuring image quality.
• For diagnostic image quality: Expert opinion of an American Board Certified Radiologist ("produces images of diagnostic quality").

8. The sample size for the training set:

Not applicable. This document describes a CT scanner, not an AI algorithm that requires a training set in the typical sense. The "training" of the system involves its design, manufacturing under quality systems, and adherence to engineering specifications.

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

Not applicable. (As above, not an AI algorithm with a training set).

Ask a specific question about this device

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. These volume sets can be used to perform specialized studies, using indicated software/hardware, by a trained and qualified physician.

The Aquilion Lightning, TSX-035A/4 and /5, v7.0 is a 16-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.

I am sorry, but the provided text is a 510(k) premarket notification for a Computed Tomography (CT) X-ray system (Aquilion Lightning, TSX-035A/4 and /5, V7.0). This type of document is a submission to the FDA to demonstrate that a device is substantially equivalent to a predicate device already on the market.

This specific document does not contain information about the acceptance criteria or a study proving the device meets acceptance criteria in the manner requested (e.g., using metrics like sensitivity, specificity, or performance against a ground truth dataset).

The document primarily focuses on:

• Indications for Use: What the device is intended for.
• Technological Characteristics Comparison: How the new device differentiates from its predicate (e.g., gantry rotation speed, X-ray rated output, patient couch specifications).
• Safety and Performance Standards Conformance: Listing of relevant IEC standards and CFR parts that the device adheres to.
• Testing: Mentions "summary tables detailing the risk analysis and verification/validation testing conducted through bench testing" and "successful completion of software validation." It does not provide details of such studies or specific performance metrics that would be considered acceptance criteria for AI/algorithm performance.

Therefore, I cannot provide the requested information about acceptance criteria, device performance, sample sizes, expert ground truth, adjudication methods, MRMC studies, standalone performance, or training set details because these are not present in the provided text. This document is for a general CT system, not an AI-powered diagnostic algorithm with performance metrics relative to a ground truth dataset.

Ask a specific question about this device