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510(k) Data Aggregation
(30 days)
Aquilion Lightning, TSX-036A/1, V8.4
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.
| Performance Metric | Reported Device Performance |
|---|---|
| Spatial Resolution | Demonstrated substantial equivalence to predicate device |
| CT Number Magnitude and Uniformity | Demonstrated substantial equivalence to predicate device |
| Noise Properties | Demonstrated substantial equivalence to predicate device |
| Low Contrast Detectability | Demonstrated substantial equivalence to predicate device |
| CNR Performance | Demonstrated substantial equivalence to predicate device |
| Diagnostic Image Quality (overall) | Produces images of diagnostic quality for head, chest, abdomen, pelvis, peripheral exams |
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).
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