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510(k) Data Aggregation
(88 days)
This device is indicated to acquire and display cross sectional volumes of the whole body, to include the head, with the capability to image whole organs in a single rotation. Whole organs include but are not limited to brain, heart, pancreas, etc.
The Aquilion ONE has the capability to provide volume sets of the entire organ. These volume sets can be used to perform specialized studies, using indicated software/hardware, of the whole organ by a trained and qualified physician.
The Aquilion ONE Vision, TSX-301C/3 and 301C/4, v6.00 are 320-row CT Systems and the TSX-301C/5, v6.00 is a 160-row CT system consisting of the same gantry, couch and console used for data processing and display. These devices capture cross sectional volume data sets used to perform specialized studies, using indicated software, by a trained and qualified physician. These systems are based upon the technology and materials of previously marketed Toshiba CT systems.
The provided 510(k) summary focuses on demonstrating substantial equivalence of a modified CT system (Aquilion ONE Vision, TSX-301C/3, 301C/4 and 301C/5, v6.00) to its predicate device. This submission is for a hardware and associated software modification to an existing CT scanner, not a new AI/CADe device. Therefore, many of the typical acceptance criteria and study elements pertinent to AI systems that you requested are not directly applicable or explicitly detailed in this document.
However, I can extract the information that is present and explain why some of your requested details might not be found in this type of submission.
1. Table of Acceptance Criteria and Reported Device Performance
For this type of device modification, the "acceptance criteria" are generally related to demonstrating that the modified device performs at least as well as the predicate device in terms of image quality and safety, and continues to meet relevant standards. The performance is assessed through various tests, primarily utilizing phantoms and review by an expert.
| Performance Metric | Acceptance Criteria (Implied) | Reported Device Performance |
|---|---|---|
| Detector Sensitivity | Improvement compared to predicate device. | Demonstrated an improvement. |
| Spatial Resolution | Substantially equivalent to predicate device (via phantom testing). | Validated that the subject device is substantially equivalent to the predicate device. |
| CT Number | Substantially equivalent to predicate device (via phantom testing). | Validated that the subject device is substantially equivalent to the predicate device. |
| Contrast-to-Noise Ratio (CNR) | Substantially equivalent to predicate device (via phantom testing). | Validated that the subject device is substantially equivalent to the predicate device. |
| Noise Properties | Substantially equivalent to predicate device (via phantom testing). | Validated that the subject device is substantially equivalent to the predicate device. |
| Uniformity Performance | Substantially equivalent to predicate device (via phantom testing). | Validated that the subject device is substantially equivalent to the predicate device. |
| Diagnostic Image Quality | Produce images of diagnostic quality. | Representative diagnostic images, including brain, chest, abdomen, and peripheral exams, were obtained using the subject device and reviewed by an American Board Certified Radiologist, demonstrating that the device produces images of diagnostic quality and performs as intended. |
| Compliance with Standards | Conformance to applicable regulatory and performance standards. | Conforms to Quality System Regulations (21 CFR § 820, ISO 13485), applicable IEC standards (IEC60601 series, IEC62304, IEC62366), NEMA standards (PS 3.1-3.18, XR-25, XR-26), and radiation safety standards (21 CFR §1010 and §1020). |
| Software Validation | Successful completion per FDA guidance. | Successful completion of software validation for a Moderate Level of Concern, per FDA guidance. |
| Risk Management & Design Controls | Application of appropriate methodologies. | Application of risk management and design controls. |
2. Sample size used for the test set and the data provenance
- Sample Size for Test Set: The document mentions "representative diagnostic images" but does not specify a numerical sample size for the clinical image review. This type of submission often relies on a qualitative assessment of a small, representative set of images rather than a large statistical study.
- Data Provenance: Not explicitly stated (e.g., country of origin). Since it's a Toshiba America Medical Systems submission, the testing would likely have occurred in the US or Japan. The assessment of diagnostic images by an "American Board Certified Radiologist" suggests at least some of the data review, if not acquisition, was US-based.
- Retrospective or Prospective: Not specified. Given the nature of a device modification test, it could involve prospective acquisition of new images for evaluation, or retrospective review of images acquired with the modified device.
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts
- Number of Experts: "an American Board Certified Radiologist" (singular).
- Qualifications: "American Board Certified Radiologist." The duration of experience is not specified.
4. Adjudication method for the test set
- Adjudication Method: Not applicable or specified. With only one radiologist reviewing, there is no inter-reader discrepancy to adjudicate.
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, an MRMC comparative effectiveness study was not done. This device is a CT scanner itself, not an AI/CADe accessory intended to assist human readers in image interpretation. Therefore, assessing human reader improvement with/without "AI assistance" is not relevant to this submission.
6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done
- Standalone Performance: Not applicable. This is a conventional CT imaging system, not an algorithm being evaluated for standalone performance. The "softwar" mentioned refers to control software and image processing pathways within the CT system, not an independent AI diagnostic algorithm.
7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.)
- Type of Ground Truth: For the image quality assessment, the "ground truth" was the qualitative judgment of an "American Board Certified Radiologist" that the images were of "diagnostic quality" and that the device "performs as intended." For the phantom studies, the ground truth is against known physical properties and measurements within the phantoms, assessed by quantitative metrics (spatial resolution, CT number, CNR, noise, uniformity).
8. The sample size for the training set
- Sample Size for Training Set: Not applicable/not provided. This submission is for a hardware and control software modification to a CT scanner. The concept of a "training set" in the context of machine learning (AI) does not apply here. The system's performance is based on engineered design and physical principles, not on being trained on a dataset of images with ground truth labels.
9. How the ground truth for the training set was established
- How Ground Truth for Training Set was Established: Not applicable, as there is no "training set" in the AI sense for this device. The system's operational parameters and calibration are established through design specifications, factory calibration, and quality control processes.
In summary: This 510(k) submission is for a modification to a general-purpose CT imaging system. The performance evaluation focuses on demonstrating that the modified hardware and software maintain or improve the fundamental imaging capabilities and safety profiles compared to the predicate device, primarily through phantom testing and qualitative clinical image review by a radiologist. It does not involve the a-typical AI/CADe specific study designs and ground truth methodologies you would expect for an AI-powered diagnostic tool.
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