(235 days)
The SCENARIA Phase 3 system is indicated for head, whole body, cardiac and vascular X-ray Computed Tomography applications in patients of all ages. The images can be acquired in either axial, helical, gated or dynamic modes.
The volume datasets acquired by the SCENARIA Phase 3 can be post processed by the SCENARIA Phase 3 to provide additional information. Post processing capabilities included in the SCENARIA Phase 3 software include CT angiography (CTA), Multi-planar reconstruction (MPR) and volume rendering.
Volume datasets acquired by the SCENARIA Phase 3 can be transferred to external devices via a DICOM standard interface.
The guideShot Option adds a remote in-room display and controls to support interventional imaging. The device output can provide an aid to diagnosis when used by a qualified physician.
The SCENARIA Phase 3 is a multi-slice computed tomography system that uses x-ray data to produce cross-sectional images of the body at various angles. The system consists of a Gantry, Operator's Workstation, Patient Table, High-Frequency X-ray Generator, and accessories. The system uses 128-slice CT technology, where the X-ray tube and detector assemblies are mounted on a frame that rotates continuously around the patient using slip ring technology. The solid-state detector assembly design collects up to 64 slices of data simultaneously. The X-ray sub-system features a high frequency generator, X-ray tube, and collimation system that produces a fan beam X-ray output. The system can operate in a helical (spiral) scan mode where the patient table moves during scanning. As the X-ray tube/detector assembly rotates around the patient, data is collected at multiple angles. The collected data is then reconstructed into cross-sectional images by a high-speed reconstruction sub-system. The images are displayed on a Computer Workstation, stored, printed, and archived as required. The workstation is based on current PC technology using the Windows™ operating system.
The provided text describes the 510(k) premarket notification for the HITACHI SCENARIA Phase 3 Whole-body X-ray CT System, asserting its substantial equivalence to a predicate device (SCENARIA Phase 2 Whole-body X-ray CT System, K123509). The document focuses on demonstrating that the new device has comparable performance and technological characteristics to the predicate device, rather than proving that it meets specific, quantitative acceptance criteria for diagnostic accuracy through a controlled study.
Therefore, many of the requested items (e.g., sample size for test set, ground truth experts, adjudication methods, MRMC study, standalone performance, training set size) are not explicitly detailed in the provided document, as the submission strategy is based on demonstrating substantial equivalence to a previously cleared device.
Here's an analysis based on the available information:
1. Table of Acceptance Criteria and Reported Device Performance
The acceptance criteria are not presented in a traditional "pass/fail" format with numerical thresholds for diagnostic accuracy metrics. Instead, the "acceptance criteria" are implied by the demonstration of comparable performance to the predicate device for various technical parameters and a judgment of clinical usability of example images.
| Testing Type | Acceptance Criteria (Implied) | Reported Device Performance |
|---|---|---|
| Performance Testing - Bench | Meeting the conditions of 21 CFR 1020.33(c) or (g) for specific parameters, and demonstrating equivalent basic performance as the predicate device (SCENARIA Phase 2). This includes: Dose Profile, Noise, Mean CT number and Uniformity, Spatial Resolution, Tomographic Section Thickness and Sensitivity Profile, Tomographic Plane Location, CT dose index. | Confirmed that the tested items (Dose Profile, Noise, Mean CT number and Uniformity, Spatial Resolution, Tomographic Section Thickness and Sensitivity Profile, Tomographic Plane Location, CT dose index) met the conditions of 21 CFR 1020.33(c) or (g). Showed equivalent basic performance as the predicate device. |
| Performance Testing - Clinical Usability | Sufficient clinical image examples to judge clinical usability, covering the general anatomy outlined in the indications for use, and comparable to the anatomy examples provided for the predicate device. | Five clinical image examples were provided and judged to be sufficient to judge clinical usability. These examples covered general anatomy and were comparable to the predicate. |
| Technological Characteristics | The technological characteristics should not impact safety and effectiveness, and the device should be generally equivalent to the predicate device in terms of design features, fundamental scientific technology, indications for use, and safety and effectiveness. Any differences from the predicate device (e.g., in reconstruction time, dose controls, or specific features) must be analyzed to show no significant changes in technological characteristics or new intended use, and that they do not affect system image performance. | The analysis concluded that the new features and minor differences (e.g., reduced reconstruction time, new IntelliEC Plus, ECG Dose Modulation, Access Control, Preview Scan, Priority Recon, Exam Split, Quality Exam) do not constitute a new intended use, a significant change in technological characteristics, or do not affect system image performance. |
2. Sample Size Used for the Test Set and Data Provenance
- Sample Size for Test Set: "Five clinical image examples" were provided for the clinical evaluation. This is a very small sample, used for qualitative judgment of usability and comparability, not for statistical performance evaluation.
- Data Provenance: The document does not specify the country of origin of the clinical data. It is implied these were collected as part of the device's development or validation. It is a prospective collection for the purpose of this submission as "Clinical images were collected and analyzed."
3. Number of Experts Used to Establish the Ground Truth for the Test Set and Qualifications of Those Experts
The document states: "We provide five clinical image examples which we judged to be sufficient to judge a clinical usability." It does not specify the number or qualifications of the individuals who performed this judgment.
4. Adjudication Method (for the test set)
No adjudication method is described. The text only mentions that "we judged" the examples to be sufficient.
5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study
No MRMC comparative effectiveness study was mentioned or performed to assess human reader improvement with or without AI assistance. The device is a CT system itself, not an AI-assisted diagnostic tool that augments human interpretation in the sense of comparing human performance with and without an AI algorithm.
6. Standalone (Algorithm Only) Performance
This document describes a CT imaging system. The performance metrics reported (e.g., spatial resolution, low-contrast resolution) are for the system's image acquisition and reconstruction capabilities themselves, which represent its "standalone" performance as an imaging device. The device itself is the "algorithm only" in the context of image generation. It's not an AI diagnostic algorithm that processes images; it's the image acquisition and processing hardware and software for a CT scanner.
7. Type of Ground Truth Used
For the clinical evaluation, the "ground truth" was a subjective assessment of "clinical usability" and "comparability" to the predicate device's examples. For bench testing, the ground truth was based on adherence to regulatory standards (21 CFR 1020.33) and technical specifications (e.g., IEC standards, NEMA standards) for image quality parameters (dose, noise, spatial resolution, etc.), established through phantom studies and measurements.
8. Sample Size for the Training Set
There is no mention of a "training set" in the context of machine learning, as this device is a CT imaging system and not primarily an AI algorithm for diagnostic interpretation that would require a distinct training set of labeled data in the sense of deep learning. The "training" for the device's image reconstruction algorithms would be inherent in its design and engineering based on physics and mathematical principles.
9. How the Ground Truth for the Training Set Was Established
Not applicable, as a traditional machine learning training set with external ground truth labels is not described or relevant for this type of device submission. The device's performance is validated against physical and regulatory standards.
§ 892.1750 Computed tomography x-ray system.
(a)
Identification. A computed tomography x-ray system is a diagnostic x-ray system intended to produce cross-sectional images of the body by computer reconstruction of x-ray transmission data from the same axial plane taken at different angles. This generic type of device may include signal analysis and display equipment, patient and equipment supports, component parts, and accessories.(b)
Classification. Class II.