Search Results
Found 1 results
510(k) Data Aggregation
(29 days)
The AQUARIUS 8600 is intended for use by a qualified doctor or technologist on both adult and paediatric patients for taking diagnostic radiographic exposures of all body parts of the AQUARIUS 8600 provides digital image capture and is intended to replace radiographic film/screen. The x-ray tube and associated equipment are not provided with the proposed sensor. Prescription use only.
The AQUARIUS 8600 is not intended for mammography.
The Aquarius 8600 is a digital radiography sensor which automatically collects x-ray images from an x-ray source. The Aquarius 8600 sensor (flat panel type) collects x-rays and digitizes the images for their transfer and display to a computer. The sensor does not have an x-ray source, which is provided by independent manufacturers. The sensor includes with a flat panel for x-ray acquisition and digitization and a computer (including proprietary processing software) for processing, annotating and storing x-ray images.
This is a 510(k) premarket notification for the AQUARIUS 8600 Digital Radiography Sensor. The core of this submission is to demonstrate the substantial equivalence of the modified device to a previously cleared predicate device.
Based on the provided text, the acceptance criteria and the study proving the device meets those criteria are established through a non-clinical performance evaluation and image comparison rather than a human-in-the-loop clinical trial or a detailed algorithm performance study with predefined metrics like sensitivity/specificity. This is typical for certain types of device modifications or new devices where the primary claim is equivalence in image quality or basic functionality to an existing device.
Here's the breakdown of the information requested, based on the provided document:
1. A table of acceptance criteria and the reported device performance
The document does not explicitly state "acceptance criteria" in the traditional sense of numerical performance thresholds (e.g., specific sensitivity, specificity, or image quality scores). Instead, the acceptance criteria are implicitly met by demonstrating substantial equivalence to predicate devices in terms of:
| Acceptance Criteria Category | Reported Device Performance and Justification |
|---|---|
| Image Quality and Characteristics | "The Aquarius 8600 produces images of similar quality and characteristics that are equivalent to those of the both the Aquarius 8600 and BIOK4600 predicate devices." |
| Hardware Performance | "Because the sensor hardware is the same as the Aquarius 8600 predicate device hardware, the sensor has the same performance, biocompatibility, effectiveness, thermal, electrical and mechanical safety and is substantially equivalent to the predicate device." |
| Software Functionality | "The Aquarius 8600 software functionality is equivalent to the original BIOK4600 predicate device, except for the Sensor Driver interface application which reads the images from the Aquarius 8600 flat panel instead of the BIOK 4600 sensor." |
| Conformity to Standards | "The design, development and production of the sensor conforms to 892.1680 and ISO 13485 quality systems." |
| Technical Specifications (Physical/Operational) | Spatial resolution: 3.9 lp/mmOptical resolution: 3.9 lp/mmAcquisition to display time: < 2 secNetwork interface: 1000 mbps LANImage and file formats: DICOM compliantPower: 24Vdc, 1.9A max (18W typ)These specs implicitly met by equivalence to predicate. |
2. Sample size used for the test set and the data provenance
- Sample Size for Test Set: The document mentions "Test images have been submitted along with the equivalent images from the Aquarius 8600 predicate device." However, it does not specify the number of images or cases in this test set.
- Data Provenance: The provenance (country of origin, retrospective/prospective) of these "test images" is not specified in the provided text. It's likely that these were images generated internally or from previous testing since the hardware is effectively the same as a predicate.
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts
The document does not mention the use of experts in a formal ground truth establishment process for a test set. The assessment appears to be based on a direct comparison of physical image attributes and technical specifications against the predicate devices, rather than a clinical interpretation by experts.
4. Adjudication method for the test set
Given that formal expert review to establish ground truth is not mentioned, there is no stated adjudication method. The comparison seems to be based on the manufacturer's internal assessment of image similarity and technical equivalence.
5. If a multi-reader multi-case (MRMC) comparative effectiveness study was done, and 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 done or reported. This device is a digital radiography sensor (hardware and basic image acquisition/processing software), not an AI-powered diagnostic aid for interpretation. Therefore, the concept of human readers improving with AI assistance is not applicable here.
6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done
A "standalone" algorithm performance study in the sense of sensitivity, specificity, and other performance metrics calculated by an algorithm on a dataset was not performed or reported. The evaluation focused on the technical equivalence of the image acquisition system to previously cleared devices. The "algorithm" here is primarily within the proprietary processing software for image acquisition and digitization, not a diagnostic AI algorithm.
7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.)
The concept of "ground truth" as typically used for AI diagnostics (e.g., for disease detection) is not applicable here. The "ground truth" in this context is implicitly the established performance and image characteristics of the predicate devices. The new device is considered "equivalent" if its images and functionality align with what the predicate devices produce.
8. The sample size for the training set
This device is not an AI algorithm that undergoes "training" on a dataset in the typical machine learning sense. It's a digital radiography sensor with associated image processing software. Therefore, there is no concept of a training set in the provided document.
9. How the ground truth for the training set was established
As there is no training set for this type of device, this question is not applicable.
Ask a specific question about this device
Page 1 of 1