(103 days)
The Varian Nexus DR™ Digital X-ray Imaging System is a high resolution digital imaging system intended to replace conventional film techniques, or existing digital systems, in multipurpose or dedicated applications specified below. The Nexus DR™ Digital X-ray Imaging System enables an operator to acquire, display, process, export images to portable media, send images over a network for long term storage and distribute hardcopy images with a laser printer. I mage processing algorithms enable the operator to bring out diagnostic details difficult to see using conventional imaging techniques. Images can be stored locally for temporary storage. The major system components include an image receptor, computer, monitor and imaging software.
The Varian Nexus DR™ Digital X-ray Imaging System is intended for use in general radiographic examinations and applications (excluding fluoroscopy, angiography, and mammography).
The Varian Nexus DR™ Digital X-ray Imaging System is a high resolution digital imaging system designed for digital X-ray imaging through the use of an X-ray detector. The Varian Nexus DR™ Digital X-ray Imaging System is designed to support general radiographic (excluding fluoroscopy, angiography, and mammography) procedures through a single common imaging platform.
The modified device consists of an X-ray imaging receptor, Varian PaxScan 4336Wv4, computer, monitor, and the digital imaging software.
The provided document is a 510(k) premarket notification for the Nexus DR 100 Digital X-ray Imaging System (with PaxScan 4336Wv4). It focuses on establishing substantial equivalence to existing predicate devices.
Based on the provided text, the document primarily discusses non-clinical testing and general validation, rather than a specific study designed to meet predetermined acceptance criteria for a new AI or diagnostic algorithm's performance. The information requested in the prompt is highly relevant for studies proving the performance of AI/CADe/CADx devices. This submission, however, is for a digital X-ray imaging system, which is a hardware and software system for image acquisition and display, and not explicitly an AI-driven diagnostic tool in the sense of the prompt's questions.
Therefore, many of the questions regarding specific acceptance criteria for diagnostic performance, sample sizes for test sets, experts for ground truth, adjudication methods, MRMC studies, and training set details are not fully addressable from this document as it does not describe such a study for the device's diagnostic performance.
However, I can extract information related to the technological characteristics comparison which serves as a form of "acceptance criteria" for substantial equivalence.
1. A table of acceptance criteria and the reported device performance
For this 510(k) submission, "acceptance criteria" are not framed in terms of diagnostic performance metrics like sensitivity or specificity for a specific condition. Instead, the device's performance is compared against predicate devices based on technological characteristics and physical image quality parameters to demonstrate substantial equivalence. The "acceptance" is that these characteristics are equivalent or better than the predicates.
| Feature/Item | Predicate Device (Nexus DRFTM Digital X-ray Imaging System) | Predicate Device (Stingray DR Digital Radiographic System) | Subject Device (Nexus DRTM Digital X-ray Imaging System with PaxScan 4336Wv4) | Acceptance Criterion (Implicit for Substantial Equivalence) | Subject Device Performance (Reported) |
|---|---|---|---|---|---|
| Flat Panel Detector | Varian PaxScan 4343R | Trixell Pixium 4600 | Varian PaxScan 4336Wv4 | Comparable or improved detector technology | Varian PaxScan 4336Wv4 (Wireless with vTrigger) |
| Detector Material | a-Si sensor array with CsI or Gd2O2S:TB scintillator | a-Si sensor array with CsI scintillator | a-Si sensor array with CsI or Gd2O2S:TB scintillator | Comparable material used for X-ray detection | a-Si sensor array with CsI or Gd2O2S:TB scintillator |
| Detector Dimensions | 17" x 17" | 17" x 17" | 17" x 14" | Comparable or slightly different, maintaining intended use | 17" x 14" |
| Pixel Size | 139 x 139 microns | 143 x 143 microns | 139 x 139 microns | Comparable or smaller for higher resolution | 139 x 139 microns |
| Detector Element Matrix | 3072 x 3072 | 2981 x 3021 | 3072 x 2560 | Comparable or higher for better image detail | 3072 x 2560 |
| Dynamic Range | 14 bits | 14 bits | 16 bits | Comparable or higher for better contrast resolution | 16 bits |
| Uniform Density | 1.63 | N/A | 1.52 | Comparable or improved (lower variability implying better uniformity) | 1.52 |
| Spatial Resolution | 3.2 lp/mm | 3.51 lp/mm | 3.2 lp/mm | Comparable or better for detail visibility | 3.2 lp/mm |
| Sensitivity | 128 @ 1.1uGy/frame, ..., 3143 @ 30uGy/frame (14-bit) | N/A | 540 @ 1.1uGy/frame, ..., 12804 @ 30uGy/frame (16-bit) | Comparable or higher for better low-dose performance | Significantly higher (540 @ 1.1uGy/frame, 12804@ 30uGy/frame) (16-bit subject panel) |
| Signal to Noise Ratio | 67 @ 2.8uGy/frame, ..., 275 @ 50uGy/frame | N/A | 73 @ 2.8uGy/frame, ..., 285 @ 50uGy/frame | Comparable or higher for reduced noise | Higher (73 @ 2.8uGy/frame, 285 @ 50uGy/frame) |
| Modulation Transfer Function | 0.521 @ 1cycle/mm, ..., 0.08 @ 3cycles/mm | N/A | 0.551 @ 1cycle/mm, ..., 0.099 @ 3cycles/mm | Comparable or higher for better detail preservation | Higher (0.551 @ 1cycle/mm, 0.099 @ 3cycles/mm) |
| Detective Quantum Efficiency | 0.242 @ 1cycle/mm, ..., 0.04 @ 3cycles/mm | N/A | 0.232 @ 1cycle/mm, ..., 0.07 @ 3cycles/mm | Comparable or higher for overall image quality and dose efficiency | Comparable (0.232 @ 1cycle/mm, 0.07 @ 3cycles/mm) |
| Total Image Processing Time | 10 seconds per image | 30 seconds per image | 10 seconds per image | Comparable or faster | 10 seconds per image |
2. Sample size used for the test set and the data provenance (e.g., country of origin of the data, retrospective or prospective)
The document primarily describes non-clinical tests and a technological characteristics comparison to establish substantial equivalence. It refers to "Validation Protocols" and "predetermined test methods and corresponding acceptance criteria" but does not detail a specific "test set" of clinical images or patients in the sense of a diagnostic performance study. The data presented is characteristic measurements of the detector and system, not image data from patients for a diagnostic evaluation.
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts
Not applicable. The document does not describe a clinical study where experts established ground truth for diagnostic decisions.
4. Adjudication method (e.g., 2+1, 3+1, none) for the test set
Not applicable. This type of adjudication method is used in diagnostic performance studies, which are not detailed in this submission.
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
Not applicable. This submission is for a digital X-ray imaging system, not an AI-assisted diagnostic device, and thus no MRMC study for AI assistance is described.
6. If a standalone (i.e., algorithm only without human-in-the-loop performance) was done
Not applicable, as this device is an imaging system and not primarily a standalone diagnostic algorithm.
7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.)
Not applicable in the context of a diagnostic performance study. The "ground truth" for the non-clinical tests would be the measured physical properties of the system and detector according to standard testing methodologies (e.g., those detailed in the referenced FDA guidance for solid-state X-ray imaging devices).
8. The sample size for the training set
Not applicable. The document does not describe an AI/ML component with a "training set" for diagnostic performance. The device involves image processing algorithms, but these are typically deterministic or rule-based for image enhancement, not machine learning algorithms trained on large datasets for diagnostic classification.
9. How the ground truth for the training set was established
Not applicable, as there is no mention of a training set for a diagnostic AI/ML algorithm.
§ 892.1680 Stationary x-ray system.
(a)
Identification. A stationary x-ray system is a permanently installed diagnostic system intended to generate and control x-rays for examination of various anatomical regions. This generic type of device may include signal analysis and display equipment, patient and equipment supports, component parts, and accessories.(b)
Classification. Class II (special controls). A radiographic contrast tray or radiology diagnostic kit intended for use with a stationary x-ray system only is exempt from the premarket notification procedures in subpart E of part 807 of this chapter subject to the limitations in § 892.9.