(56 days)
These Radiographic Systems are intended for use by a qualified/trained doctor or technician on both adult and pediatric subjects for taking diagnostic radiographic exposures of the skull, spinal column, chest, abdomen, extremities, and other body parts. Applications can be performed with the patient sitting, standing, or lying in the prone or supine position..
The Rad Vision E is a standard configuration fixed column diagnostic radiographic system. The column can move right or left on a track and the tube head can move up and down. Rad Vision eu is a universal swivel arm X-ray system. It is easy to operate and permits a swift radiographic procedure, a feature which applies to all conventional exposure techniques on all parts of the body. The system is composed of a floor-to-wall column and a turnable arm. On the arm is the tubehead with a collimator mounted to it. All components required for a complete system are supplied. With the patient table, the patient can be moved into any required position without the need for repositioning. Therefore it offers the same advantages as a bucky radiography table. Owing to the large vertical movement of the swivel arm patients in the standing position can be examined from head to feet.
The provided text is a 510(k) summary for the Rad Vision E and Rad Vision eu Diagnostic X-Ray Systems, along with the FDA's clearance letter. This document focuses on the substantial equivalence of the device to a predicate device, rather than a detailed study proving performance against specific acceptance criteria for a novel AI/software medical device.
Therefore, many of the requested elements (like a table of acceptance criteria and proven performance, sample sizes for test/training sets, expert qualifications, adjudication methods, MRMC study results, or detailed ground truth information) are not typically found in this type of 510(k) submission for a conventional X-ray system. These aspects are more central to the regulatory submission for AI/ML-driven or image-analysis software devices that require validation of diagnostic accuracy.
The document indicates "The results of bench, test laboratory and clinical testing indicates that the new device is as safe and effective as the predicate devices," but does not elaborate on the specifics of these tests or acceptance criteria beyond general safety and effectiveness in comparison to the predicate.
Here's an attempt to answer the questions based only on the provided text, acknowledging that much of the requested information is not present:
1. A table of acceptance criteria and the reported device performance
The document does not explicitly state specific quantitative acceptance criteria or detailed reported device performance metrics in the format of a table you'd expect for an AI/software device. The overarching "acceptance criterion" inferred is that the new device is as safe and effective as the predicate device.
| Acceptance Criterion | Reported Device Performance |
|---|---|
| Safety and Effectiveness equivalent to predicate device | "The results of bench, test laboratory and clinical testing indicates that the new device is as safe and effective as the predicate devices." (No specific metrics provided). |
2. Sample sized used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)
Not explicitly stated in the provided text. The document refers to "clinical testing" but does not provide details on sample size, data provenance, or study design (retrospective/prospective).
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g. radiologist with 10 years of experience)
Not applicable or not explicitly stated. For a conventional X-ray system submission, the "ground truth" relates more to the physical performance and image quality of the X-ray machine itself, typically evaluated through engineering tests, phantom studies, and possibly clinical trials (without needing a separate expert panel to establish "ground truth" for diagnostic images in the way an AI algorithm would).
4. Adjudication method (e.g. 2+1, 3+1, none) for the test set
Not applicable or not explicitly stated.
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 such MRMC comparative effectiveness study is mentioned. This type of study is relevant for AI-assisted diagnostic tools, not typically for the clearance of a conventional X-ray hardware system.
6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done
This question is not applicable as the device is a diagnostic X-ray system (hardware), not an algorithm or software-only device.
7. The type of ground truth used (expert consensus, pathology, outcomes data, etc)
For a conventional X-ray system, "ground truth" in terms of diagnostic images is less about establishing a definitive diagnosis on images and more about ensuring the system produces diagnostically acceptable images, measured by objective image quality metrics, dose, and clinical utility. The document does not specify the method for establishing ground truth for any "clinical testing" mentioned.
8. The sample size for the training set
Not applicable. The device is a hardware X-ray system; it does not involve a "training set" in the context of machine learning.
9. How the ground truth for the training set was established
Not applicable, as there is no training set for this type of device.
§ 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.