Search Results

ReadeR is a dental imaging system indicated for capturing, digitization and processing of intra oral x-ray images stored in imaging plate recording media.

ReadeR system is intended to be used only by dentist and other qualified dental professionals to process x-ray images exposed to the imaging plates from the intraoral complex of the skull.

The ReadeR is a computer radiography system which produces the x-ray diagnostic image in digital format instead of using traditional screens and film. This device does not have a wireless transmission. The device utilizes reusable x-ray storage phosphor plate (IP) that is sensitive to x-ray and stores latent image when it is exposed to x-ray. After x-ray exposure to the x-ray storage phosphor plate, x-ray storage phosphor plate is scanned by means of laser in the device. Latent image in the x-ray storage phosphor plates is released in a form of light by laser scanning. Then the light is collected and converted into a form of digital image. The signal processing is made to the digital image data such as the digital filtering, the gain and offset correction and flat fielding. The image can then be viewed on a computer workstation, adjusted if necessary, the stored locally, sent to an archive, printed or sent to PACS system.

After acquisition of the latent image from the x-ray storage phosphor plate, it is erased thoroughly to be reused.

The provided text is a 510(k) summary for the Trident s.r.l. device, ReadeR, VieweR, QuickScan PSP. It asserts substantial equivalence to a predicate device, the NICAL SMART MICRO CR System. However, it does not contain information about a study proving the device meets acceptance criteria in the way typically expected for a new device's performance claims.

Instead, the document's core argument for substantial equivalence is that the subject device is, in essence, the same as the predicate device with minor external differences and re-branding. This means the acceptance criteria and performance are essentially inherited from the predicate device.

Here's an attempt to extract and format the information as requested, with caveats due to the nature of the document:

1. Table of Acceptance Criteria and Reported Device Performance

Since this is a substantial equivalence submission based on the subject device being "the same" as the predicate, there are no specific "acceptance criteria" for the subject device beyond matching the predicate's specifications. The reported performance is essentially identical to the predicate.

2. Sample Size Used for the Test Set and Data Provenance

• Sample Size for Test Set: The document mentions "Clinical images were provided" but explicitly states they "were not necessary to establish substantial equivalence" and that "bench performance data" was the primary basis. No specific sample size for a test set of clinical images used for a performance study is provided.
• Data Provenance: Not specified for any clinical images provided. The bench testing (e.g., electrical safety, EMC, software validation) was performed by laboratories in Italy and the USA.

3. Number of Experts Used to Establish the Ground Truth for the Test Set and Qualifications of Those Experts

• Not applicable. The document relies on the predicate device's established performance and bench testing of the subject device. There is no mention of expert-established ground truth for a clinical test set for the ReadeR device itself.

4. Adjudication Method for the Test Set

• Not applicable, as no described clinical performance test set requiring adjudication is detailed.

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 MRMC or comparative effectiveness study is mentioned. This device is an imaging system, not an AI-powered diagnostic tool, and the focus is on substantial equivalence to an existing device rather than reader improvement with AI.

6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) was done

• Not applicable. This is a hardware imaging system with associated software, not a standalone algorithm.

7. The Type of Ground Truth Used

• For the technical specifications, the "ground truth" is the established technical specifications and performance of the legally marketed predicate device, backed by bench test results for the subject device.
• "Clinical images were provided" but their role in establishing "ground truth" or performance criteria for this 510(k) is downplayed.
• No pathology or outcomes data is referenced for the subject device.

8. The Sample Size for the Training Set

• Not applicable. This document describes a medical imaging device, not an AI/machine learning model that would typically have a "training set."

9. How the Ground Truth for the Training Set was Established

• Not applicable, as there is no training set mentioned for an algorithm.

Ask a specific question about this device