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This system is intended to collect dental x-ray photons and convert them into electronic impulses that may be stored, views and manipulated for diagnostic use by dentists.

RIOSensor(Model RIS500) is intended to acquire real-time, clinical digital intraoral X-ray images using a solid-state imaging sensor. This system consists of the CMOS sensor and software for image display. This system senses the onset of the X-ray exposure and automatically acquires and save the image data to a PC (software).

Here's a breakdown of the acceptance criteria and study information for the RIO Sensor (RIS 500) based on the provided document:

This device is an intraoral imaging unit (dental X-ray sensor), and the focus of the performance evaluation is on its imaging characteristics, rather than diagnostic AI performance. Therefore, many typical AI-related criteria like MRMC studies, effect size of AI, and ground truth for disease detection are not applicable here. The evaluation centers on the physical and technical performance of the X-ray sensor itself.

1. Table of Acceptance Criteria and Reported Device Performance

Note: The predicate devices have slightly different sizing conventions (e.g., "Size 1.5", "Size 2.0") which makes a direct pixel matrix comparison challenging without knowing the exact dimensions corresponding to "Size 1" and "Size 2" for RIS500 relative to the predicates. However, the theoretical resolution, MTF, and DQE are directly comparable and are the primary performance metrics. The RIS500 meets or exceeds the key imaging performance criteria of the predicate devices.

2. Sample Size for the Test Set and Data Provenance

The document primarily describes bench testing and non-clinical testing for evaluating the technical performance of the imaging device. It does not mention a "test set" in the context of clinical images for diagnostic evaluation, as there is no AI algorithm being evaluated for diagnostic efficacy.

• Bench Testing: Used to assess whether parameters related to imaging properties and patient dosage satisfy designated tolerances. The sample size for this is not explicitly stated but implies testing a sufficient number of devices or iterations to demonstrate compliance with standards.
• Non-Clinical Testing: Included MTF and DQE of the detector. No specific "sample size" of images is mentioned beyond the testing methodology itself.
• Clinical Considerations: "For clinical testing, two licensed practitioners/clinicians observed and verified that Intraoral Imaging Unit from RIOSensor (Model name: RIS500)." This suggests a qualitative observation by clinicians, not a quantitative study with a defined image test set.
• Data Provenance: Not applicable in the traditional sense for an AI model. The tests are on the device's hardware performance.

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

• Ground Truth for Technical Performance: The "ground truth" for the technical performance metrics (MTF, DQE, theoretical resolution) is established by the performance testing standards themselves (e.g., IEC 61223-3-4). These are objective measurements against established physical standards.
• Clinical Observation: Two "licensed practitioners/clinicians" observed and verified the device. Their specific qualifications (e.g., years of experience, specialization) are not detailed beyond being "licensed practitioners/clinicians." This was for general verification, not for establishing diagnostic ground truth on a specific test set of cases.

4. Adjudication Method for the Test Set

Not applicable. There was no diagnostic test set requiring adjudication from multiple experts. The "clinical considerations" involved observation and verification by two practitioners, but not an adjudication process as understood in AI studies for ground truth establishment.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was done, Effect Size

No, an MRMC comparative effectiveness study was not done. The device is a direct X-ray sensor, not an AI-assisted diagnostic tool.

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

Not applicable. This device is the X-ray sensor itself; it's not a standalone AI algorithm. Its performance is its ability to acquire and convert X-ray photons into electronic impulses for display.

7. The Type of Ground Truth Used

For the technical performance of the device (MTF, DQE, resolution), the ground truth is established by objective physical measurements against industry standards (e.g., IEC 61223-3-4). This is a form of scientific/engineering ground truth.

8. The Sample Size for the Training Set

Not applicable. This is an X-ray sensor, not an AI model that requires a training set.

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

Not applicable, as there is no AI model or training set.

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