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510(k) Data Aggregation
(23 days)
R-Sensor, R-Sensor
R-Sensor is used for a radiographic examination by a dental professional to assist in the diagnosing of diseases of the teeth, jaw and oral structures.
Radiographic examination to assist with diagnosis of diseases of the teeth, jaw, and oral structure.
The R-Sensor dental sensor is intended to replace film and to capture an intraoral x-ray image, when exposed to X-rays, for dental diagnostic purposes.
The subject R-Sensor is an intraoral digital x-ray system comprised of two components: (1) an intraoral detector which connects to a PC via a USB port; and (2) an Image Management Software package.
The subject devices comes in two sizes: Size 1 is 600mm² and Size 2 is 884mm².
R-Sensor, Size 1 is also known as factory code S11684-12; R-Sensor, Size 2 is also known as factory code S116845-12.
The provided text describes the "R-Sensor," an intraoral digital x-ray sensor, and its substantial equivalence to a predicate device, the "QuickRay HD." The document primarily focuses on demonstrating that the R-Sensor is identical to the predicate device, rather than presenting a standalone study with specific acceptance criteria and performance metrics for the R-Sensor itself.
Therefore, the information below is derived from the comparison to the predicate and the general statements about the device's characteristics, as the document states the two devices are identical in all aspects.
1. Table of Acceptance Criteria and Reported Device Performance
Since this submission argues for substantial equivalence based on the R-Sensor being identical to the predicate device (QuickRay HD), the "acceptance criteria" are implicitly met by matching the predicate's specifications and performance where specified. The document does not explicitly state unique acceptance criteria for the R-Sensor but rather focuses on demonstrating its identical characteristics to the cleared predicate.
| Characteristic | Acceptance Criteria (Implied from Predicate & Standards) | Reported Device Performance (R-Sensor) |
|---|---|---|
| Resolution | Real ≥ 20 lp/mm (from predicate) | Real ≥ 20 lp/mm |
| Pixel size | 20 x 20μm (from predicate) | 20 x 20μm |
| Grey levels | 14 bits (from predicate) | 14 bits |
| Lifespan CMOS | Min. 100,000 cycles (from predicate) | Min. 100,000 cycles |
| Electrical Safety | Conforms to IEC 60601-1 | Conforms to IEC 60601-1 |
| EMC | Conforms to IEC 60601-1-2 | Conforms to IEC 60601-1-2 |
| Image Quality / DQE | Conforms to IEC 62220-1 | Bench tests performed in conformance with IEC 62220-1 |
| Ingress Protection (IP Code) | Conforms to IEC 60529 | Bench tests performed in conformance with IEC 60529 |
| Biocompatibility | No direct/indirect patient-contacting components (addressed by single-use barrier) | Biocompatible testing not warranted due to single-use protective barrier. |
| Diagnostic Relevance | Images diagnostically relevant and reliable (based on clinical images of the "complete system" - likely the predicate or very similar system) | Clinical images found to be diagnostically relevant and reliable. |
2. Sample size used for the test set and the data provenance
The document mentions "Clinical images were provided" for the R-Sensor to demonstrate that "the complete system works as intended." However, it explicitly states these images "were not necessary to establish substantial equivalence based on the modifications to the predicate device but they provide further evidence in addition to bench testing data." This suggests the primary "test set" for equivalence relies on the established performance of the predicate device and bench testing of the R-Sensor's components.
- Sample size for clinical images: Not specified.
- Data provenance: Not specified, but a qualified practitioner in Ormond Beach, FL reviewed them. It's unclear if these were retrospective or prospective, or from what country of origin beyond the U.S. location of the reviewing practitioner.
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts
- Number of experts: One expert is explicitly mentioned.
- Qualifications of experts: Dr. Parham, a "qualified practitioner in Ormond Beach, FL." Specific specialty (e.g., dentist, radiologist) or years of experience are not provided.
4. Adjudication method for the test set
Not applicable/Not specified. The clinical images were "examined by Dr. Parham" and "found to be diagnostically relevant and reliable," suggesting a single-reader assessment rather than an adjudication process involving multiple experts.
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. This document does not mention an MRMC comparative effectiveness study or any AI component. The R-Sensor is described as a hardware device (intraoral sensor) with associated image management software, performing as a receiver of X-rays to capture images.
6. If a standalone (i.e., algorithm only without human-in-the-loop performance) was done
A standalone performance assessment of the R-Sensor hardware was performed through bench testing conforming to IEC 62220-1 (for DQE, influencing image quality) and other standards (IEC 60601-1, IEC 60601-1-2, IEC 60529). The "algorithm" here refers to firmware and driver, which are part of the sensor's electronics, and image management software. The entire system's performance, including software, is presented as being identical to the predicate.
7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.)
For the "clinical images" section, the ground truth was established by the assessment of a single "qualified practitioner" who deemed them "diagnostically relevant and reliable." For the direct comparison, the "ground truth" for substantial equivalence is the previously cleared predicate device, QuickRay HD, and its established performance and adherence to standards.
8. The sample size for the training set
Not applicable. The document describes a medical device seeking 510(k) clearance based on substantial equivalence to an existing predicate. It does not describe an AI/ML device that requires a training set. The firmware and drivers were provided by Hamamatsu, and the image management software (Xray Vision) is an off-the-shelf package cleared previously (K983111).
9. How the ground truth for the training set was established
Not applicable, as there is no mention of a training set for an AI/ML algorithm.
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