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Digital Dental Intra Oral Sensor is intended to collect dental x-ray photons and convert them into electronic impulses that may be stored, viewed and manipulated for diagnostic use by dentists.

Device Description

Digital Dental Intra Oral Sensor is a device which acquires digital intra-oral images. Direct digital systems acquire images with a sensor that is connected to a computer to produce an image almost instantaneously following exposure. The primary advantage of direct sensor systems is the speed with which images are acquired. For patient comfort, the ergonomic design is based on human intraoral anatomy.

Excellent image quality based on advanced CMOS technology
More comfortable sensor ergonomic shape for the human oral structure
Lower dose exposure (Compared to film sensor)
Enhanced durability
Easy-to-use USB interface

AI/ML Overview

The provided document is a 510(k) summary for the Rayence Co., Ltd. Digital Dental Intra Oral Sensor. This type of submission focuses on demonstrating substantial equivalence to a predicate device rather than providing detailed clinical study results designed to prove device performance against specific acceptance criteria.

Therefore, the document does not contain the information requested for acceptance criteria and a study proving the device meets those criteria in the typical sense of a clinical trial. Instead, it relies on demonstrating similar performance to a legally marketed predicate device through non-clinical testing.

Here's what can be extracted and what is missing based on your request:

1. Table of Acceptance Criteria and Reported Device Performance

The document measures performance characteristics like DQE, MTF, Pixel Pitch, and Sensor Dimensions against those of a predicate device. It doesn't explicitly state "acceptance criteria" but rather demonstrates "equivalence" to the predicate.

Characteristic	Acceptance Criteria (Implicit: Equivalent to Predicate)	Reported Device Performance (Proposed Device)	Predicate Device Performance
Sensor Dimension (mm) (±10%)	Equivalent to Predicate or justified differences	Size 1.0: 36.8 x 25.4
Size 1.5: 39.5 x 29.2
Size 2.0: 42.9 x 31.3	Size 1.0: 37.6 x 25.4
Size 1.5: 39.5 x 29.2
Sensor Thickness (mm)	Equivalent to Predicate	4.8	4.8
Active Area (mm)	Equivalent to Predicate or justified differences	Size 1.0: 30.01 x 20.01
Size 1.5: 33.00 x 23.98
Size 2.0: 35.99 x 25.99	Size 1.0: 30.01 x 20.00
Size 1.5: 33.00 x 23.98
Pixel Pitch (µm) (Full Resolution)	Equivalent to Predicate	14.8	14.8
Pixel Pitch (µm) (Binning mode)	Equivalent to Predicate	29.6	29.6
DQE (6 lp/mm) (Full Resolution)	Equivalent to Predicate	0.38	0.38
DQE (6 lp/mm) (Binning mode)	Equivalent to Predicate	0.34	0.34
MTF (3 lp/mm) (Full Resolution)	Equivalent to Predicate	0.642	0.642
MTF (3 lp/mm) (Binning mode)	Equivalent to Predicate	0.630	0.630
Electrical, Mechanical, Environmental Safety	Compliance with IEC 60601-1: 2005 + CORR.1(2006) + CORR(2007)	Performed and Compliant	(Not explicitly stated for predicate in summary, but assumed compliant)
EMC Testing	Compliance with IEC 60601-1-2:2007	Performed and Compliant	(Not explicitly stated for predicate in summary, but assumed compliant)
Dynamic Range	Same as predicate device	Same dynamic range	(Not explicitly stated with a value, but equivalence claimed)

2. Sample size used for the test set and the data provenance:

The document explicitly states: "Clinical images were not necessary to establish substantial equivalence based on the modifications to the device. The laboratory performance data shows that the subject device operates similar to the predicate device." Therefore, there was no clinical test set in the traditional sense with human patient data.
The comparison was done with laboratory performance data. The provenance of this laboratory data (e.g., specific lab, country) is not detailed. It's an internal test report.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts:

Not applicable, as no clinical test set with human data and expert ground truth was used.

4. Adjudication method (e.g., 2+1, 3+1, none) for the test set:

Not applicable, as no clinical test set with human data and associated adjudication was used.

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 study was done, nor is this device described as an AI-assisted device. The device is a digital intra-oral sensor for image acquisition.

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

The device itself is a standalone hardware component (sensor). The "performance" being evaluated is its technical characteristics (DQE, MTF, etc.) demonstrating equivalence to a predicate, not an algorithmic diagnostic output. The document states that "The DQE, MTF and linear response to X-ray exposure test demonstrated that the subject sensor performed equivalently compared to the predicate device with the same dynamic range." This is a standalone device performance evaluation, but not in the context of an AI algorithm's diagnostic accuracy.

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.):

Not applicable, as the evaluation was based on non-clinical, laboratory performance measurements (e.g., DQE, MTF values, physical dimensions) compared to a predicate device's specifications. The ground truth for these measurements would be the reference standards and protocols used in the laboratory setting to measure these physical properties.

8. The sample size for the training set:

Not applicable. This is a 510(k) submission for a hardware device (digital x-ray sensor) demonstrating substantial equivalence to a predicate through non-clinical bench testing. It does not involve machine learning or AI models with training sets.

9. How the ground truth for the training set was established:

Not applicable, as there is no training set for an AI model mentioned.

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