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510(k) Data Aggregation
(25 days)
EzSensor Soft, EzSensor Soft i, EzSensor Bio, and EzSensor Bio i
EzSensor Soft, EzSensor Soft i, EzSensor Bio i Digital Dental Intra Oral Sensors are intended to collect dental x-ray photons and convert them into electronic impulses that may be stored, viewed and manipulated for dagnostic use by dentists.
EzSensor Soft, EzSensor Soft i, EzSensor Bio i are digital dental intraoral sensors which acquire digital intra oral images. Direct digital systems acquire images with a bendable sensor that is connected to a computer to produce an image almost instantaneously following exposure. The primary advantage of direct sensor systems is the image acquisition speed. For patient comfort, the ergonomic design is based on human intraoral anatomy.
The provided text describes a Special 510(k) submission for device modifications to the EzSensor Soft, EzSensor Soft i, EzSensor Bio, and EzSensor Bio i digital dental intraoral sensors. This filing primarily focuses on demonstrating substantial equivalence to a predicate device through performance testing and does not include a comparative effectiveness study with human readers (MRMC) or a standalone (algorithm-only) performance study. The device itself is a digital dental intraoral sensor, not an AI algorithm.
Here's a breakdown of the requested information based on the provided document:
1. A table of acceptance criteria and the reported device performance
The document doesn't explicitly state "acceptance criteria" in a pass/fail quantifiable manner for the overall device. Instead, it demonstrates performance equivalence to a predicate device against specific technical characteristics.
| Characteristic | Acceptance Criteria (Implied: Equivalence to Predicate) | Reported Device Performance (Proposed Device) | Predicate Device Performance (K143753) |
|---|---|---|---|
| Indications for Use | Substantially equivalent to predicate. | EzSensor Soft, EzSensor Soft i, EzSensor Bio and EzSensor Bio i Digital Dental Intra Oral Sensors are 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. | EzSensor Soft [Alternative name : EzSensor Bio] 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. |
| Sensor Dimension (mm) (±10%) | Slight variation within acceptable tolerance. | Size "1.0": 37.8 x 26.6 Size "1.5": 40.8 x 30.6 Size "2.0": 44.0 x 32.5 | Size "1.0": 37.5 x 26.5 Size "2.0": 43.5 x 32.5 |
| Sensor Thickness (mm) | Equivalent to predicate. | 5 | 5 |
| Active Area (mm) | Slight variation within acceptable tolerance. | Size "1.0": 20.01 x 30.01 Size "1.5": 23.98 x 33.00 Size "2.0": 25.99 x 35.99 | Size "1.0": 20 x 30 Size "2.0": 25.99 x 35.99 |
| USB Module | Equivalent to predicate. | Integrated USB 2.0 module | Integrated USB 2.0 module |
| 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 84.64 µGy 6 lp/mm - Full Resolution | Equivalent to predicate. | 0.070 | 0.070 |
| DQE 84.64 µGy 6 lp/mm - Binning mode | Equivalent to predicate. | 0.070 | 0.070 |
| MTF 84.64 µGy 6 lp/mm - Full Resolution | Equivalent to predicate. | 0.154 | 0.154 |
| MTF 84.64 µGy 6 lp/mm - Binning mode | Equivalent to predicate. | 0.133 | 0.133 |
| Typical dose range (µGy) | N/A (Information provided for proposed device only) | Incisor & Canine: 300 ~ 500 / Molar: 400 ~ 600 | Not specified for predicate. |
| Viewer Software | Equivalence in function and indications for use. | Easydent or EzDent-i (K150747) (Note: EzDent-i 2.0 has additional features, but maintains similar indications and functionalities as EzDent-i 1.0 (K131594) from predicate) | Easydent or EzDent-i (K131594) |
| Safety and Effectiveness | No additional safety risk identified, substantially equivalent to predicate. | Performance test results indicate the subject detector performed equally to the predicate. No additional safety risk identified in bench test. | N/A (Predicate performance is the benchmark) |
2. Sample size used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)
The document describes "Performance Testing" and "bench test: Non-clinical report" according to FDA Guidance "Guidance for the Submissions of 510(k)'s for Solid State X-ray Imaging Devices." However, it does not specify a sample size for the test set or the data provenance (country of origin, retrospective/prospective). The testing appears to be laboratory-based ("bench test").
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)
This information is not provided. The assessment relies on technical specifications (DQE, MTF, linear response to X-ray exposure) and safety testing, not on a ground truth established by medical experts for diagnostic accuracy in a clinical context. The device is a sensor, not a diagnostic algorithm that interprets images.
4. Adjudication method (e.g. 2+1, 3+1, none) for the test set
Not applicable and not provided. As mentioned above, the evaluation is based on technical specifications and safety testing, not on clinical image interpretation requiring adjudication.
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 comparative effectiveness study was done. This is a 510(k) for a digital dental intraoral sensor, not an AI-powered diagnostic tool. The document states a "Summary of Performance Testing" based on technical specifications compared to a predicate device.
6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done
No standalone algorithm performance study was done. The device itself is a sensor that collects data, which is then viewed and manipulated by dentists using viewer software. It is not an algorithm making standalone diagnostic assessments.
7. The type of ground truth used (expert consensus, pathology, outcomes data, etc)
The concept of "ground truth" as typically applied to diagnostic algorithms (expert consensus, pathology, etc.) is not directly relevant or discussed in this submission. The device's performance is assessed through technical metrics (DQE, MTF, linear response to X-ray exposure) and compliance with electrical, mechanical, and environmental safety standards (IEC 60601-1, IEC 60601-1-2), comparing these metrics against those of a predicate device to establish substantial equivalence.
8. The sample size for the training set
Not applicable. This submission is for hardware (an X-ray sensor) and associated viewer software, not a machine learning or AI algorithm that would require a training set.
9. How the ground truth for the training set was established
Not applicable. As there is no training set for an AI algorithm, there is no ground truth established for one.
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