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510(k) Data Aggregation
(20 days)
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.
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
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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(36 days)
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.
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.
Here's a breakdown of the acceptance criteria and study information for the "Digital Dental Intra Oral Sensor, EzSensor Smart" device, based on the provided text:
Important Note: The provided text is a 510(k) summary, which focuses on demonstrating substantial equivalence to a predicate device rather than a comprehensive standalone performance study with detailed acceptance criteria and ground truth validation as one might find for a novel AI device. Therefore, some information, particularly regarding specific numerical acceptance criteria and a detailed multi-reader multi-case (MRMC) study, is not present.
1. Table of Acceptance Criteria and Reported Device Performance
The document doesn't explicitly state numerical acceptance criteria in a pass/fail format for diagnostic performance. Instead, it focuses on demonstrating superiority or substantial equivalence to a predicate device (EzSensor) through various technical characteristics and a comparative image review.
| Characteristic | Acceptance Criteria (Implied) | Reported Device Performance (Proposed Device) |
|---|---|---|
| Diagnostic Image Quality | Produce images allowing for correct diagnosis of a range of anatomic structures while minimizing radiation exposure to patients. Ideally, equivalent or superior to the predicate device. | Images produced by the proposed device (IOS-U15VF AND 11MODELS, both Binning Mode and Full Resolution Mode) were consistently better than the predicate device (EzSensor) in terms of diagnostic quality in most cases. Negligible difference between Binning Mode and Full Resolution Mode. All images from both devices presented no significant difficulty in evaluating a range of anatomic structures necessary for a correct diagnosis. |
| Pixel Pitch ($\mu$m) | Improved (smaller) pixel pitch compared to predicate for better resolution. | Full Resolution: 14.8 $\mu$m; Binning mode: 29.6 $\mu$m (Predicate: 35 $\mu$m) |
| DQE (6 lp/mm) | Improved DQE compared to predicate for better dose efficiency. | Full Resolution: 0.38; Binning mode: 0.34 (Predicate: 0.123) - Consistently performed better. |
| MTF (6 lp/mm) | Improved MTF compared to predicate for better resolution. | Full Resolution: 0.642; Binning mode: 0.630 (Predicate: 0.382) - Consistently performed better. |
| Linear Response to X-ray Exposure | Improved linearity (closer to 1) compared to predicate. | Very linear response, closer to 1, than the predicate device in the same dynamic range. |
| Electrical, Mechanical, Environmental Safety | Compliance with IEC/EN 60601-1 and IEC 60601-1-2 EMC standards. | Electrical, mechanical, environmental safety and performance testing according to IEC 60601-1: 2005 + CORR.1(2006) + CORR(2007) and EMC testing according to IEC 60601-1-2:2007 were performed. (Implied compliance, as the conclusion states the device is safe and effective). |
| Risk Mitigation | All identified risks successfully mitigated and accepted. | Risks analyzed with FMEA method; specific risk control measures implemented. Overall assessment concluded all risks from design change successfully mitigated and accepted. |
2. Sample Size Used for the Test Set and Data Provenance
- Sample Size: "Total 30 sets of radiographic image samples" were reviewed for diagnostic image quality.
- Data Provenance: The document does not specify the country of origin. It also does not explicitly state whether the data was retrospective or prospective, but given it's an evaluation of image samples, it's most likely retrospective image data captured using the devices.
3. Number of Experts Used to Establish the Ground Truth for the Test Set and Qualifications
- Number of Experts: "a licensed dentist." (Singular)
- Qualifications: "licensed dentist." (No specific years of experience or subspecialty beyond general dentistry is mentioned).
4. Adjudication Method for the Test Set
- Adjudication Method: "Based on the reviewer's conclusion..." The use of a single licensed dentist for review indicates none in terms of formal adjudication (e.g., 2+1 or 3+1 consensus). The assessment relies on a single expert opinion.
5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was Done
- MRMC Study: No, a formal MRMC comparative effectiveness study was not done. The evaluation of diagnostic image quality was conducted by a single licensed dentist reviewing 30 image sets.
- Effect Size: Not applicable, as no MRMC study was performed. The comparison was qualitative by a single reviewer ("superior to EzSensor in terms of diagnostic quality in most cases").
6. If a Standalone (i.e. algorithm only without human-in-the-loop performance) was Done
- This device is a digital intra-oral sensor, a hardware device that captures X-ray images, not an AI algorithm. Therefore, the concept of a "standalone (algorithm only)" performance study is not applicable. The performance tests described relate to the sensor's physical and technical image acquisition capabilities (e.g., DQE, MTF, pixel pitch) and how the images are perceived by a human reader.
7. The Type of Ground Truth Used
- Type of Ground Truth: For the diagnostic image quality assessment, the ground truth was expert opinion/consensus by a single "licensed dentist." It's an assessment of whether the images facilitate correct diagnosis, rather than being linked to independent pathology or patient outcomes data.
- For technical characteristics (pixel pitch, DQE, MTF, linearity), the "ground truth" is derived from laboratory measurements using standardized testing methods.
8. The Sample Size for the Training Set
- The document describes a hardware device (intra-oral sensor) and its associated viewing software. It does not mention any machine learning or AI components that would require a "training set." Therefore, this question is not applicable to this device submission.
9. How the Ground Truth for the Training Set was Established
- As there is no mention of a training set for an AI algorithm, this question is not applicable.
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