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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.

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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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.

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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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.

EzSensor Soft[Alternative name : EzSensor Bio] Digital Dental Intra Oral Sensor is a device which acquires digital intra oral images. Direct digital systems acquire images with a flexible 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.

The provided document is a 510(k) premarket notification for a dental imaging device, "EzSensor Soft [Alternative name : EzSensor Bio] digital dental image processing system". It compares the new device to a predicate device, "EzSensor". The study primarily focuses on technical performance comparisons and a small-scale image review, rather than a clinical effectiveness study with strict acceptance criteria often seen in AI/CAD device approvals.

Here's an analysis of the acceptance criteria and study findings based on the provided text, while noting the limitations in terms of typical AI/CAD device study requirements:

1. A table of acceptance criteria and the reported device performance

The document does not explicitly state formal acceptance criteria in the manner of quantifiable metrics for diagnostic performance (e.g., sensitivity, specificity, AUC targets). Instead, the performance comparison focuses on technical image quality metrics and a qualitative review by a dentist.

Here's a table based on the provided technical characteristics and the stated comparative performance:

• MTF (6 lp/mm): 0.436 (Full Resolution) / 0.464 (Binning Mode) vs. 0.382 (Predicate). Better performance.
• NPS: Not explicitly quantified, but stated "outperformed EzSensor". Better performance. |
  | Linearity | At least equivalent to the predicate device (EzSensor). | "Very linear and has better linearity than EzSensor in the same dynamic range." Better performance. |
  | Contrast-to-Noise Ratio (CNR) | At least equivalent to the predicate device (EzSensor). | "Superior CNR characteristics compared to EzSensor... direct result of Noise improvement." Better performance. |
  | Resolution/Sharpness | Images should be similar or moderately superior to EzSensor, presenting no difficulty in evaluating anatomical structures. | "Final images generated by both new and predicate sensors are similar or moderately superior to existing EzSensor (Predicate)." "Images of EzSensor Soft [Alternative name : EzSensor Bio] in full resolution mode is generally more sharper and clearer than EzSensor Soft [Alternative name : EzSensor Bio] in binning mode." |
  | Clinical Acceptability | Images present no difficulty in evaluating anatomical structures necessary for correct diagnosis. | "All images present no difficulty in evaluating a range of anatomic structures necessary to provide a correct conclusion..." (based on review by one dentist). |
  | Risk Assessment | All risks and hazardous conditions mitigated to acceptable limits. | "All risks and hazardous conditions identified arising from the design change were successfully mitigated and accepted." Risks identified (electronic shock, device failure, misdiagnosis, tissue damage, serious leakage current, sensor fracture/breakage, cable disconnection) analyzed via FMEA and verified with IEC/EN 60601-1 and drop & vibration tests. |
  | Software Functionality | Easydent and EzDent i have same functionality and performance. | "Easydent and EzDent i image viewing software have the same functionality and performance." Main difference is UI design and new consulting simulation tool for EzDent i. |

2. Sample size used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)

• Test Set Sample Size: "Total 30 sets of radiographic images were reviewed."
• Data Provenance: Not explicitly stated, but the submission is from Rayence Co. Ltd. in Korea. The context of a premarket notification for a new device suggests these would likely be newly acquired images for testing, making them prospective to the submission. However, this is an inference; it is not explicitly stated. The country of origin for the images is not specified.

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)

• Number of Experts: "a licensed dentist" (singular).
• Qualifications: "a licensed dentist." No information on years of experience or specialization is provided.

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

• Adjudication Method: "none". The document states "Based on the reviewer's conclusion," indicating a single reviewer's assessment without a consensus or adjudication process.

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

• MRMC Study: No. This document describes a medical device (digital dental x-ray sensor), not an AI/CAD system. Therefore, an MRMC study comparing human readers with and without AI assistance is not applicable and was not performed. The study described is a technical comparison of the image sensor and a qualitative review of images by a single dentist.

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

• Standalone Study: Not applicable. This is not an AI/CAD algorithm. The device is an image acquisition sensor. There is no algorithm operating standalone on diagnostic tasks.

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

• Type of Ground Truth: The "ground truth" for the image review consisted of the qualitative assessment by a single "licensed dentist" regarding the difficulty in evaluating anatomical structures and the sharpness/clearness of images. This is best characterized as expert opinion/review by a single expert. It is not objective ground truth like pathology or outcomes data.

8. The sample size for the training set

• Training Set Sample Size: Not applicable. As this is an image acquisition hardware device (sensor) and not an AI/machine learning algorithm, there is no "training set" in the context of AI. The device's performance is based on its physical and electronic design and confirmed through bench testing.

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

• Ground Truth for Training Set: Not applicable, as there is no training set for an AI algorithm.

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HDI 2000 / HDI 2000A, an Intra-Oral 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.

The HDI 2000 / HDI 2000A Intra-oral imaging system is a device which acquires digital image. Direct digital systems acquire images with a solid-state 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. The sensor is connected by a wire to an analog-to-digital USB box, which is connected to the computer. Images are produced within seconds of sensor exposure. The wire length from a direct sensor is about 3 meters and less. The USB box connects to the USB 2.0 port of the computer.

Here's an analysis of the provided text regarding the acceptance criteria and study for the HDI 2000 / HDI 2000A device:

This 510(k) submission primarily focuses on demonstrating substantial equivalence to a predicate device (EzSensor P) rather than presenting a de novo clinical study with explicit acceptance criteria for diagnostic performance. Therefore, many of the typical clinical study details are not present.

1. Table of Acceptance Criteria and Reported Device Performance

Because this is a 510(k) submission for substantial equivalence based on technological characteristics, explicit numerical diagnostic performance acceptance criteria (e.g., sensitivity, specificity, AUC) are not defined or reported for diagnostic accuracy. The acceptance criteria revolve around meeting established safety and performance standards for medical electrical equipment and X-ray imaging devices, and demonstrating that the new device does not raise new questions of safety or effectiveness compared to the predicate.

2. Sample Size Used for the Test Set and Data Provenance

The document does not describe a clinical test set in the traditional sense of a clinical trial for diagnostic performance. The testing described is primarily for engineering standards compliance (safety, EMC) and performance characteristics relevant to sensor operation, rather than a clinical evaluation of diagnostic accuracy using a test dataset of patient images.

• Sample Size for Test Set: Not applicable in the context of diagnostic performance. The document refers to "test results" for electrical, mechanical, environmental, and EMC testing, which would involve laboratory testing of device units.
• Data Provenance: Not applicable for diagnostic performance as no clinical diagnostic test set is described. The engineering tests would be performed in a controlled laboratory environment.

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

Not applicable. As no clinical test set for diagnostic performance is described, there's no mention of experts establishing a ground truth for such a set.

4. Adjudication Method for the Test Set

Not applicable. No clinical test set or adjudication process for diagnostic interpretations is described.

5. If a Multi Reader Multi Case (MRMC) Comparative Effectiveness Study was done

No, a Multi Reader Multi Case (MRMC) comparative effectiveness study was not done or reported in this 510(k) submission. The submission focuses on demonstrating substantial equivalence based on technological characteristics and engineering tests, not a clinical comparison of diagnostic efficacy.

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

This device is an intra-oral imaging system (sensor and software), not an AI algorithm designed to interpret images independently. Therefore, the concept of "standalone performance" for an AI algorithm without human-in-the-loop is not applicable to this submission. The device's function is to acquire and display images for diagnostic use by dentists.

7. The Type of Ground Truth Used

The concept of "ground truth" for diagnostic accuracy (e.g., pathology, outcomes data) is not applicable in this submission, as no diagnostic performance study is described. The "truth" being evaluated relates to the device's adherence to engineering standards and its functional performance as an imaging system (e.g., image capture, viewing) being equivalent to the predicate device.

8. The Sample Size for the Training Set

Not applicable. This device is an imaging sensor and viewer software; it is not an artificial intelligence (AI) or machine learning (ML) algorithm that requires a "training set" of data for learning diagnostic patterns.

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

Not applicable, as there is no training set for an AI/ML algorithm described.

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HDI 1000 / HDI 1000A, an Intra-oral Imaging System, is used to collect dental x-rays photons and convert them into electronic impulses that may be stored, viewed, and manipulated for diagnostic use by dentists.

The HDI 1000 / HDI 1000A intra-oral imaging system is a device which acquires digital intra oral images. Direct digital systems acquire images with a solid-state 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. The sensor is connected by a wire to an analog-to-digital converter box, which is connected to the computer. Images are produced within seconds of sensor exposure. The wire length from a direct sensor is about 3 meters and less. This wire plugs into an analogto-digital converter box. The converter box connects to the computer with a USB cable.

The provided text does not contain information about specific acceptance criteria or a study that proves the device meets those criteria with detailed performance metrics. The submission is a 510(k) summary for a digital dental intra-oral sensor (HDI 1000, HDI 1000A), which primarily focuses on demonstrating substantial equivalence to a predicate device.

The document states: "The indications for use, material, form factor, performance, and safety characteristics of HDI 1000 / HDI 1000A described in this 510(k) are the same as that of the predicate device, EzSensor of Rayence Co., Ltd." This indicates that the device is deemed acceptable because it performs similarly to an already approved device.

The "Summary for any testing in the submission" section mentions:

• Electrical, mechanical, environmental safety and performance testing according to standard IEC 60601-1: 2005 + CORR.1(2006) + CORR(2007).
• EMC testing conducted in accordance with standard IEC 60601-1-2:2007.
• Non-clinical & Clinical considerations according to FDA Guidance "Guidance for the Submissions of 510(k)'s for Solid State X-ray Imaging Devices" was performed.

It concludes that "All test results were satisfactory" and the device is "safe and effective and substantially equivalent to predicate device." However, specific quantitative acceptance criteria or detailed results from these tests are not provided in this summary.

Therefore, most of the requested information regarding detailed acceptance criteria, device performance, sample sizes, ground truth establishment, expert involvement, and comparative effectiveness studies are not present in this 510(k) summary.

Here's a breakdown of what can be extracted and what is missing:

1. A table of acceptance criteria and the reported device performance

• Indications for use, material, form factor, performance, and safety characteristics are the same. |
  | Electrical, mechanical, environmental safety and performance according to IEC 60601-1: 2005 + CORR.1(2006) + CORR(2007) |- All test results were satisfactory. |
  | EMC compliance according to IEC 60601-1-2:2007 |- All test results were satisfactory. |
  | Non-clinical & Clinical considerations according to FDA Guidance "Guidance for the Submissions of 510(k)'s for Solid State X-ray Imaging Devices" |- All test results were satisfactory. |

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

• Missing: The document does not specify any sample sizes for test sets or data provenance (e.g., country of origin, retrospective/prospective). The testing mentioned seems to be primarily engineering and regulatory compliance, not clinical performance studies with patient data to establish diagnostic accuracy.

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

• Missing: This information is not provided as there is no mention of a clinical study involving experts establishing ground truth for diagnostic accuracy.

4. Adjudication method for the test set

• Missing: Not applicable, as no clinical test set requiring adjudication is described.

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

• Missing: This is not an AI or CAD device. It's an imaging acquisition device. No MRMC study was conducted or is relevant based on the provided information.

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

• Missing: Not applicable. This is an imaging sensor, not an algorithm.

7. The type of ground truth used

• Missing: As no clinical performance study for diagnostic accuracy is described, there's no mention of ground truth established through expert consensus, pathology, or outcomes data. The "ground truth" for this device's acceptance is its ability to meet safety and performance standards (e.g., IEC standards) and demonstrate substantial equivalence to a predicate device.

8. The sample size for the training set

• Missing: Not applicable. This is an imaging sensor, not a machine learning model, so there is no "training set."

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

• Missing: Not applicable.

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