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X 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.

X Sensor is a digital intraoral sensor which acquires digital intra-oral images. X Sensor acquires intra oral 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. X Sensor includes the firmware for the sensor and the previously cleared imaging software "ExDent-I".

The provided text is a 510(k) summary for the X Sensor, a digital dental intraoral sensor. It describes the device's technical characteristics, intended use, and its comparison to a predicate device (EzSensor XHD). However, this document does not contain the detailed information necessary to fully answer your request regarding acceptance criteria and the study proving the device meets these criteria in the context of AI/ML performance.

Specifically, the document focuses on regulatory clearance for a medical device (an X-ray sensor) based on hardware and image quality performance relative to a predicate device, as well as electrical, mechanical, and software safety. It does not mention any AI/ML components or studies evaluating AI/ML performance.

Therefore, many parts of your request, such as those related to AI/ML specific acceptance criteria, sample sizes for AI/ML test and training sets, expert adjudication, MRMC studies, or standalone algorithm performance, cannot be answered from the provided text.

However, I can extract information related to the device's general performance testing and comparison to the predicate device, which serves as a form of "acceptance" for medical device clearance.

Here's what can be extracted and how it relates to your request, with a clear indication of what information is not present:

Acceptance Criteria and Device Performance (General Device Performance)

Based on the document, the "acceptance criteria" appear to be meeting or exceeding the performance of the predicate device (EzSensor XHD) in key technical metrics and demonstrating adequate image quality for diagnostic use.

Study Information (Based on Available Text)

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

   • The document states "The clinical images obtained from the X Sensor and EzSensor XHD were reviewed and rated comparatively."
   • Specific sample size for the clinical images reviewed is NOT provided.
   • Data provenance (country of origin, retrospective/prospective) is NOT provided. Given it's a 510(k) for a device like an X-ray sensor, the "test set" likely refers to physical images generated during bench testing and some limited clinical image capture, rather than a large dataset for AI/ML validation.

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

   • The document states "The clinical images obtained from the X Sensor and EzSensor XHD were reviewed and rated comparatively."
   • The number of experts and their qualifications are NOT specified. This phrasing suggests a qualitative human review of generated images to ensure diagnostic utility.

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

   • Adjudication method is NOT specified. The review appeared to be a comparative assessment of image quality.

4. 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 performed or discussed. This device is an X-ray sensor, not an AI-assisted diagnostic tool in the sense of an algorithm interpreting images for a human. It provides the images.

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

   • This is NOT applicable. The "device" is the sensor itself, which captures images. There is no mention of an algorithm that performs standalone diagnostic interpretations.

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

   • For the image quality assessment, the "ground truth" seems to be clinical utility/diagnostic adequacy as judged by human review of images generated by both the new device and the predicate device. Quantitative metrics (DQE, MTF, NPS) also served as objective performance measures.

7. The sample size for the training set:

   • NOT applicable/NOT provided. This document describes a medical device, an X-ray sensor, not an AI/ML algorithm that requires a "training set" in the context of machine learning. The "training" for such a device would be its engineering and design optimization.

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

   • NOT applicable/NOT provided. As above, there's no mention of a machine learning training set or associated ground truth.

Summary of Limitations:

The provided document is a regulatory submission for an X-ray sensor. It focuses on demonstrating substantial equivalence to a predicate device based on technical specifications, image quality, and regulatory compliance (electrical safety, EMC, software documentation, cybersecurity). It does not describe an AI/ML diagnostic algorithm or any studies related to its performance, and therefore cannot answer the specific questions posed about AI/ML acceptance criteria and validation.

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EzSensor XHD 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 XHD is a digital intraoral sensor which acquires digital intra-oral images. EzSensor XHD acquires intra oral 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. The ergonomic design based on human intraoral anatomy improves patient comfort. EzSensor XHD includes the software (firmware) with MODERATE level of concern.

The provided text is a 510(k) summary for the EzSensor XHD, a digital dental intraoral sensor. It aims to demonstrate substantial equivalence to a predicate device, not necessarily to prove the device meets specific acceptance criteria for a new, unproven technology. Therefore, the traditional concept of "acceptance criteria" and a "study that proves the device meets the acceptance criteria" in the context of a clinical trial with specific performance thresholds is not directly applicable in the way one might expect for a novel AI device.

Instead, the document focuses on demonstrating substantial equivalence to an already legally marketed predicate device (EzSensor Soft, EzSensor Soft i, EzSensor Bio, EzSensor Bio I, Model: 1.0, 1.5, 2.0; K151707). This is achieved by showing that the proposed device has the same indications for use and similar technological characteristics, and that any differences do not raise new questions of safety or effectiveness.

However, we can extract performance specifications and comparative data presented as part of the substantial equivalence claim.

Here's an attempt to structure the information based on your requested format, interpreting "acceptance criteria" as the performance metrics deemed acceptable for demonstrating substantial equivalence to the predicate device, and "study" as the non-clinical and comparative evaluations conducted.

Device Name: EzSensor XHD
Regulatory Product Code: MUH (Extraoral source X-ray system)

Interpretation of "Acceptance Criteria" in the Context of 510(k) Substantial Equivalence:

For a 510(k) submission, "acceptance criteria" are not explicitly defined as pass/fail thresholds against a clinical endpoint for a novel diagnostic. Instead, the "acceptance" is the FDA's determination of substantial equivalence to a predicate device. This is achieved by demonstrating that the new device is as safe and effective as the predicate device. The performance comparisons below serve as the data points to support this claim, rather than a direct set of pre-defined "acceptance criteria" for clinical accuracy or efficacy.

1. Table of Acceptance Criteria (Interpreted as Performance Metrics for Substantial Equivalence) and Reported Device Performance

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

The document describes non-clinical bench testing and a comparative review of clinical images.

• Non-clinical (Bench) Test Set: No specific sample size is given for the non-clinical tests (DQE, MTF, NPS, Electrical, Mechanical, Environmental, EMC). These tests are typically performed on a limited number of manufactured devices/prototypes.
• Clinical Image Test Set: The text states, "The clinical images obtained from the EzSsensor XHD and EzSensor Soft were reviewed and rated comparatively." No specific number of images or patients (sample size) is provided for this comparative review.
• Data Provenance: Not explicitly stated, but assumed to be from a controlled in-house setting given the nature of a 510(k) submission primarily relying on bench testing and limited comparative review. The studies are prospective in the sense that they were conducted specifically for this submission, although the images themselves could be from previous patient encounters (retrospective collection).

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

• The document mentions a "comparative review" of clinical images but does not specify the number or qualifications of experts who performed this review or established any form of ground truth for the clinical image set. It simply states the images were "reviewed and rated comparatively" and that "EzSensor XHD produces overall better definition and grayscale of bony and soft tissue images in comparison with EzSensor Soft."

4. Adjudication Method (for the Test Set)

• No adjudication method is described. The comparative review appears to be a qualitative assessment, not a formal quantitative study requiring adjudication of expert readings against a ground truth.

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study

• No MRMC comparative effectiveness study was done as described in the provided text. The evaluation was a qualitative comparison of image quality, not a study of human readers' performance with and without AI assistance. The device itself is an imaging sensor, not an AI-assisted diagnostic tool.

6. Standalone (Algorithm Only Without Human-in-the-Loop Performance)

• This question is not applicable in the context of this device. The EzSensor XHD is a hardware imaging sensor that collects X-ray photons and converts them to digital images for diagnostic use by dentists. It does not perform an algorithm-only diagnostic task without human interpretation. It is a data acquisition device, not a diagnostic AI.

7. Type of Ground Truth Used

• For the non-clinical performance metrics (DQE, MTF, NPS), the "ground truth" is established by physical measurements and standardized testing protocols performed according to FDA guidance and international standards (e.g., "Guidance for the Submissions of 510(k)'s for Solid State X-ray Imaging Devices").
• For the comparative clinical image review, the "ground truth" is implied to be the expert visual assessment of image quality enabling diagnostic use, compared to an accepted predicate device. There is no mention of pathology, clinical outcomes data, or a multi-expert consensus process for a defined "ground truth" in the sense of disease presence/absence.

8. Sample Size for the Training Set

• The EzSensor XHD is a digital imaging sensor, not an AI/machine learning algorithm that requires a "training set" of data in the typical sense. Therefore, there is no training set as would be understood for an AI device. The device's performance is determined by its physical and electronic design and manufacturing, not by learning from a dataset.

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

• Not applicable, as there is no training set for this hardware device.

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