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EzSensor T, Intra-oral Imaging System, 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 T is a solid state x-ray imager designed for dental radiographic applications. EzSensor T provides digital image capture for conventional film/screen radiographic dental examinations. The device is used to replace radiographic film/screen systems in general dental diagnostic procedures. The captured digital image is transferred to Personal Computer via USB interface port.

Here's an analysis of the provided text regarding the EzSensor T device, focusing on acceptance criteria and study details:

Interpretation of the Document:

The provided document is a 510(k) summary for the EzSensor T, an intra-oral imaging system. It aims to demonstrate substantial equivalence to a predicate device, not necessarily to independently prove performance against a set of predefined clinical acceptance criteria using a standalone clinical study. The "acceptance criteria" here largely refer to demonstrating the new device's performance is comparable to or better than the predicate device and meets relevant safety and performance standards.

The document emphasizes non-clinical and clinical considerations according to FDA Guidance "Guidance for the Submissions of 510(k)'s for Solid State X-ray Imaging Devices". This implies that the 'study' likely involved technical performance tests and comparative analyses against the predicate, rather than a prospective clinical trial with specific diagnostic accuracy endpoints.

Acceptance Criteria and Reported Device Performance

Note: The document does not explicitly state quantitative "acceptance criteria" in the typical sense of diagnostic metrics (e.g., sensitivity, specificity, AUC) for a clinical study. Instead, it focuses on demonstrating equivalence to a predicate device based on technical specifications and adherence to safety and performance standards. The "reported device performance" is primarily a comparison of technical characteristics to the predicate.

Study Details

Given the nature of a 510(k) summary for substantial equivalence, especially for an imaging device replacing another with similar function, the "study" is primarily a series of non-clinical technical tests and a comparative analysis against the predicate, guided by FDA regulations. It is not presented as a traditional clinical trial.

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

   • Test Set Sample Size: Not explicitly stated as a 'test set' with a specific number of images or patients for diagnostic accuracy assessment. The "all test results were satisfactory" refers to engineering and performance tests.
   • Data Provenance: Not applicable in the context of a retrospective/prospective clinical data set for diagnostic performance. The data provenance is from laboratory and engineering testing.

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

   • Not applicable. The document does not describe a study involving expert readers establishing ground truth for a diagnostic test set. The focus is on technical performance and equivalence.

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

   • Not applicable. There is no mention of a diagnostic test set requiring adjudication in this summary.

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. An MRMC comparative effectiveness study was not performed or mentioned. The device is a direct-capture digital X-ray sensor, not an AI-powered diagnostic aide designed to enhance human reader performance.

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

   • Yes, effectively. The "standalone" performance in this context refers to the intrinsic technical performance of the imaging device itself (e.g., resolution, pixel size, EMC compliance) without a human interpreting images. The listed technical specifications (resolution, pixel matrix, etc.) are characteristics of the device operating in a standalone capacity. "All test results were satisfactory" under "Safety, EMC and Performance Data" implies this.

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

   • Not applicable in the typical sense of clinical ground truth. The "ground truth" for the engineering performance tests would be defined by established calibration standards, physical measurements, and regulatory requirements for safe and effective operation of X-ray imaging devices.

7. The sample size for the training set:

   • Not applicable. The EzSensor T is a hardware device (digital X-ray imager for dental applications), not an AI algorithm requiring a training set of data.

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

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

Summary of the "Study" from the document:

The "study" referenced in this 510(k) summary primarily consists of:

• Engineering and Performance Testing: Compliance with electrical, mechanical, and environmental safety standards (IEC 60601-1, IEC 60601-1-1, IEC 60601-1-2). This includes verifying technical specifications like resolution, pixel size, etc.
• Comparative Analysis: Side-by-side comparison of technical specifications and Indications for Use with the predicate device (EzSensor K090526) to demonstrate substantial equivalence.
• Compliance with FDA Guidance: Adherence to "Guidance for the Submissions of 510(k)'s for Solid State X-ray Imaging Devices," which likely includes non-clinical and some clinical considerations, but without detailing a full-scale clinical trial with diagnostic endpoints in this summary.

The conclusion is that the EzSensor T is "safe and effective and substantially equivalent to predicate device." This means it meets the regulatory hurdle for market entry based on its similarity to an already approved device and its satisfactory performance in compliance tests.

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The EzSensor 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 EzSensor is a solid state x-ray imager designed for dental radiographic applications. The EzSensor provides digital image capture for conventional film/screen radiographic dental examinations. The device is used to replace radiographic film/screen systems in general dental diagnostic procedures. The captured digital image is transferred to Personal Computer via USB interface port.

The provided 510(k) summary (K090526) describes a new dental X-ray imager, EzSensor, seeking substantial equivalence to a predicate device, Schick's CDR (K072134). However, the document primarily focuses on demonstrating device characteristics and safety/performance testing in comparison to the predicate, rather than providing details of a clinical study with specific acceptance criteria and outcome data for the EzSensor's diagnostic performance for the purposes of this request.

Therefore, many of the requested sections below cannot be fully populated as the document does not contain the detailed clinical study information.

Here's an analysis based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

The 510(k) summary does not explicitly state acceptance criteria in terms of diagnostic performance (e.g., sensitivity, specificity, accuracy) for the EzSensor. It focuses on demonstrating substantial equivalence to the predicate device through technical specifications and safety/performance testing.

The document mentions "Electrical, mechanical, environmental safety and performance testing according to standard EN/IEC 60601-1 was performed, and EMC testing was conducted in accordance with standard EN/IEC 60601-1-2(2001). All test results were satisfactory." and "Software testing and validation were done according to written test protocols established before testing was conducted. Test results were reviewed by designated technical professionals before software proceeded to release. Test results support the conclusion that actual device performance satisfies the design intent." However, these refer to technical and safety performance, not diagnostic efficacy.

The comparison table provided in the document focuses on technical specifications between the predicate and proposed device:

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

The document does not describe a clinical test set or data provenance for a diagnostic performance study. The testing mentioned relates to electrical, mechanical, environmental, and software validation.

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

Not applicable, as no diagnostic performance test set is described.

4. Adjudication method for the test set

Not applicable, as no diagnostic performance test set 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

No MRMC study is mentioned. The device is an imaging sensor, not an AI-assisted diagnostic tool as understood in current contexts.

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

The device is a digital X-ray sensor, which requires human interpretation. Standalone "algorithm only" performance would not be relevant in the context of this device as described.

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

Not applicable, as no diagnostic performance study is described. The "ground truth" used for technical testing would be established standards and specifications for electrical, mechanical, and software functionality.

8. The sample size for the training set

Not applicable. The device is a hardware sensor. While it involves software, the document does not describe machine learning or AI models that would typically require a "training set" in the diagnostic performance sense. The software validation mentioned refers to standard software engineering practices.

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

Not applicable, for the reasons stated above.

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