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Endograph DC is an extraoral source x-ray unit for dental radiographic examination and diagnosis of diseases of the teeth, jaw, and oral structures. The device is to be operated and used by dentists, radiologists and other legally qualified health care professionals. It can be used with both pediatric and adult patients.

Endograph DC is an extraoral source x-ray unit. Endograph DC is dedicated to intraoral radiography in which the x-ray source is placed outside the mouth of the patient while the image detector (film or digital detector) is placed inside the mouth. The image detector is not part of Endograph DC system, so it is not part of this submission.

The provided text describes a 510(k) summary for the "Endograph DC" device, which is an extraoral source x-ray unit for dental radiography. This document primarily focuses on demonstrating substantial equivalence to a predicate device rather than presenting a detailed study proving the device meets specific performance-based acceptance criteria through an independent clinical trial or extensive standalone performance evaluation.

Here's a breakdown based on the provided input:

1. A table of acceptance criteria and the reported device performance
   The document does not explicitly state quantitative acceptance criteria or detailed device performance metrics in a table. The rationale for substantial equivalence is based on the device having the same indication for use and similar technological characteristics as the predicate device (ViVi S.r.l. Ergon-X HF, K120318). Minor technological differences are stated to "not raise any new questions regarding safety or effectiveness."

   Acceptance Criteria	Reported Device Performance
   Indication for Use Equivalence	Endograph DC has the same indication for use as the predicate device.
   Technological Equivalence	Endograph DC shares the same technological characteristics as the predicate device. Minor differences do not raise new safety or effectiveness concerns.
   Safety Standards Compliance	The device has been tested for compliance to IEC 60601-1 Medical Electrical Equipment Part 1: General Requirements for Safety, and its derivatives.
   Software Development & Validation	Software development follows documented processes for design, verification, and validation testing. Final device validation and risk assessment conducted.

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 does not mention a specific "test set" or data directly related to a study proving performance against acceptance criteria. The evaluation presented is a regulatory submission for substantial equivalence. The "testing" mentioned refers to compliance with safety standards (IEC 60601-1) rather than a clinical performance study with a test set of images or patients.

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)
   Not applicable, as no clinical performance study with a test set requiring expert ground truth establishment is described.

4. Adjudication method (e.g. 2+1, 3+1, none) for the test set
   Not applicable, as no clinical performance study with a 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
   No MRMC comparative effectiveness study is mentioned. The device is an X-ray unit, not an AI-powered diagnostic tool, so the concept of human readers improving with AI assistance is not relevant in this context.

6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done
   The document describes an X-ray unit, which is a medical imaging device/hardware. It does not describe an algorithm or software requiring standalone performance testing in the sense of an AI model's diagnostic accuracy. The safety and effectiveness information focuses on compliance with general medical electrical equipment standards and quality system processes for software development and risk assessment.

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc)
   Not applicable. The evidence provided is based on technical specifications, adherence to standards, and comparison to a predicate device, not on a ground truth derived from clinical data for diagnostic accuracy.

8. The sample size for the training set
   Not applicable. The device is an X-ray unit, not a machine learning model that requires a training set.

9. How the ground truth for the training set was established
   Not applicable, as there is no training set for a machine learning model.

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