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DC-Air™, Athlos-1, Athlos-Air are intended to be used for a radiographic examination by a dental professional to assist in the diagnosing of diseases of the teeth, jaw, and oral structures.

DC-Air™, Athlos-1, Athlos-Air are suitable for general populations.

The subject device DC-Air™ (also known as Athlos-Air) is a wireless intraoral digital X-ray system that comprises of three (3) main components:

• (1) An intraoral X-ray image detector (sensor) with rechargeable battery for capturing X-ray images and which connects to the docking station via a wireless communications protocol (Bluetooth 5.0),
• (2) A docking station that acts as the receiver of the data (X-ray image) sent by the detector and which forwards the data to the operator's personal computer (PC) via USB connection. Also, the docking station functions as a charging station of the detector, and
• (3) An Imaging Software package.

The provided text describes the regulatory filing for the DC-Air™ device, focusing on its substantial equivalence to a predicate device (QuickRay HD). While it mentions performance data, it does not detail a comprehensive study with specific acceptance criteria, corresponding performance metrics, sample sizes, expert ground truth establishment, or MRMC studies that would typically be required to prove device performance against predefined acceptance criteria. Instead, it provides a general statement that images were "examined by doctors... and found to be diagnostically relevant and reliable" (Page 12, Performance Data 8).

Therefore, I cannot fulfill all parts of your request with the provided input. I will highlight what information is present and indicate what is missing.

Missing Information:

• Specific Acceptance Criteria: The document does not list quantitative acceptance criteria (e.g., minimum sensitivity, specificity, or AUC).
• Reported Device Performance against Acceptance Criteria: Since acceptance criteria are not stated, there is no direct table showing the device meeting these criteria. The document states images were "diagnostically relevant and reliable" but provides no quantitative metrics.
• Sample Size for the Test Set: No specific number of images or patients in a test set is provided.
• Data Provenance (Country of Origin, Retrospective/Prospective): This information is not explicitly stated for the "examined images."
• Number of Experts and Qualifications for Ground Truth: While three doctors are named (Robert Sachs D.D.S., John M. Steinberg D.D.S., and Steven R. Gluck D.D.S.), their specific qualifications beyond "D.D.S." (Doctor of Dental Surgery) and their experience levels are not detailed.
• Adjudication Method: No method for resolving discrepancies among experts (e.g., 2+1, 3+1) is described.
• Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study: The document does not mention an MRMC study or any comparison of human readers with vs. without AI assistance. The device itself is an X-ray sensor, not an AI diagnostic aid.
• Standalone Performance: The performance data mentioned (Page 12, Performance Data 8) describes human examination of images from the device, not a standalone algorithm's performance.
• Type of Ground Truth Used: The ground truth appears to be expert consensus (the three D.D.S. doctors), but it's not explicitly labeled as such, nor are specifics on how this consensus was reached.
• Sample Size for the Training Set: This is a performance study, not an AI training study. The device is a hardware sensor.
• Ground Truth Establishment for Training Set: Not applicable as it's a hardware device, not an AI model.

Here's a summary of the available information regarding the device's performance assessment:

Device: DC-Air™, Athlos-1, Athlos-Air (Intraoral Digital X-ray Sensor)

Study Purpose (Implied): To demonstrate the diagnostic relevance and reliability of images produced by the DC-Air™ sensor.

1. Table of Acceptance Criteria and Reported Device Performance:

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

• Sample Size: Not specified. The text mentions "X-ray images taken using the DC-Air™," implying an unspecified number of images.
• Data Provenance: Not specified (e.g., country of origin, retrospective or prospective).

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

• Number of Experts: Three.
• Qualifications: Doctors Robert Sachs D.D.S., John M. Steinberg D.D.S., and Steven R. Gluck D.D.S. (Doctor of Dental Surgery). No further detail on experience (e.g., years in practice, specialization) is provided.

4. Adjudication Method for the Test Set:

• Method: Not specified. It's only stated that the doctors "examined" the images and "found" them to be diagnostically relevant and reliable, implying a consensus or individual findings without detailing the agreement process.

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

• No, a MRMC comparative effectiveness study was not explicitly mentioned or performed. This device is an imaging sensor, not an AI-based diagnostic tool. The performance claim relates to the quality of the generated images for human interpretation, not an AI's impact on human reading.

6. Standalone Performance:

• Not directly applicable in the AI context. The performance mentioned refers to the quality of the images produced by the sensor for human interpretation, not an algorithm performing a diagnostic task independently.

7. The Type of Ground Truth Used:

• Type: Expert consensus (from the three D.D.S. doctors). No mention of pathology or outcomes data as ground truth.

8. The Sample Size for the Training Set:

• Not applicable. This document describes the testing of a hardware device's image output, not the training of a machine learning model.

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

• Not applicable. (See point 8).

Summary of Device Features and Technical Benchmarking (from Table 6-1):

While not directly "acceptance criteria" for a study proving clinical performance in the AI sense, the document does list various technical specifications and comparisons to the predicate device. These are more akin to engineering and imaging performance metrics rather than clinical outcome measures.

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