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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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The DEXIS / KaVo sensor is a USB-driven digital sensor which is intended to acquire dental intraoral radiographic images. The DEXIS / KaVo sensor shall be operated by healthcare professionals, who are educated and competent to perform the acquisition of dental intra-oral radiographs. The DEXIS / KaVo sensor can be used either in combination with special positioning devices to facilitate positioning and alignment with the x-ray beam or it may also be positioned by hand with the assistance of the patient.

The DEXIS Titanium, Kavo IXS HD (Size 1, Size 2) intraoral sensors are an indirect converting x-ray detector, e.g. incident x-rays are converted by a scintillating material into (visible) light, this light is coupled optically to a light detection imager based on CMOS technology. The design of the sensor assembly supports the automatic detection of the incident x-rays to generate digital images for dental intra oral applications. The DEXIS Titanium, Kavo IXS HD (Size 1, Size 2) intraoral sensors supports USB2.0 connectivity to personal computers using a dedicated electronic assembly and a sensor software driver.

The provided text describes the KaVo Dental Technologies, LLC DEXIS Titanium, KaVo IXS HD (Size 1, Size 2) intraoral sensors. It concludes that clinical performance data was not needed to characterize performance and establish substantial equivalence. Therefore, there is no information about specific acceptance criteria related to clinical performance or a study proving the device meets such criteria.

The document focuses on non-clinical test data and substantial equivalence to a predicate device (DEXIS Sensor - K090458). It states that substantial equivalence is based on a comparison of intended use, indications, technological characteristics, principle of operation, features, and non-clinical performance data.

Here's an overview of the non-clinical performance testing and device characteristics provided, which indirectly serve as acceptance criteria for regulatory clearance:

1. Table of Acceptance Criteria (Implied Non-Clinical) and Reported Device Performance:

Since clinical performance criteria are explicitly stated as "not needed," the acceptance criteria are derived from the performance bench testing and conformance to international standards for extraoral source X-ray systems. The reported device performance is presented in the comparison table with the predicate device and in the list of standards met.

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

The document states "Clinical data is not needed to characterize performance." Therefore, there is no test set of patient data described for clinical validation. The testing described is non-clinical bench testing. The provenance of the data is not specified beyond being "non-clinical test data."

3. Number of Experts Used to Establish Ground Truth and Qualifications:

Not applicable, as no clinical study with a test set requiring expert ground truth establishment was conducted. The assessment relied on engineering and scientific principles and adherence to recognized standards.

4. Adjudication Method for the Test Set:

Not applicable, as no clinical study with a test set requiring adjudication was conducted.

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

No MRMC study was performed or cited, as clinical data was deemed unnecessary for substantial equivalence. Therefore, there is no information on the effect size of AI assistance on human readers.

6. Standalone Performance Study:

A standalone performance assessment was effectively done through the non-clinical bench testing, where the device's technical specifications and adherence to international standards were evaluated independently. However, this is not a standalone clinical performance study in the sense of an algorithm-only evaluation against ground truth in a clinical context.

7. Type of Ground Truth Used:

For the non-clinical testing, the "ground truth" was based on established engineering principles, reference standards (e.g., test phantoms for X-ray resolution if applicable, though not explicitly stated), and regulatory compliance requirements for medical devices. There was no clinical ground truth (e.g., pathology, outcomes data) used in this submission.

8. Sample Size for the Training Set:

Not applicable, as this device is a hardware sensor, not an AI/ML algorithm that requires a training set in the conventional sense.

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

Not applicable, as it is a hardware device and not an AI/ML algorithm that requires a training set.

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