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The CD-Dent Digital Imaging Devices for Dental X-Ray Systems are intended for digital dental radiography using a phosphor storage screen for radiographic diagnostic intraoral and extraoral images.

The CD-Dent Digital Imaging Devices for Dental X-Ray Systems are filmless systems intended for digital intraoral, extraoral and cephalometric radiography using a phosphor storage screen. They enable the dentist to scan or import images for display, review or storage in a database. They consist of reusable phosphor storage screens for recording radiographic images, an image reader/digitizer, and software for displaying, enhancing, and storing dental radiographs using a userprovided personal computer.

This 510(k) summary (K990049) for the CD-Dent Digital-Imaging Device for Dental X-Ray Systems does not contain detailed information about acceptance criteria or a specific study proving the device meets those criteria. The provided document is primarily an administrative summary for a 510(k) submission, confirming the device's substantial equivalence to predicate devices.

Therefore, many of the requested details about acceptance criteria, study design, and performance metrics are not available in this document. The summary focuses on product description, intended use, and a list of predicate devices.

However, I can extract the available information and state where information is missing:

Response based on K990049 Summary:

1. Table of Acceptance Criteria and Reported Device Performance

Not available in the provided document. The 510(k) summary does not list specific acceptance criteria or provide a table of reported device performance against such criteria. The FDA's decision of substantial equivalence in this context typically relies on demonstrating that the new device is as safe and effective as a legally marketed predicate device, often through engineering analysis, performance testing, and comparison of technical characteristics, rather than explicitly stated acceptance criteria and a detailed performance table in the summary.

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

Not available in the provided document. The 510(k) does not describe a specific clinical or performance test set, its sample size, or the provenance of any data used for such a test.

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

Not available in the provided document. No information is given regarding ground truth establishment, experts, or their qualifications.

4. Adjudication Method for the Test Set

Not available in the provided document. No mention of an adjudication method is made.

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

Not available in the provided document. The summary does not indicate that an MRMC comparative effectiveness study was performed, nor does it discuss human reader improvement with or without AI assistance. The device is described as an "Accessory to Electrostatic X-Ray imaging system," which implies it is for image acquisition and display, not an AI diagnostic tool in the modern sense.

6. Standalone (Algorithm Only) Performance Study

Partially available/Inferable, but no specific study details. The core of a 510(k) submission for an imaging device often includes technical performance data to demonstrate that the device produces images of comparable quality to predicate devices. While a formal "standalone" algorithm performance study in the context of AI isn't detailed, the substantial equivalence determination implies that the imaging system itself (the "digitizer" and "software for displaying, enhancing, and storing dental radiographs") has met performance expectations. However, no specific study, metrics, or results are provided in this summary.

7. Type of Ground Truth Used

Not available in the provided document. The document does not specify any type of ground truth used for performance evaluation (e.g., expert consensus, pathology, outcomes data).

8. Sample Size for the Training Set

Not applicable/Not available in the provided document. Given the device is an "Accessory to Electrostatic X-Ray imaging system" and was approved in 1999, it is highly unlikely to involve machine learning or "AI" in the contemporary sense, which would require a training set. Therefore, there's no mention of a training set or its size.

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

Not applicable/Not available in the provided document. As there is no indication of a training set, this information is not provided.

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The Digora for Windows 2.0 is a software device intended to using and managing dental x-ray images sent by Digora imaging plate scanner, storing the images and allowing the user to process and examine the images in order to achieve improved diagnoses. The software can also innager other imaging devices such as larger size imaging plate scanners and intraoral video cameras.

Digora for Windows 2.0 is a dental imaging, image processing and archiving software, mainly to be used with Soredex Digora scanner (K934949). With this software is is possible to scan intra oral images using the Digora scanner, perform functions like edge enhancement, 3D emboss, density and contrast manipulation, length and angulation measurement with the image. The software also archives the images in the database, from where images can be stored in image media like CD-rom's.

The provided text describes a 510(k) submission for "Digora for Windows 2.0" dental imaging software. However, it does not contain detailed information regarding specific acceptance criteria, a comprehensive study design with sample sizes, expert qualifications, or ground truth establishment for performance evaluation.

The document focuses on establishing substantial equivalence to a predicate device ("RadWorks Medical Imaging Software") rather than presenting a detailed de novo performance study.

Based on the information provided, here's what can be extracted and what is missing:

Acceptance Criteria and Reported Device Performance

The document does not explicitly state quantitative acceptance criteria or detailed reported device performance metrics (e.g., accuracy, sensitivity, specificity, F1-score) in a tabular format related to diagnostic image quality or clinical outcomes.

Instead, the acceptance criteria are implicitly tied to demonstrating safety and effectiveness and substantial equivalence to the predicate device. The performance is described in terms of its features and capabilities being "substantially equivalent" to the predicate, with the implication that these features contribute to improved diagnoses.

Detailed Study Information (Missing or Not Applicable)

The document alludes to "laboratory and clinical tests" but provides no details on the methodology, sample sizes, or specific results of these tests. This 510(k) summary is primarily focused on demonstrating substantial equivalence, which often relies on a comparison of features and intended use rather than a full-scale, de novo clinical performance study with detailed metrics.

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

   • N/A (Not provided). The document mentions "clinical tests" and "laboratory tests" but does not specify the sample size of images or patients used for performance evaluation, nor does it state the country of origin or whether the data was retrospective or prospective.

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

   • N/A (Not provided). Ground truth establishment details are absent.

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

   • N/A (Not provided). Adjudication methods are not mentioned.

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:

   • N/A (Not provided). There is no mention of an MRMC study or any assessment of human reader improvement with or without AI assistance. This device is described as imaging software with processing capabilities, not explicitly as an AI diagnostic aid in the modern sense.

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

   • N/A (Not provided). No standalone performance metrics are discussed. The software is intended to allow a user to "process and examine the images in order to achieve improved diagnoses," implying human interpretation.

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

   • N/A (Not provided). The method for establishing ground truth is not specified.

7. The sample size for the training set:

   • N/A (Not provided). Training set information is not included, as the document doesn't detail the development or validation of machine learning algorithms requiring a distinct training phase.

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

   • N/A (Not provided). As no training set is discussed, its ground truth establishment is also not detailed.

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