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NICAL SMART MICRO CR system for intraoral dental images is indicated for capturing, digitization and processing of intra oral x-ray images stored in imaging plate recording media.

The NICAL/SMART MICRO is a Computed Radiography System which produces the Xray diagnostic image in digital format instead of using traditional screens and film. This device utilizes reusable X-ray storage phosphor plate (IP) that is sensitive to X-ray and stores latent image when it is exposed to X-ray. After X-ray exposure to the X-ray storage phosphor plate, X-ray storage phosphor plate is scanned by means of laser in the device. Latent image in the X-ray storage phosphor plate is released in a form of light by laser scanning. Then the light is collect and converted into a form of digital image. The signal processing is made to the digital image data such as the digital filtering, the gain & offset correction and flat fielding. The image can then be viewed on a computer workstation, adjusted if necessary, then stored locally, sent to an archive, printed or sent to PACS system. After acquisition of the latent image from the X-ray storage phosphor plate, it is erased thoroughly to be reused.

The NICAL SMART MICRO CR System for Intraoral Dental Images (K143703) was deemed substantially equivalent to its predicate device, the FireCR Dental by 3D Imaging & Simulations Corp. (K131442), based on technical performance and indications for use.

Here's a breakdown of the acceptance criteria and study information provided:

1. Table of Acceptance Criteria and Reported Device Performance

The submission primarily focuses on demonstrating equivalence to the predicate device, rather than explicit "acceptance criteria" against regulatory standards (beyond general safety and quality). However, the comparison table provided acts as a de facto set of acceptance criteria, where the NICAL device is expected to perform comparably or better than the predicate.

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

• Test Set Sample Size: The document does not specify a distinct "test set" in terms of a number of images or cases for non-clinical image quality testing. It mentions a "verification report on image testing" to check image quality and resolution.
• Data Provenance: Not explicitly stated for the non-clinical image testing or clinical images. The clinical images were reviewed by a "Board Certified US based radiologist," implying the images themselves might be from a US context or at least reviewed by a US expert.

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

• Number of Experts: One.
• Qualifications of Experts: "Board Certified US based radiologist." No years of experience are specified.

4. Adjudication method for the test set

• Adjudication method: Not applicable/not described as a formal adjudication process. The document states, "Clinical Images were reviewed by a Board Certified US based radiologist and were found to be of excellent quality, suitable for the intended use." This suggests a single expert opinion rather than a multi-reader adjudication.

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

• MRMC Study: No, an MRMC comparative effectiveness study was not done. This submission is for a Computed Radiography System, not an AI-based diagnostic aid that assists human readers.

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

• Standalone Performance: Not applicable in the context of an AI algorithm. The device itself is a standalone imaging system. The performance metrics reported (MTF, spatial resolution) are inherent to the device's image acquisition capabilities.

7. The type of ground truth used

• For the non-clinical image quality and resolution tests (e.g., MTF, spatial resolution), the "ground truth" would be established by physical measurements using standardized phantoms and imaging protocols.
• For the clinical images, the "ground truth" was effectively the expert opinion of a single Board Certified US based radiologist, who deemed them "of excellent quality, suitable for the intended use." This is closer to expert consensus (though only one expert) on diagnostic suitability rather than pathology or outcomes data.

8. The sample size for the training set

• Training Set Sample Size: Not applicable. This device is a Computed Radiography System; it is not an AI/ML device that requires a training set.

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

• Ground Truth for Training Set: Not applicable, as there is no training set for this type of device.

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DIGITAL IMAGE ACQUISITION SYSTEM TO BE USED IN CONJUCTION WITH AN IMAGE INTESIFIER DURING RADIOGRAPHY OR FLUOROSCOPY X-RAY EXAMINATION TO CAPTURE IMAGES BY A CAMERA, DIGITALIZE THE IMAGE, REVIEW IMAGES AND FORMAT IMAGES ACCORDING TO DICOM PROTOCOL TO BE SENT THROUGH NETWORK CONNECTION

The NICAL NDR+ is a digital image acquisition system to be used in conjunction with an image Intensifier during radiography or fluoroscopy x-ray examination to capture images by a camera, digitalize the image, review images and format images according to DICOM 3.0 protocol to be sent through network connection.

The provided text is a 510(k) summary for the NICAL SPA NDR+/DIVA-D digital image acquisition system. It focuses on demonstrating substantial equivalence to a predicate device and does not contain detailed information about acceptance criteria, specific device performance, or a clinical study proving its performance. The document is primarily a comparison of features and intended use between the NDR+/DIVA-D and the INFIMED Platinum ONE (ORION) system.

Therefore, I cannot provide a table of acceptance criteria and reported device performance, nor details about a study that proves the device meets specific acceptance criteria based solely on the provided text. The document does not describe any specific performance study, sample sizes, ground truth establishment, or multi-reader multi-case studies.

However, I can extract the information that is present:

1. Table of Acceptance Criteria and the Reported Device Performance:

This information is not available in the provided document. The 510(k) summary focuses on demonstrating substantial equivalence through feature comparison with a predicate device, rather than presenting specific performance metrics against pre-defined acceptance criteria from a study.

2. Sample size used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective):

This information is not available in the provided document. No test set or clinical study details are mentioned.

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):

This information is not available in the provided document. No ground truth establishment related to a clinical study is mentioned.

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

This information is not available in the provided document. No adjudication method for a test set is mentioned.

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:

This information is not available in the provided document. The device is a digital image acquisition system, not an AI-assisted diagnostic tool, and no MRMC study is mentioned.

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

This information is not available in the provided document. The device is an image acquisition and processing system, not an algorithm with standalone performance metrics.

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

This information is not available in the provided document, as no specific clinical study or ground truth establishment is described.

8. The sample size for the training set:

This information is not available in the provided document, as no machine learning or AI training is described.

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

This information is not available in the provided document, as no machine learning or AI training is described.

Summary of what is available from the document:

• Device Trade Name: NDR+/DIVA-D
• Common Name: Digital image acquisition system
• Intended Use: "DIGITAL IMAGE ACQUISITION SYSTEM TO BE USED IN CONJUNCTION WITH AN IMAGE INTENSIFIER DURING RADIOGRAPHY OR FLUOROSCOPY X-RAY EXAMINATION TO CAPTURE IMAGES BY A CAMERA, DIGITALIZE THE IMAGE, REVIEW IMAGES AND FORMAT IMAGES ACCORDING TO DICOM PROTOCOL TO BE SENT THROUGH NETWORK CONNECTION." (This indicates it's a system for acquiring, digitalizing, reviewing, and formatting X-ray images, not a diagnostic AI device requiring a clinical performance study with "acceptance criteria" in the typical sense for a new diagnostic claim.)
• Predicate Device: INFIMED Platinum ONE (certified under the name ORION) (K012490)
• Comparison with Predicate Device features: The document primarily lists technical specifications and features of the NDR+/DIVA-D and compares them to the predicate device to establish substantial equivalence. These features include:
  • Image Acquisition (CCD camera 1024x1024 12 bit for both)
  • Speed Acquisition (NDR+/DIVA-D: 1024x1024 at 25 fps for FLUORO and all radiographic exams; INFIMED: Up to 30 fps for FLUORO acquisition and up to 15 fps for spot acquisition)
  • Edge Enhancer (NDR+/DIVA-D: Completely hardware in real time; INFIMED: Software package)
  • Noise Reduction (NDR+/DIVA-D: Dynamic recursive filter; INFIMED: Standard recursive filter)
  • Image Storage, Post Processing, DSA Application, Multi Image, DICOM Functions, Laser Interface, Interface Commands.

The 510(k) process for this type of device typically relies on demonstrating that the new device is as safe and effective as a legally marketed predicate device, often through technical comparison and compliance with relevant standards, rather than extensive clinical performance studies to meet specific "acceptance criteria" for a diagnostic accuracy claim.

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