Search Results

ProVecta 3D Prime:

ProVecta 3D Prime is computed tomography x-ray unit intended to generate 3D and panoramic X-ray images in dental radiography for adult and pediatric patients. It provides diagnostic details of the maxillofacial areas for a dental treatment. The device is operated and used by physicians, dentists, and x-ray technicians. Not intended for mammography use.

VistaSoft:

The VistaSoft software functions for recording, displaying, analyzing, diagnosing, managing and sending digital or digitized video and X-ray images in dental practices and specialist dental clinics. Not intended for mammography use.

This device is a cone beam CT x-ray device for the acquisition of dental images. Similar to computer tomography or magnetic resonance tomography, sectional images can be generated with CBCT. With CBCT, an X-ray tube and an imaging sensor opposite it rotate around a seated or standing patient. The X-ray tube rotates through 180°-540° and emits a conical X-ray beam. The X-rays pass through the region under investigation and are measured for image generation by a detector as an attenuated grey scale X-ray image. Here, a large series of two-dimensional individual images is acquired during the revolution of the X-ray tube. Using a mathematical calculation on the rotating image series via a reconstruction computer, a grey value coordinate image is generated in the three spatial dimensions. This three-dimensional coordinate model corresponds to a volume graphic that is made up of individual voxels. This volume can be used to generate sectional images (tomograms) in all spatial dimensions as well as 3D views. The system complies with US Radiation Safety Performance Standard.

The provided text is a 510(k) Premarket Notification summary for the ProVecta 3D Prime with VistaSoft device, a dental computed tomography x-ray system. The document focuses on demonstrating substantial equivalence to a predicate device, rather than proving the device meets specific acceptance criteria through a clinical study. Therefore, much of the requested information regarding acceptance criteria and performance study details is not explicitly available in this document.

However, based on the information provided, here's what can be inferred and explicitly stated:

Overall Conclusion from the Document:
The device's performance is not evaluated against formal "acceptance criteria" in the sense of a new feature or algorithm being validated. Instead, the focus is on demonstrating that the device's technical characteristics and image quality are equivalent or non-inferior to the predicate device, thereby not raising new questions of safety or effectiveness. The statement "The overall impression is sufficient for dental diagnostics. Good contrast and good resolution. Teeth, osseous structures, sinus maxillaries are clearly shown" acts as a qualitative assessment of diagnostic quality, though not tied to specific acceptance criteria.

1. A table of acceptance criteria and the reported device performance

Since this is a 510(k) submission primarily focused on demonstrating substantial equivalence, formal quantifiable "acceptance criteria" for a new AI algorithm's performance are not explicitly defined or measured in a distinct table in the provided text. The performance assessment is comparative to the predicate device's specifications for imaging parameters.

Study Proving Acceptance Criteria:

The document describes "Non-Clinical Data and Performance Testing" and "Clinical Data" that generally support the claim of substantial equivalence. This is not a study designed to meet specific quantitative "acceptance criteria" for a novel AI feature, but rather to show general safety and effectiveness in comparison to an existing device.

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

• Sample Size for Test Set: Not explicitly stated as a separate test set in the context of an AI/algorithm-driven performance study. The "Clinical Data" section mentions "performed to assure diagnostic quality of the dental images" but does not quantify the number of images or patients.
• Data Provenance: Not specified. It's likely from internal testing using the device, but the country of origin, retrospective or prospective nature of the "clinical data" is not detailed.

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

• Number of Experts: Not specified.
• Qualifications of Experts: Not specified. The document states "The overall impression is sufficient for dental diagnostics. Good contrast and good resolution. Teeth, osseous structures, sinus maxillaries are clearly shown," implying evaluation by dental professionals, but no details on their number or qualifications.

4. Adjudication method for the test set

• Adjudication Method: Not applicable/not described. There is no mention of multiple readers or an adjudication process for a ground truth establishment in a test set.

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. The document does not describe an MRMC study or any study comparing human reader performance with and without AI assistance. The device is a CT X-ray system and image viewing software, not explicitly an AI-assisted diagnostic tool in the sense of a standalone algorithm for pathology detection. The "VistaSoft" software is for general image management and viewing.

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

• Standalone Performance: Not applicable/not described. The device is a hardware/software system for generating and viewing images, not a standalone algorithm for automated diagnosis or a specific AI task. The "VistaSoft" software is an imaging software that is used by physicians, dentists, and x-ray technicians.

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

• Type of Ground Truth: Not explicitly defined in terms of a formal ground truth for validating an AI algorithm. The statement "The overall impression is sufficient for dental diagnostics. Good contrast and good resolution. Teeth, osseous structures, sinus maxillaries are clearly shown" suggests a qualitative clinical assessment, effectively an expert-based subjective evaluation of image quality rather than a formal ground truth for a diagnostic task.

8. The sample size for the training set

• Sample Size for Training Set: Not applicable/not described. This document is for a traditional medical imaging device, not a machine learning/AI model that would have a distinct training set. The software mentioned (VistaSoft) is image management and viewing software, not a deep learning algorithm that requires a training set in the typical sense.

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

• Ground Truth for Training Set: Not applicable/not described, as there is no mention of an AI model with a training set.

Ask a specific question about this device