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Use as a software for the display and 3D visualization of dental image files from scanning devices such as CT, MRI for radiologists, dental clinicians and practitioners to acquire, process, render, review, store, print and distribute DICOM 3.0 complaint image studies, utilizing standard PC hardware.

Accurex™ is a dental imaging software which loads DICOM images taken from CT. MR and provides 3D visualization, 2D image reformation and various diagnosis tools for dentists on PC, laptop or PACS Workstation. Dentists can do further precise and accurate pre/post-operative diagnosis by using advanced image reformation and generation functionality of Accurex™ such as Panoramic image and Cephalometric x-ray imaqe as well as 3D volume rendering image.

The provided 510(k) summary for CyberMed, Inc.'s Accurex device (K061126) focuses on demonstrating substantial equivalence to predicate devices (V-works and Vimplant) based on its functional description and intended use. It does not contain information about specific acceptance criteria, a formal study design the proves the device meets the acceptance criteria (beyond functional testing), reader performance, or detailed ground truth establishment for a test set.

Therefore, I cannot populate the requested table and details regarding a specific study proving acceptance criteria. The document primarily describes the device's features and its compliance with DICOM 3.0 standards, comparing it generally to predicate devices in terms of functionality for image processing and visualization in a dental context.

However, I can extract information related to the device's capabilities and its intended use, which inherently imply certain functional acceptance.

Here's what can be inferred and what information is missing:

1. Table of Acceptance Criteria and Reported Device Performance

As noted above, no specific quantitative acceptance criteria or reported performance metrics (like sensitivity, specificity, accuracy, or reader agreement statistics) are provided in the document. The document asserts the device's functionality and substantial equivalence to previously cleared devices.

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

• Sample Size: Not specified.
• Data Provenance: Not specified. The document mentions loading DICOM images from CT/MR devices, but does not detail the origin or nature of data used for any testing.
• Retrospective/Prospective: Not specified.

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.

4. Adjudication method for the test set

• Adjudication Method: Not specified.

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 study is not mentioned or implied. The device is described as an image processing and visualization tool, not an AI diagnostic aid that would directly assist human readers in interpretation or improve their performance. The purpose is to provide "further precise and accurate pre/post-operative diagnosis" by enabling the practitioner to view and manipulate images.

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

• Standalone Performance: Not explicitly detailed with performance metrics. The document describes the algorithm's capabilities (e.g., 3D image construction via Volume Rendering) and states that "3D Algorithm was cleared in Vworks((K013878)." This implies that the core 3D processing algorithms underwent some form of validation in the predicate device, but specific standalone performance metrics for Accurex are not provided.

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

• Type of Ground Truth: Not specified in relation to any formal testing or validation within this document. The device is a display and processing software, implying that the "ground truth" would relate to the accuracy of image rendering, measurements, and reformations compared to the source DICOM data and established imaging principles, rather than a diagnostic outcome.

8. The sample size for the training set

• Training Set Sample Size: Not applicable. The document describes image processing software, not a machine learning or AI algorithm that would typically require a "training set" in the conventional sense for a diagnostic model. While algorithms might have been developed using data, a dedicated "training set" for a submitted AI model is not indicated.

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

• Ground Truth Establishment for Training Set: Not applicable, as no training set for an AI model is indicated.

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The Accurex Glucose Test Strip for use with the following glucometers: One Touchi Basic, OneTouch II and OneTouch Profile meters, all manufactured by Lifescan, Inc.

It is to be used for the quantitative measurement of glucose in fresh capillary whole blood. Glucose measurements are used as an aid to monitor the effectiveness of diabetes control.

The Accurex Glucose Test Strip is a generic replacement for the Lifescan One Touch Glucose Test Strip. These glucose test strips can be used with the following glucometers: One Touch Profile, One Touch II, One Touch Basic (with large screen), One Touch Basic (with small screen).

Here's an analysis of the provided text regarding the Accurex Glucose Test Strip, focusing on acceptance criteria and study details.

Important Note: The provided document is a 510(k) summary, which typically focuses on demonstrating substantial equivalence to a predicate device rather than presenting a detailed, standalone clinical trial report with specific acceptance criteria and detailed performance metrics as might be found in a more comprehensive study. Therefore, some of the requested information (especially about specific performance thresholds, full study design, and AI-related details) is not explicitly present in this type of regulatory submission.

Acceptance Criteria and Reported Device Performance

Study Details

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

   • Test Set Sample Size: The document does not explicitly state the specific sample size used for the performance evaluation that demonstrated equivalence.
   • Data Provenance: Not explicitly stated (e.g., country of origin). The document indicates performance comparison to a predicate device, which would typically involve testing of the new device. It is a regulatory submission for a medical device in the USA.
   • Retrospective/Prospective: Not explicitly stated. For device validation, prospective testing is common, but the nature of this summary means such details are omitted.

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

   • Not applicable/Not provided. For a glucose test strip, the "ground truth" is typically established by comparative measurements against a laboratory reference method (e.g., a highly accurate glucose analyzer) rather than expert consensus on interpretation. The document mentions "method comparison" and "technical comparison," implying such a comparison was done.

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

   • Not applicable/Not provided. Adjudication methods like 2+1 or 3+1 are typically used for subjective assessments (e.g., image interpretation by radiologists). For objective quantitative measurements like glucose levels, comparison to a reference method is standard.

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:

   • This is not applicable. The Accurex Glucose Test Strip is a standalone in-vitro diagnostic device for measuring glucose levels. It does not involve human readers interpreting results in the way an imaging AI device would, nor does it involve AI assistance for human performance.

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

   • Yes, for this type of device, the performance evaluation would be a "standalone" assessment of the device's accuracy and precision compared to a reference method. The device itself (the test strip in conjunction with compatible glucometers) is the "algorithm only" in this context. The reported substantial equivalence is based on this standalone performance.

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

   • The "ground truth" for a glucose test strip is typically established by comparing the device's readings to those obtained from a highly accurate laboratory reference method (e.g., a hexokinase method performed on a clinical chemistry analyzer). The summary mentions "method comparison," which supports this type of ground truth.

7. The sample size for the training set:

   • Not applicable in the context of this device. Glucose test strips are primarily electrochemical or photometric devices; they do not typically undergo "training" like machine learning algorithms with a dedicated training set. Their performance is validated through analytical and clinical studies against known standards or reference methods.

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

   • Not applicable, as there is no "training set" in the machine learning sense for this device.

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