Search Results

Dual Energy and Tissue Equalization software options are intended for use in generating digital radiographic images of human anatomy, EXCEPT MAMOCRAMS.
Dual Energy and Tissue Equalization software options are intended for use in generating Dual Energy and Though Equalization sontomy. This device is not intended for mammographic applications.

Dual Energy is a technique whereby two images are acquired at different x-ray energies and then used to create two derived images, for example soft tissue and bone.

The Tissue equalization algorithm is used to enhance the contrast in thick areas while maintaining suitable contrast in the primary area of interest.

This 510(k) submission (K013481) describes Dual Energy and Tissue Equalization software options for digital radiographic systems. The submission primarily focuses on establishing substantial equivalence to a predicate device (K012389) and ensuring conformance to relevant regulations and standards.

Based on the provided document, there is no specific study described that proves the device meets detailed acceptance criteria in terms of performance metrics like sensitivity, specificity, accuracy, or any quantitative effect size of human improvement with AI assistance. The submission primarily addresses regulatory conformance and device description.

Here's an analysis of the requested information based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

Note: The document does not provide performance metrics (e.g., accuracy, sensitivity, specificity, contrast enhancement ratios, or specific image quality improvements) that would typically be associated with clinical acceptance criteria for newly developed medical imaging software. The "acceptance criteria" here are primarily regulatory and equivalence-based.

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

• Sample Size: Not specified.
• Data Provenance: Not specified. It's highly probable that no dedicated test set for performance evaluation was used, given the nature of a 510(k) submission based on substantial equivalence to software for a different system, rather than a de novo device with novel performance claims.

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.
  This information is not provided because no dedicated clinical performance study with a test set and ground truth establishment is described.

4. Adjudication method for the test set

• Adjudication Method: Not specified.
  This information is not provided because no dedicated clinical performance study with a test set and ground truth establishment is described.

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.
• Effect Size: Not applicable, as no such study was conducted or reported.
  This device is software for image generation and enhancement, not a diagnostic AI intended to assist human readers in interpretation.

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

• Standalone Performance: No specific standalone performance study (e.g., quantitative image quality metrics, diagnostic accuracy of the algorithm itself) is described in the provided text beyond conformance to standards. The device is software that modifies images for human interpretation, not an automated diagnostic tool.

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

• Type of Ground Truth: Not applicable, as no clinical performance study requiring ground truth is described.

8. The sample size for the training set

• Sample Size for Training Set: Not specified.
  While the device uses "algorithms," there is no mention of a machine learning (ML) component that would typically require a distinct training set in the contemporary sense. The "algorithms" likely refer to conventional image processing techniques (e.g., dual energy subtraction, tissue equalization filters) that are deterministic and traditionally don't involve "training" with a dedicated dataset in the ML context.

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

• How Ground Truth was Established: Not applicable, as no training set requiring ground truth is described in the traditional machine learning sense.

Ask a specific question about this device