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MED-SEG is a software device that receives medical images and data from various imaging sources (including but not limited to CT, MR, US, RF units), computed and direct radiographic devices, and secondary capture devices, (scanners, imaging gateways or imaging sources). Images and data can be stored, communicated, processed and displayed within the system or across computer networks at distributed locations.

In addition to general PACS use, this system can be used by trained professionals (e.g. physicians, radiologists, nurses, medical technicians, and assistants) to separate 2D images into "digitally related" sections or regions that, after colorization, can be individually labeled by the user. These images can be used to find appropriate window/level settings, to facilitate report generation and communication, or for other uses. These processed images should not be used for primary image diagnosis.

Only DICOM "for presentation" uncompressed or non-lossy compressed images can be used on an FDA cleared or approved monitor for primary image diagnosis in mammography.

MED-SEG™ software is a Picture Archiving and Communications System (PACS) for radiological image processing, storage, display and communication. The system can receive digital images via a secure internet communication link. The system incorporates parallel-computing algorithms that perform high-speed segmentation and regionalization. The system does not contact the patient nor does it control any life sustaining devices. A clinician interprets the images and information displayed by the system. providing ample opportunity for human intervention in the clinical decision process.

The provided text does not contain information about specific acceptance criteria, a study that proves the device meets those criteria, or detailed performance data in the format requested. The submission is a 510(k) summary for the Bartron Medical Imaging MED-SEG™ System, focusing on demonstrating substantial equivalence to a predicate device rather than providing a detailed performance study against defined acceptance criteria.

The key points from the provided document regarding performance are:

• Performance Data: "Laboratory testing documented that MED-SEG™ complies with the recognized consensus standards for radiological image processing systems." This is a general statement and does not include specific metrics, sample sizes, or methodologies.
• Substantial Equivalence: The primary argument for clearance is that MED-SEG™ is "substantially equivalent" to its predicate device (DEMASQ Imaging Software) because it "uses the same software technology, and complies with the same recognized consensus standards as its predicate device. It has the same intended use and indications for use as the predicate system." This implies that by meeting the same standards as the predicate, its performance is considered acceptable.

Therefore, I cannot populate the table or answer most of the questions because the document explicitly states that the device's substantial equivalence is based on using the "same software technology" and complying with "recognized consensus standards" as its predicate device, rather than providing a detailed performance study with acceptance criteria and results.

Here's how I would address your request based only on the provided text, indicating where information is absent:

Acceptance Criteria and Device Performance Study (Based on Provided Document)

1. Table of Acceptance Criteria and Reported Device Performance

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

• Sample Size (Test Set): Not specified in the provided document.
• Data Provenance: Not specified in the provided document. (e.g., country of origin, retrospective or prospective data)

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

• Not specified. The document indicates "laboratory testing" and compliance with consensus standards, but does not detail a study involving expert-established ground truth for a test set.

4. Adjudication method for the test set

• 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

• No MRMC comparative effectiveness study is mentioned. The device's purpose is to "separate 2D images into 'digitally related' sections or regions," facilitate "appropriate window/level settings," "report generation and communication," and for "other uses." It explicitly states: "These processed images should not be used for primary image diagnosis." The description suggests it's a tool for image manipulation and organization, not a diagnostic aid intended to improve human reader performance in primary diagnosis.

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

• Not explicitly mentioned. The "laboratory testing" for compliance to consensus standards might involve standalone evaluation, but details are not provided. The device is a "software device" with "parallel-computing algorithms that perform high-speed segmentation and regionalization," implying standalone algorithmic function. However, the regulatory submission focuses on its equivalence as a PACS and image processing system.

7. The type of ground truth used

• Not specified. Given the focus on "compliance with recognized consensus standards for radiological image processing systems" and substantial equivalence, the "ground truth" likely refers to the software's ability to accurately perform its functions (segmentation, regionalization, image processing, storage, display) as per those standards, rather than diagnostic ground truth (e.g., pathology, outcomes data).

8. The sample size for the training set

• Not applicable/Not specified. This document is a 510(k) summary for a PACS and image processing system, which, at least as described here, is not presented as an AI/ML device in the modern sense requiring a 'training set' for machine learning. Its functionality seems to be based on programmed algorithms for segmentation and regionalization, validated against established standards.

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

• Not applicable/Not specified, as no training set for an AI/ML model is mentioned.

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