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Ashvins is a modular software solution and is used for viewing, reporting, storing, printing, and archiving, as well as for the exchange and distribution or web-based distribution of digital, multimodal medical images, including mammographic images and bio-signals, as well as findings and demographic information. Ashvins also collects, manages, and distributes patient-related and device-related information.

Ashvins offers advanced image processing functions such as multiplanar reconstruction, 3D reconstruction and measurement functions.

For primary image diagnosis in Mammography only uncompressed or non-lossy compressed images and only preprocessed DICOM "For Presentation" images must be used. Existing regulatory or legal requirements regarding hardware, such as diagnostic monitors, that are applicable for diagnosing in respective imaging specialties must be observed.

Ashvins is designed to be used by trained professionals, including but not limited to physicians, radiologists, ophthalmologists, nurses, medical technicians, and assistants.

Ashvins is a modular software solution and is used for viewing, reporting, storing, printing, and archiving, as well as for the exchange and distribution or web-based distribution of digital, multimodal medical images, including mammoqraphic images and bio-signals, as well as findings and demographic information. Ashvins also collects, manages, and distributes patient-related and device-related information.

Ashvins offers advanced image processing functions such as multiplanar reconstruction, 3D reconstruction and measurement functions.

Ashvins does not utilize artificial intelligence or computer-aided diagnosis to identify abnormalities in medical images or to assist in diagnosis.

For primary image diagnosis in Mammography only uncompressed images and only preprocessed DICOM *For Presentation" images must be used. Existing regulatory or legal require-ments regarding hardware, such as diagnostic monitors, that are applicable for diagnosing in respective imaging specialties must be observed.

Ashvins is designed to be used by trained professionals, including but not limited to physicians, radiolo-gists, ophthalmologists, nurses, medical technicians, and assistants.

Ashvins integrates systems including, but not limited to, devices and information systems for medical practices and hospitals (such as radiology and/or ophthalmology information systems, etc.) in accord-ance with international standards. Ashvins also provides manufacturer-specific interfaces for other sys-tems/devices.

Ashvins offers the possibility of capturing and digitizing documents such as PDFs, single images, image sequences and film sequences, as well as converting and archiving them in DICOM format. As such, Ash-vins serves as a manufacturer-independent image management system and archive, a so-called "Vendor Neutral Archive."

Ashvins also provides functions for the collection, processing, and transfer of medical and administrative data in hospitals or medical practices, including billing functions.

Ashvins allows the visualization and statistical evaluation of meta data provided by the devices.

Ashvins is used at a physician's workplace to assist the physician with diagnosis and treatment planning, but the product itself does not generate an automated diagnosis, findings, or a treatment plan. The final decision regarding the diagnosis always remains with the physician or the medical staff within their own decision-making area.

Ashvins is offered in different variants with different ranges of functions for different application areas depending on the configuration (for further information see document "General Description of Product Vari-ants").

The provided text is a 510(k) summary for a Picture Archiving and Communication System (PACS) named Ashvins. It states that Ashvins does not utilize artificial intelligence or computer-aided diagnosis to identify abnormalities in medical images or to assist in diagnosis. Therefore, the information typically associated with AI/CADe/CADx devices regarding acceptance criteria, performance studies with ground truth, expert readers, and human-in-the-loop studies is not present in this document.

The document primarily focuses on demonstrating substantial equivalence to predicate PACS devices based on intended use, technological characteristics, and general software validation principles.

Based on the information provided, here's what can be extracted and what cannot:

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

The document does not provide a specific table of acceptance criteria with quantitative performance metrics for a diagnostic task. Instead, it states:

• "MedicalCommunications performed software verification and validation, to confirm that Ashvins functions as intended."
• "The performance data demonstrates continued conformance with special controls for medical devices containing software."
• "Non-clinical tests were conducted on the device Ashvins (and variants) during product development."
• "The testing results support that all the software specifications have met the acceptance criteria."
• "Testing for verification and validation for the device was found acceptable to support the claims of substantial equivalence."
• "Performance tests were conducted to test the functionality of Ashvins (and variants). These tests have been performed to assess the functionality of the subject device. Results of all conducted testing were found acceptable in supporting the claim of substantial equivalence."

In summary, the acceptance criteria are general functional requirements, and the reported performance is that the device "met the acceptance criteria" and "functions as intended." No specific quantitative metrics like sensitivity, specificity, AUC as would be seen for an AI/CADe/CADx device are listed.

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 provided in the document, as it is a PACS system and not an AI/CADe/CADx device. The testing described focuses on software functionality, not diagnostic performance on a dataset of patient images.

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 applicable and therefore not provided given the device description. Ground truth establishment by experts is typically required for AI/CADe/CADx devices that make diagnostic assessments. Ashvins is described as a PACS system that "does not utilize artificial intelligence or computer-aided diagnosis to identify abnormalities in medical images or to assist in diagnosis."

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

This information is not applicable and therefore not provided given the device description.

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

An MRMC study was not performed, and is not applicable for this device as it does not include AI or CAD for diagnostic assistance. The document explicitly states: "Ashvins does not utilize artificial intelligence or computer-aided diagnosis to identify abnormalities in medical images or to assist in diagnosis."

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

This information is not applicable and therefore not provided as Ashvins is a PACS system for viewing, processing, and managing images, not a diagnostic algorithm.

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

This information is not applicable and therefore not provided. The validation described is for software functionality and substantial equivalence to predicate PACS systems, not for diagnostic accuracy against a clinical ground truth.

8. The sample size for the training set

This information is not applicable and therefore not provided as Ashvins is a PACS system and not an AI/ML or CAD device that requires a training set of medical images.

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

This information is not applicable and therefore not provided for the reasons stated above.

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