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The HealthMyne software is a Picture Archiving and Communications System (PACS) intended to be used as a Digital Imaging and Communications in Medicine (DICOM) and non-DICOM information and data management system. The HealthMyne software displays, processes, stores, and transfers medical data from original equipment manufacturers (OEMs) that support the HL7 and DICOM (including DICOM-RT) standards, with the exception of mammography. It provides the capability to store images and patient information from OEM equipment, and perform filtering, digital manipulation and quantitative measurements, and export the product automatically and semiautomatically segments normal structures and abnormal structures (for example, nodules and lesions), and provides metrics for the structures.

The client software is designed to run on standard personal and business computers. The product is intended to be used by trained medical professionals, including but not limited to radiologists, and physicians. It is intended to provide image and related information (for example, image analysis) that is interpreted by a trained professional to render findings and/or diagnosis, but it does not directly generate any diagnosis or potential findings.

HealthMyne accesses the information in real-time so that current patients and images are available to a clinician. The clinician can filter and search the patient and image metadata to find the desired patient(s) and/or image(s). The clinician can view the images in various hanging protocol layouts. The layouts contain viewports of the slices within the image set, each annotated with patient information. Within the viewports the clinician can manipulate the image using standard tools: scroll, pan, zoom, window and level, and view the location of the slice in other viewports. The system can automatically segment and register data sets, and calculate metrics on clinician-identified nodules. When a clinician identifies a seed point for a nodule, the system automatically finds the extent of the nodule. Regions of interest (organs and nodules) can be viewed on the 2D data sets as contours and as 3D models. Metrics can be used to aid in analysis of a study, including the American College of Radiology RADS standards, and specifically with this release the LungRADs categories.

The provided text describes the HealthMyne software, a Picture Archiving and Communications System (PACS), and its comparison to a predicate device (VitreaAdvanced). However, it does not contain the detailed information necessary to fully answer all aspects of your request regarding acceptance criteria and a study proving the device meets those criteria.

Specifically, the document is a 510(k) summary for FDA clearance, which focuses on substantial equivalence to a predicate device rather than presenting a comprehensive clinical study with specific acceptance criteria, sample sizes, expert adjudication, or MRMC studies for AI performance improvement.

The only section that vaguely touches upon "studies" is "Summary of Studies" on page 7, which states: "The HealthMyne software has undergone verification and validation to confirm its functional performance. Non clinical testing conformance to the following FDA recognized industry standards applicable to PACS devices: DICOM standard for medical diagnostic images, HL7 standard for patient information, SMPTE display, and the JPEG2000 image standard." This refers to technical compliance and functional testing, not a clinical study to prove the device meets specific performance criteria related to diagnostic accuracy or AI assistance.

Therefore, I cannot provide a table of acceptance criteria with reported device performance or information on MRMC studies, ground truth establishment for a test set, etc., as this information is not present in the provided document.

I will, however, extract what information is available:

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

This information is not provided in the document. The document focuses on functional comparison with a predicate device, not on specific clinical performance metrics with pre-defined acceptance criteria.

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. The "Summary of Studies" mentions "verification and validation to confirm its functional performance" and "Non clinical testing conformance to...industry standards," but it does not describe a test set, its sample size, or data provenance.

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 provided in the document. No information on ground truth establishment for a test set is present.

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

This information is not provided in the document.

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

There is no indication of an MRMC comparative effectiveness study in the document. The device is described as a PACS system with features like segmentation and metrics, but not a device that directly assists with diagnosis in a way that would typically be evaluated by an MRMC study for improved human reader performance. Its intended use states "It is intended to provide image and related information (for example, image analysis) that is interpreted by a trained professional to render findings and/or diagnosis, but it does not directly generate any diagnosis or potential findings."

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

The document does not detail any standalone performance studies for an "algorithm." While it mentions the product "automatically and semi-automatically segments normal structures and abnormal structures (for example, nodules and lesions), and provides metrics for the structures," it does not present a standalone performance evaluation of these automatic features.

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

This information is not provided in the document.

8. The sample size for the training set

This information is not provided in the document. The document describes functional software capabilities, not an AI model that undergoes a training phase.

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

This information is not provided in the document. Similar to point 8, there's no mention of a training set or its ground truth.

In summary: The provided FDA 510(k) summary for HealthMyne focuses on demonstrating substantial equivalence to a predicate PACS device primarily through a feature-by-feature comparison and adherence to industry standards (DICOM, HL7, SMPTE, JPEG2000). It does not contain the detailed clinical study data, acceptance criteria, test set specifics, or AI performance metrics requested. The document emphasizes the device's role as a tool for trained medical professionals to interpret, rather than an AI system providing direct diagnoses or requiring extensive comparative effectiveness studies of human-AI collaboration.

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The HealthMyne PACS software is a Picture Archiving and Communications System (PACS) intended to be used as a Digital Imaging and Communications in Medicine (DICOM) and non-DICOM information and data management system. The HealthMyne PACS software displays, processes, stores, and transfers medical data from original equipment manufacturers (OEMs) that support the DICOM (including DICOM-RT) standard, with the exception of mammography. It provides the capability to store images and patient information from OEM equipment, and perform filtering, digital manipulation and quantitative measurements.

The client software is designed to run on standard personal and business computers. The product is intended to be used by trained medical professionals, including but not limited to radiologists, and physicians. It is intended to provide image and related information that is interpreted by a trained professional to render findings and/or diagnosis, but it does not directly generate any diagnosis or potential findings.

HealthMyne PACS accesses the information in real-time so that current patients and images are available to a clinician. The clinician can filter and search the patient and image metadata to find the desired patient(s) and/or image(s). The clinician can view the images in various hanging protocol layouts. The layouts contain viewports of the slices within the image set, each annotated with patient information. Within the viewports the clinician can manipulate the image using standard tools: scroll, pan, zoom, window and level, and view the location of the slice in other viewports.

Here's a summary of the acceptance criteria and the study information based on the provided text, where available:

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

The provided text does not explicitly list quantitative acceptance criteria for the HealthMyne PACS system (e.g., minimum accuracy, processing speed, etc.). The "Summary of Studies" section generally states that the device "has undergone verification and validation to confirm its functional performance" and "conformance to the following FDA recognized industry standards applicable to PACS devices." It does not provide specific performance metrics against defined criteria.

Therefore, a table of acceptance criteria and reported device performance cannot be generated from this document.

2. Sample size used for the test set and the data provenance (e.g., country of origin of the data, retrospective or prospective)

The document does not specify the sample size used for any test set, nor does it provide information on data provenance (country of origin, retrospective/prospective). It only mentions "non clinical testing conformance to the following FDA recognized industry standards."

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)

The document does not mention any experts used to establish ground truth for a test set or their qualifications. The nature of the device (a PACS system for managing and displaying images, not for diagnosis) suggests that human expert ground truth for interpretation might not be the primary focus of its validation, compared to, for example, a diagnostic AI algorithm.

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

The document does not describe any adjudication method.

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

A Multi-Reader Multi-Case (MRMC) comparative effectiveness study was not conducted based on the provided text. The device is a PACS system, not an AI-assisted diagnostic tool designed to directly improve human reader performance for diagnosis.

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

The device itself, HealthMyne PACS, is a "software-only medical device that can manage OEM medical diagnostic images." Its intended use states it "provides image and related information that is interpreted by a trained professional to render findings and/or diagnosis, but it does not directly generate any diagnosis or potential findings." This indicates it is a standalone system in its function as a PACS, but its output requires human interpretation. The testing described is "non clinical testing conformance to... industry standards" rather than a performance study of diagnostic output.

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

Given the nature of the device as a PACS for managing and displaying images, the concept of "ground truth" as typically applied to diagnostic AI algorithms (e.g., pathology for cancer detection) is not directly applicable. The "ground truth" for verifying this device would likely be related to the accuracy of image display, storage, manipulation, and transfer according to DICOM and other technical standards, rather than clinical diagnostic ground truth. The document mentions "conformance to the following FDA recognized industry standards applicable to PACS devices: DICOM standard for medical diagnostic images, SMPTE display, and the JPEG2000 image standard." This suggests the "ground truth" for testing was adherence to these technical specifications.

8. The sample size for the training set

The document does not mention any training set size. As a PACS system, it primarily manages and displays existing data, rather than being an AI model that learns from a training set in the conventional sense.

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

Not applicable, as no training set or its ground truth establishment is mentioned.

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