(63 days)
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.
§ 892.2050 Medical image management and processing system.
(a)
Identification. A medical image management and processing system is a device that provides one or more capabilities relating to the review and digital processing of medical images for the purposes of interpretation by a trained practitioner of disease detection, diagnosis, or patient management. The software components may provide advanced or complex image processing functions for image manipulation, enhancement, or quantification that are intended for use in the interpretation and analysis of medical images. Advanced image manipulation functions may include image segmentation, multimodality image registration, or 3D visualization. Complex quantitative functions may include semi-automated measurements or time-series measurements.(b)
Classification. Class II (special controls; voluntary standards—Digital Imaging and Communications in Medicine (DICOM) Std., Joint Photographic Experts Group (JPEG) Std., Society of Motion Picture and Television Engineers (SMPTE) Test Pattern).