(15 days)
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.
§ 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).