(185 days)
EV Insite System by PSP Corporation is a device that receives medical images and data from various imaging sources. Images and data can be stored, communicated, processed and displayed within the system or across computer networks. Typical users of this system are trained professionals, including but not limited to physicians, radiologists, nurses, medical technicians, and assistants. Lossy compressed mammographic images must not be reviewed for primary image interpretations. Mammographic images may only be interpreted using an FDA cleared display that meets technical specifications reviewed and accepted by FDA.
The EV Insite System is comprised of two pieces of software: EV Insite R and EXtServer. It is a medical image display and processing software product that provides users with capabilities relating to the storage, communication, digital processing and display within the system or across computer networks of medical images. Images can be searched and displayed in a number of layouts as well as stacked. Image and information processing functions include: paging, magnification, gradation, annotation, PET/CT fusion, multiplaner reconstruction (MPR), Maximum Intensity Projection (MIP), superimposing, measurement, multi-display, flip, rotate, and mirror, cine window level adjustment, and comparison display.
The EV Insite system includes features to access and manage medical imaging studies from DICOM 3.0 compliant imaging modalities, and provides information after processing for diagnosis. The EV Insite System software displays image processing results and allows online image search and reading. It does not perform any automatic diagnosis.
The system does not produce any original medical images but can be used for primary diagnosis, except for lossy compressed mammographic images which must not be reviewed for primary image interpretations unless using an FDA cleared display. Mammographic images may only be interpreted using an FDA cleared display that meets technical specifications reviewed and accepted by FDA. All images located on the EV Insite system have been received from DICOM compliant modalities and/or image acquisition systems.
The EV Insite system allows trained professionals to display and manipulate images stored in DICOM archive devices. These trained professionals include but are not limited to physicians, radiologists, nurses, medical technicians, and assistants.
The EV Insite system is designed to be deployed over conventional networking infrastructure available in most healthcare organizations and utilizes commercially available computer platforms and operating systems.
The provided text describes a 510(k) premarket notification for the "EV Insite System" as a Picture Archiving and Communications System (PACS). It details the device's intended use, technological characteristics, and comparison to a predicate device (Centricity PACS-IW).
However, the document specifically states under "10. Clinical Performance Data":
"There was no human clinical testing required to support the medical device as the indications for use is equivalent to the predicate device. These types of devices, including the predicate devices, have been on the market for many years with proven safety and efficacy for the device. The non-clinical testing detailed in this submission supports the substantial equivalence of the device."
This means that a study proving the device meets acceptance criteria through clinical performance (i.e., involving human subjects, AI assistance, or expert interpretation) was not conducted or required for this 510(k) clearance. The clearance was primarily based on non-clinical performance data and the device's substantial equivalence to a predicate device with established safety and efficacy.
Therefore, I cannot provide the requested information regarding acceptance criteria and a study proving the device meets those criteria in terms of clinical performance, as such a study was explicitly stated as not being required and thus not present in the provided documentation.
The "Non-Clinical Performance Data" section (Section 9) refers to internal requirements and software verification and validation testing, which indicates the device met engineering and software requirements, not clinical performance metrics tied to human interpretation or AI performance.
To answer your specific points based on the provided document:
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A table of acceptance criteria and the reported device performance:
- Acceptance Criteria (Non-Clinical/Software): "The EV Insite System meets all the requirements for overall design results confirming that the design output meets the design inputs and specifications for the device." and "The EV Insite System passed all the testing in accordance with internal requirements shown below to support substantial equivalence of the subject device." Specific quantitative criteria for clinical performance are not provided as no clinical study was done.
- Reported Device Performance (Non-Clinical/Software):
- "Software verification and validation testing were conducted..."
- "The software for this device was considered as a 'moderate' level of concern, since a malfunction or a latent design flaw in the software device could lead to an erroneous diagnosis that would lead to a minor injury." (This is a risk categorization, not a performance metric).
- "Verification and validation of the EV Insite System Software consists of unit testing, software testing and system verification and validation."
- "The EV Insite System software was developed and tested following processes in compliance with IEC 62304: 2006 Medical Device Software - Software Life Cycle."
A table of quantitative clinical performance metrics is not available from this document.
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Sample sized used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective): Not applicable, as no human clinical testing or test set for clinical performance was used. The focus was on software testing and device functionality.
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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): Not applicable, as no human clinical testing or ground truth establishment for clinical performance was specified. The "trained professionals" mentioned are users, not necessarily experts for ground truth generation in a study.
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Adjudication method (e.g. 2+1, 3+1, none) for the test set: Not applicable, as no human clinical testing or test set for clinical performance was used.
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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: Not applicable. The device is a PACS system for displaying and processing images, not an AI-powered diagnostic aid that assists human readers in a comparative effectiveness study.
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If a standalone (i.e. algorithm only without human-in-the-loop performance) was done: Not applicable. The device is a PACS system, not an AI algorithm. Its function is to facilitate human interpretation, not to provide standalone diagnoses.
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The type of ground truth used (expert consensus, pathology, outcomes data, etc): Not applicable for clinical performance. For software verification, the "ground truth" would be the specified design inputs and expected behavior of the software.
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The sample size for the training set: Not applicable, as the device is not an AI/ML model that requires a training set of medical data for learning.
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How the ground truth for the training set was established: Not applicable, as the device is not an AI/ML model.
§ 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).