(143 days)
SynthVISION is a software application that can be used within a web -browser to process and view DICOM and non-DICOM image data and associated medical in the day-to-day diagnostic activities of medical imaging professionals and those involved in the care of a patient.
SynthVISION is a medical imaging viewing software used with off-the-shelf workstation hardware and web browsers for the 2D & 3D diagnostic visualization of DICOM and non-DICOM medical images by intended users such as trained radiologists, technologists and all others involved in the patient's care.
SynthVISION consists of configurable software-only modules that display and process DICOM and non-DICOM images and associated medical information to aid in the day-to-day operations and workflow of imaging healthcare professionals, clinicians and other healthcare practitioners.
SynthVISION has the following primary features and functions -
- Zero-footprint medical image upload, transfer, and display of medical images between facilities
- Easy access to images for all participants in the healthcare process, including radiologists, physicians, nurses and others who participate in patient care
- Serves as information and data management system for DICOM and non-DICOM medical images
- Industry-standard tools for image manipulation, annotation and measurement ●
- Metadata information and orientation labels display
- . Advanced image manipulation functions like view synchronization across series, MIP and MPR
- Advanced image processing filters
- Encrypted transmission of medical images through secured networks
- Encrypted storage of medical images
- . HIPAA-compliant data management, including centralized storage of user activities via audit trails.
The provided text, a 510(k) summary for Synthesis Health Intelligence Inc.'s SynthVISION 1.0.0, does not contain specific acceptance criteria or details of a study that proves the device meets such criteria in the manner typically expected for medical device performance evaluation.
The document primarily focuses on demonstrating substantial equivalence to a predicate device (eUnity) through non-clinical testing. It explicitly states that clinical tests were not conducted.
Therefore, many of the requested information points cannot be extracted from this document, as they pertain to clinical performance studies.
However, I can extract information regarding the non-clinical tests performed and their general conclusions which serve as a form of acceptance criteria for software functionality and equivalence.
Here's a breakdown of the available information based on your request:
Acceptance Criteria and Reported Device Performance
Since no specific numerical performance metrics (e.g., sensitivity, specificity, AUC) are provided in this document as acceptance criteria, the "acceptance criteria" here are inferred from the stated purpose and conclusions of the non-clinical tests, which aim to demonstrate functional equivalence and safety.
| Acceptance Criteria (Inferred from Test Purpose) | Reported Device Performance (Conclusion) |
|---|---|
| Software Verification and Validation: Reliability, accuracy, and security of image processing and display functionalities. | SynthVISION functions accurately and reliably when compared to the predicate device. 100% of the tests passed verification, meeting specified requirements. |
| Usability Testing: Ease of use, user interface design, and overall user experience for effective and safe use by healthcare professionals. | SynthVISION offers an intuitive and user-friendly interface, providing an equivalent experience to the predicate device, minimizing user errors. |
| Performance Testing: Accuracy and effectiveness in processing and displaying medical images, meeting specified performance criteria and equivalence to the predicate device. | The intended use, functionality, and performance of SynthVISION 1.0.0 and the predicate device are equivalent. |
| System Safety and Risk Analysis: Identification and mitigation of potential hazards. | Risk mitigation strategies were implemented, demonstrating commitment to safety and prevention of adverse events. |
Study Details from the Provided Text:
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A table of acceptance criteria and the reported device performance: (See above table.)
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Sample size used for the test set and the data provenance:
- Test Set Sample Size: Not specified. The document refers to "key software components," "user interfaces," "image quality assessments," and "tool tests" without quantifying the number of elements or cases involved.
- Data Provenance: Not specified. Given that clinical tests were not performed, the "data" for these non-clinical tests would likely refer to synthetic data, representative DICOM/non-DICOM images, and interactive user scenarios. No country of origin is mentioned. The tests are described as non-clinical.
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Number of experts used to establish the ground truth for the test set and the qualifications of those experts: Not applicable. Since these were non-clinical software tests aimed at functionality and usability equivalence, not diagnostic accuracy, there is no mention of "ground truth" adjudicated by medical experts for diagnostic purposes. Usability testing would involve users, but the number and qualifications are not provided.
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Adjudication method (e.g. 2+1, 3+1, none) for the test set: Not applicable. There was no diagnostic test set requiring adjudication in this technical documentation.
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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 document explicitly states "Clinical tests: Not Applicable." SynthVISION is primarily a medical image viewing software, not an AI diagnostic aid requiring an MRMC 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 viewer, designed for human use. The performance tests ("Image quality assessments, tool tests, display requirements") represent standalone software capabilities validation, but not in the diagnostic sense often implied by this question for AI algorithms.
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The type of ground truth used (expert consensus, pathology, outcomes data, etc.): Not applicable for diagnostic "ground truth." The "ground truth" for the non-clinical tests would be the expected software behavior and output based on design specifications and the predicate device's functionality. For example:
- Software Verification: Specifications compliance.
- Usability Testing: User feedback, task completion rates, error rates (compared to predicate).
- Performance Testing: Expected image display accuracy, measurement accuracy, tool functionality (compared to predicate).
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The sample size for the training set: Not applicable. This document does not describe the development or testing of an AI algorithm that would require a training set. SynthVISION is described as a "medical image management and processing system" and a "software application" for viewing, manipulation, and display of images.
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How the ground truth for the training set was established: Not applicable, as there is no mention of a training set or AI 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).