(214 days)
Ophthalmology
The EXCELSIOR Software is intended for use in importing, processing, measurement, and analysis of ophthalmic clinical images as well as in management of clinical data, through a computerized network for use in analysis of images and data obtained in clinical trials.
Radiology
Excelsior is a software solution intended to be used for viewing, manipulation, annotation, and ysis, and comparison of medical images from multiple imaging modalities and/or multiple time points. The application supports images, functional data such as PET as well as anatomical datasets, such as CT or MR. Excelsior is a software only medical device to be deployed through a cloud-based computerized network via web applications and customized user interfaces for use in the analysis of images and data obtained in clinical trials. Excelsior enables visualization of information that would otherwise have to be visually compared disjointedly. Excelsior provides and workflow automation tools to help the user assess and document the extent of a disease and/or the response to therapy in accordance with user selected standards and assess changes in imaging findings over multiple time-points. Excelsior supports the interpretation and evaluations and follow-up documentation of findings for radiologic oncology imaging and data obtained in clinical trials.
The product is intended to be used as a workflow automation tool by trained medical professionals. It is intended to provide image and related information that is interpreted by a trained professional but does not directly generate any diagnosis or potential findings.
Note: The medical professional retains the ultimate responsibility for making the pertinent observations based on their standard practices and established practices related to clinical trial outcomes. Excelsior is a complement to these standard procedures. Excelsior is not to be used in mammography.
The EXCELSIOR software is a cloud-based software that provides a central reading platform integrating remote data collection, quantitative analysis and measurement, storage, and management of ophthalmic and radiological data from DICOM images for clinical trials. The software does not use artificial intelligence or machine learning algorithms.
The provided text is a 510(k) summary for the EXCELSIOR software. It describes the device's intended use, classification, predicate devices, and the rationale for substantial equivalence. However, it does not include the specific details required to answer your request regarding acceptance criteria and the study that proves the device meets those criteria.
Specifically, the document states:
- "Software validation and verification testing was performed which showed that the software performs as intended supporting substantial equivalence."
- "The methodology used to validate and verify that the software performs as intended was used to confirm performance of the additional tools and features."
These statements confirm that testing was done, but they do not provide the raw data, acceptance criteria, study design details, or expert qualifications that you've asked for.
Therefore, I cannot extract the following information from the provided text:
- A table of acceptance criteria and the reported device performance: Not provided.
- Sample size used for the test set and the data provenance: Not provided.
- Number of experts used to establish the ground truth for the test set and the qualifications: Not provided.
- Adjudication method for the test set: Not provided.
- If a multi-reader multi-case (MRMC) comparative effectiveness study was done, and its effect size: Not provided. The text implies the device is a workflow tool, not an AI for diagnosis, making MRMC less likely to be the primary evaluation method for diagnostic improvement.
- If a standalone (i.e. algorithm only without human-in-the-loop performance) was done: Not explicitly detailed. The text states "The software does not use artificial intelligence or machine learning algorithms," implying it's a tool for human use, not a standalone diagnostic algorithm.
- The type of ground truth used: Not provided.
- The sample size for the training set: Not applicable, as the document explicitly states: "The software does not use artificial intelligence or machine learning algorithms." Therefore, there is no training set as understood in the context of AI/ML.
- How the ground truth for the training set was established: Not applicable for the reason above.
In summary, the document states that validation and verification testing was performed and supports substantial equivalence, but it does not disclose the specifics of these tests, including acceptance criteria, performance metrics, or study design details.
§ 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).