(39 days)
The EXCELSIOR Software is intended for use in importing, processing, measurement, analysis and storage 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.
The EXCELSIOR software is a cloud-based software that provides a grading platform integrating remote data collection, quantitative analysis and measurement, storage and management of ophthalmic data and images for clinical trials.
EXCELSIOR provides secure access through user authentication and role authorization, and is adherent to HIPAA and CFR 21 part 11 requirements for clinical investigations. EXCELSIOR user accounts can only be initiated by EXCELSIOR administrators or project managers: administrators or project managers enter in user information, and the user is prompted by email to setup his own user name and password. Once setup, the user access is defined by its role associated with a particular trial. EXCELSIOR provides 128-bit secure network encryption and audit trail logging to ensure that changes to the data are retraceable and re-constructible.
Here's a breakdown of the acceptance criteria and study information for the EyeKor EXCELSIOR Software, based on the provided text:
1. Table of Acceptance Criteria and Reported Device Performance
The provided document describes the EXCELSIOR Software as an "image management system," primarily focusing on its functional equivalence to predicate devices (Topcon Medical Systems Synergy and Carl Zeiss Meditec Forum) rather than a diagnostic device with specific performance metrics like sensitivity or specificity.
Therefore, the "acceptance criteria" discussed are related to its intended use and technological characteristics rather than direct performance in a diagnostic task. The document asserts that the device meets these criteria by being substantially equivalent to the predicates.
| Acceptance Criteria Category | Specific Criteria (Implicitly Met by Substantial Equivalence) | Reported Device Performance (as stated in the document) |
|---|---|---|
| Intended Use | Importing ophthalmic clinical images | Intended for importing ophthalmic clinical images. |
| Processing ophthalmic clinical images | Intended for processing ophthalmic clinical images. | |
| Measurement of ophthalmic clinical images | Performs distance measurements, area measurements, ETDRS grid measurements, OCT layer thickness measurements, and macular grid summary. | |
| Analysis of ophthalmic clinical images | Intended for analysis of ophthalmic clinical images. | |
| Storage of ophthalmic clinical images | Intended for storage of ophthalmic clinical images. | |
| Management of clinical data (ophthalmic) | Intended for management of clinical data (ophthalmic). | |
| Use through a computerized network | Operates through a computerized network. | |
| Use in analysis of images/data obtained in clinical trials | Specifically intended for use in clinical trials. | |
| Technological Characteristics | Client-server based application | Client-server based application. |
| Web-based viewing of ophthalmic images | Web-based viewing of ophthalmic images. | |
| Central database for image storage | Images stored in a central database. | |
| Accessed from a PC using Windows server OS | Accessed from a PC using a Windows server operating system. | |
| Built in a SQL database | Built in a SQL database. | |
| Supports DICOM files (fundus photos, OCT) | Supports DICOM files for fundus photographs and OCT images. | |
| Supports PDF files for ophthalmic reports | Supports use of PDF files for reviewing ophthalmic reports. | |
| Ability to search for records | Allows the user to search for records (using subject IDs). | |
| Regulatory/Certification | Compliance with DICOM standard | Tested and found in compliance with DICOM. |
| Software validation and verification | Performed software validation and verification tests. | |
| HIPAA Adherence | Adherent to HIPAA requirements. | |
| CFR 21 Part 11 Adherence | Adherent to CFR 21 part 11 requirements. | |
| Secure access (user authentication, role authorization) | Provides secure access through user authentication and role authorization. | |
| 128-bit secure network encryption | Provides 128-bit secure network encryption. | |
| Audit trail logging | Provides audit trail logging. |
2. Sample Size Used for the Test Set and Data Provenance
The provided text does not contain any information about a specific test set, its sample size, or data provenance (e.g., country of origin, retrospective/prospective). The "Performance Data" section primarily mentions compliance with DICOM and general software validation/verification, but not a clinical performance test set.
3. Number of Experts Used to Establish the Ground Truth for the Test Set and Qualifications
This information is not provided in the document as there's no mention of a clinical test set requiring expert-established ground truth.
4. Adjudication Method for the Test Set
This information is not provided in the document.
5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study
The document does not mention a multi-reader multi-case (MRMC) comparative effectiveness study, nor does it provide any effect size for human readers with and without AI assistance. This device is an image management system, not a diagnostic AI.
6. Standalone Performance Study (Algorithm Only)
The document does not describe a standalone performance study in the context of a diagnostic algorithm's performance. The "performance data" refers to compliance with standards and general software functionality testing.
7. Type of Ground Truth Used
Since a clinical performance evaluation with a test set is not described, the concept of "ground truth" in terms of pathology, expert consensus, or outcomes data is not applicable in the provided text. The "ground truth" for the software validation would be adherence to functional specifications and standards like DICOM.
8. Sample Size for the Training Set
The document does not provide any information regarding a training set sample size, as it describes an image management system rather than a machine learning-based diagnostic algorithm that would typically require a training set.
9. How the Ground Truth for the Training Set Was Established
As no training set is mentioned (see point 8), there is no information on how its ground truth would have been established.
§ 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).