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Olea Sphere is an image processing software package to be used by trained professionals including but not limited to physicians and medical technicians. The software runs on a standard "off-the-shelf" workstation and can be used to perform image viewing, processing and analysis of medical images. Data and images are acquired through DICOM compliant imaging devices and modalities.

Olea Sphere provides both viewing and analysis capabilities of functional and dynamic imaging datasets acquired with MRI or other relevant modalities, including a Diffusion Weighted MRI (DWI) / Fiber Tracking Module and a Dynamic Analysis Module (dynamic contrast enhanced imaging data for MRI and CT).

The DWI Module is used to visualize local water diffusion properties from the analysis of diffusion-weighted MRI data. The Fiber Tracking feature utilizes the directional dependency of the diffusion to display the white matter structure in the brain or more generally the central nervous system.

The Dynamic Analysis Module is used for visualization and analysis of dynamic imaging data, showing properties of changes in contrast over time where such techniques are useful or necessary. This functionality is referred to as:

Perfusion Module - the calculation of parameters related to tissue flow (perfusion) and tissue blood volume.

Permeability Module - the calculation of parameters related to leakage of injected contrast material from intravascular to extracellular space.

Olea Sphere is a medical viewing, analysis and processing software package (PACS) compliant with the DICOM standard and running on Windows, Macintosh or Linux operating systems.

Olea Sphere allows the display, analysis and post-processing of medical images.

These images, when interpreted by a trained physician, may yield clinically useful information.

The software provides a wide range of basic image processing and manipulation functions, in addition to comprehensive dynamic image processing and display.

The main features of the software are:

• Image Loading & Saving
• Image Viewing
• Image Manipulation
• Image Analysis
• Imaging Processing
• Perfusion Post-processing
• Permeability Post-processing
• Diffusion Weighted Image / Tensor Image Post-processing
• Fiber Tracking Post-processing

The main users of the program are medical imaging professionals who need to visualize and analyze images acquired primarily with MRI or CT systems. Lossy compressed mammographic images and digitized film screen images must not be reviewed for primary image interpretations.

The provided document describes the Olea Sphere PACS software, which is intended for viewing, processing, and analyzing medical images, including functional and dynamic imaging datasets from MRI and CT.

Please note: This document primarily focuses on establishing substantial equivalence to predicate devices and outlines the device's features and intended use. It does not contain a detailed study proving the device meets specific performance acceptance criteria through quantitative metrics. Instead, it relies on the established performance of its predicate devices and a general statement of internal validation.

Here's an breakdown based on the information available:

1. Acceptance Criteria and Reported Device Performance

The document does not explicitly state quantitative acceptance criteria or detailed performance metrics like sensitivity, specificity, or AUC for specific tasks. Instead, it asserts that the Olea Sphere performs "substantially equivalent" to its predicate devices in terms of intended use, environment of use, limitations of use, principles of operation, and performance characteristics.

The "performance characteristics" section in the comparison table (pages 11-12) lists the main software features expected and implies that the new device performs these features similarly to the predicates.

2. Sample Size Used for the Test Set and Data Provenance

The document does not provide details on the sample size of a test set (e.g., number of images or cases) used for performance validation. It states that "OLEA Medical has conducted extensive validation testing of the Olea Sphere system," but does not specify the dataset used for these tests. Therefore, data provenance (country of origin, retrospective/prospective) is also not indicated.

3. Number of Experts and Qualifications for Ground Truth

The document does not mention the use of experts to establish a ground truth for a test set. This type of information is typically provided for studies assessing diagnostic accuracy, which is not the focus of this submission, as it mainly aims to demonstrate functional equivalence for a PACS viewing and analysis software.

4. Adjudication Method

No adjudication method is described, as there is no mention of a study involving expert readers and ground truth establishment.

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study

No MRMC comparative effectiveness study is mentioned. The document aims to demonstrate substantial equivalence to existing PACS systems, not to quantify improvement with AI assistance. The device description suggests a tool for analysis, but there is no mention of it being an "AI" device in the context of improving human reader performance.

6. Standalone (Algorithm Only) Performance Study

No standalone performance study is mentioned with quantitative metrics. The validation described is likely functional (e.g., software testing, verification) rather than a clinical performance study measuring accuracy against a reference standard.

7. Type of Ground Truth Used

No specific type of ground truth (e.g., expert consensus, pathology, outcomes data) is mentioned as being used for performance validation in the context of diagnostic accuracy. Given the nature of the device (PACS for viewing and analysis), the "validation" described likely refers to software functionality and accuracy of calculations, rather than diagnostic accuracy against a clinical ground truth.

8. Sample Size for the Training Set

The document does not refer to a training set or machine learning algorithms in a way that would require specifying a sample size for training. The Olea Sphere is described as a "software package" for image viewing, processing, and analysis, and its features are compared to predicate devices with similar functionalities, implying a traditional software development and validation approach rather than an AI/ML model.

9. How Ground Truth for the Training Set Was Established

As no training set is mentioned in the context of machine learning, there is no information on how its ground truth would have been established.

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