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Imagine® Enterprise Suite (IES) is a medical diagnostic device that receives, stores, and shares the medical images from and to DICOM-compliant entities such as imaging modalities (such as X-ray Angiograms (XA), Echocardiograms (US), MRI, CT, CR, DR, IVUS, OCT, PET and SPECT), external PACS, and other diagnostic workstations. It is used in the display and quantification of medical images, after image acquisition from modalities, for post-procedure clinical decision support. It constitutes a PACS for the communication and storage of medical images and provides a worklist of stored medical images that can be used to open patient studies in one of its image viewers. It is intended to display images and related information that are interpreted by trained professionals to render findings and/or diagnosis, but it does not directly generate any diagnosis or potential findings. Not intended for primary diagnosis of mammographic images. Not intended for intra-procedural or real-time use. Not intended for diagnostic use on mobile devices.

The Imagine® Enterprise Suite (IES) has, as its backbone, the IES PACS – a DICOM stack for the communication and storage of medical images. It is based on its predecessor, the HCP DICOM Net® PACS (K023467). The IES is made up of the following modules:

IES_EntViewer: This viewer module can be launched from the IES PACS Worklist and is intended primarily for the review and manipulation of angiographic X-ray images. It also supports the review of images from other modalities in single or combination views, thereby serving as a general-purpose multi-modality viewer.

IES_EchoViewer: This viewer module can be launched from the IES Worklist and is intended for specialized viewing, manipulation, and measurements of Echocardiography images.

IES_RadViewer: This viewer module can be launched from the IES Worklist and is intended for specialized viewing, manipulation, and measurements of Radiological images. It also supports the fusion of Radiological images (such as MRI and CT) with Nuclear Medicine images (such as PET and SPECT).

IES_ZFPViewer: This viewer is intended for non-diagnostic review of medical images over a web browser. It supports an independent worklist and a viewing component that requires no installation for the end user. It works within an intranet or over the internet via user-provided VPN or static IP.

AngioQuant: This module can be launched from the IES_EntViewer to perform automatic quantification of coronary arteries. It uses, as input, the cardiac angiogram studies stored on the IES PACS. It is intended for display and quantification of Xray angiographic images after image acquisition in the cathlab, for post-procedure clinical decision support within the cathlab workflow. It is not intended for intra-procedural or real-time use. The Imagine® Enterprise Suite (IES) is integrated with ML only for the segmentation of coronary vessels from X-ray angiographic images and uses deep learning methodology for image analysis.

Here's a breakdown of the acceptance criteria and study details for the Imagine® Enterprise Suite, specifically focusing on the AngioQuant module's machine learning component, as described in the provided 510(k) summary:

1. Table of Acceptance Criteria and Reported Device Performance

The 510(k) summary provides a narrative description of the performance evaluation rather than a direct table of acceptance criteria with corresponding performance metrics for every criterion. However, it explicitly states that the performance of the IES_AngioQuant module's machine learning-based coronary vessel segmentation function was evaluated using several metrics and compared against an FDA-cleared predicate device.

Note: The specific numerical values for Jaccard Index, Dice Score, Precision, Accuracy, and Recall are not provided in the summary. The summary highlights that these metrics were used for evaluation.

2. Sample Size and Data Provenance

• Test Set Sample Size: An independent external test set comprising 30 patient studies was used.
• Data Provenance: The dataset consisted of anonymized angiographic studies sourced from multiple U.S. and international clinical sites. It was a retrospective dataset. The dataset included adult patients of mixed gender and represented a range of age, body habitus, and diverse race and ethnicity. Clinically relevant variability, including lesion severity, vessel anatomy, image quality, and imaging equipment vendors, was represented.

3. Number of Experts and Qualifications for Ground Truth

• Number of Experts: Two independent board-certified interventional cardiologists.
• Qualifications of Experts: Each expert had more than 10 years of clinical experience.

4. Adjudication Method for the Test Set

The summary does not explicitly state a formal adjudication method like "2+1" or "3+1" for differences between the experts. However, it states that the ground truth (reference standard) was established using the FDA-cleared Medis QAngio XA (K182611) software, with verification performed by the two independent board-certified interventional cardiologists. This implies that the experts reviewed and confirmed the ground truth generated by the predicate software, rather than independently generating it and then adjudicating differences.

5. MRMC Comparative Effectiveness Study

An MRMC comparative effectiveness study was not explicitly described in the summary. The performance comparison was primarily an algorithm-only comparison against a predicate device (CAAS Workstation) for the ML component. The summary does not mention how much human readers improve with or without AI assistance.

6. Standalone (Algorithm Only) Performance

Yes, a standalone (algorithm only without human-in-the-loop performance) study was done for the IES_AngioQuant module's machine learning-based coronary vessel segmentation function. Its performance was evaluated using quantitative metrics and visual assessment, and then compared against the FDA-cleared predicate device (CAAS Workstation).

7. Type of Ground Truth Used

The ground truth was established using an FDA-cleared software (Medis QAngio XA, K182611), with its output verified by expert consensus of two independent board-certified interventional cardiologists.

8. Sample Size for the Training Set

A total of 762 anonymized angiographic studies were used for training, validation, and internal testing sets combined. The summary does not provide an exact breakdown of how many studies were specifically in the training set versus the validation and internal testing sets.

9. How the Ground Truth for the Training Set Was Established

The summary states that the ground truth ("truthing") for the dataset (which includes the training, validation, and internal testing sets) was established using the FDA-cleared Medis QAngio XA (K182611) software, with verification performed by two independent board-certified interventional cardiologists, each with more than 10 years of clinical experience. Implicitly, this same method was used for establishing ground truth for the training set.

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