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The AMICAS Diagnostic Workstation (ADW) is software intended for viewing and diagnostic interpretation of images acquired from CT, MR, CR, DR, US and other DICOM-compliant medical imaging systems when installed on suitable commercial-standard hardware. The ADW receives imaging studies over a network from AMICAS servers or directly from CD with images utilizing both lossless (reversible) and lossy (irreversible) compression. It is the user's responsibility to ensure that monitor quality, ambient light conditions and image compression ratios are consistent with the clinical application.

AMICAS Light Beam Workstation (ALBW) is software intended to create and display two-dimensional and three-dimensional images of anatomy from a series of digitally acquired images.

The provided text does not contain detailed information about acceptance criteria or a specific study proving the device meets them in the way typically required for AI/Ml medical devices. This document is a 510(k) summary for a Picture Archiving Communication System (PACS) software, Amicas Light Beam Workstation (ALBW), which handles and displays medical images, not an AI/ML diagnostic algorithm.

Here's a breakdown of what can be extracted and what is missing:

1. Table of Acceptance Criteria and Reported Device Performance

The document does not detail specific quantitative acceptance criteria or performance metrics (like sensitivity, specificity, AUC) that an AI/ML diagnostic device would typically report. Instead, it focuses on demonstrating substantial equivalence to a predicate device based on functional characteristics.

The "acceptance criteria" here are implied by the features of the predicate device (Voxar Plug'n View 3D, version 1.0). The ALBW device is deemed substantially equivalent because it performs the same core functions. The JPEG2000 compression features represent an enhancement over the predicate, not a criteria it had to meet.

2. Sample Size for Test Set and Data Provenance

The document states: "ALBW is tested with reference to its Software Requirements Specifications, as documented in the Verification Procedure included in this 510(k) filing." However, it does not provide any specific sample size for a test set or information regarding data provenance (e.g., country of origin, retrospective/prospective). This is a general statement about software testing, not a clinical validation study with a defined test set.

3. Number of Experts and Qualifications for Ground Truth

The document does not mention any experts used to establish a ground truth for a test set. This type of analysis is typically performed for diagnostic devices where human expert consensus is needed to determine the correct diagnosis or finding that the device is being evaluated against. Since ALBW is a PACS workstation, its primary function is display and manipulation, not diagnostic interpretation itself, though it is used for interpretation by qualified professionals.

4. Adjudication Method

As no experts were mentioned for establishing ground truth, there is no adjudication method described.

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

No MRMC study is mentioned. This device is a PACS workstation, not an AI assistant intended to improve human reader performance for a specific task. Its primary function is to facilitate viewing and manipulation of images.

6. Standalone Performance Study

No standalone performance study is mentioned in the context of an AI/ML algorithm. The "testing" section refers to software requirements specification verification.

7. Type of Ground Truth Used

Given the nature of the device as a PACS workstation, no specific "ground truth" (like pathology, expert consensus, or outcomes data) is described for performance evaluation. The device is assessed based on its ability to correctly display and process images as per its specifications, not on its diagnostic accuracy against a clinical ground truth.

8. Sample Size for Training Set

The document does not mention any training set size. This indicates that the device is not an AI/ML model that would require a distinct training phase with a dataset.

9. How Ground Truth for Training Set was Established

Since there is no mention of a training set, there is no information on how its ground truth would have been established.

In summary: The provided 510(k) summary is for a PACS workstation (Amicas Light Beam Workstation) from 2002. This predates the widespread regulatory focus on AI/ML in medical devices and the specific types of performance studies and reporting (like those involving sensitivity, specificity, ROC curves, ground truth establishment, training/test sets, and MRMC studies) that are now standard for such devices. The "study" here consists of software verification against functional specifications and demonstrating substantial equivalence to a predicate device based on shared features.

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