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The Anythink is a medical image processing system, which offers comprehensive solutions to viewing, manipulation, communication and storage of multi-modality DICOM images and data on exchange media.

The Anythink® is a universal imaging platform, and supports different modalities, but it is not intended for the displaying of digital mammography images for diagnosis in the U.S.

The Anythink® is based on Windows XP, providing a set of software solutions with flexible configurations in accordance with different medical imaging missions and demands. The system accepts multi-modality DICOM images and allows for view, post-processing, and communication. This product is not intended for use with or for the primary diagnostic interpretation of mammography images in the U.S.

Due to specific customer requirements and the clinical focus, the Anythink® consists of two parts: Basic Functions and Optional Functions.

Basic functions are mandatory, allowing view, easy manipulation, storage and communication of DICOM formatted images, except in the case of mammography images. Management of patient information is also included.

Optional Functions are a set of professional image processing functions, designed for specific modalities. Depending on the precise requirement, customers can select the appropriate function(s) from the optional functions, under the precondition of installing Basic Functions.

The clinician retains the ultimate responsibility for making the pertinent diagnosis based on their standard practices. The Anythink® is intended to assist the physician in diagnosis or treatment planning.

The provided FDA 510(k) summary for the Anythink® / Anythink PACS Workstation (K131299) establishes substantial equivalence to a predicate device (Siemens Syngo Multimodality Workplace, K072728). However, this summary does not include a study that explicitly proves the device meets specific acceptance criteria based on quantitative performance metrics, such as sensitivity, specificity, or reader performance.

Instead, the submission relies on a comparison of technological characteristics and intended use to demonstrate substantial equivalence. For medical image processing systems like PACS workstations, the FDA often accepts this approach without requiring a full-scale clinical performance study if the new device shares fundamental technological characteristics and intended use with a legally marketed predicate.

Therefore, many of the requested details about acceptance criteria, study design, sample size, ground truth, and reader studies are not present in this 510(k) summary. This is typical for Class II medical devices considered substantially equivalent, where the focus is on demonstrating that the new device does not raise new questions of safety and effectiveness compared to an existing one.

However, I can extract information related to the device's technical specifications and general safety and effectiveness claims.

Here's a breakdown of what can be inferred or explicitly stated based on the provided text, and what information is missing:

1. Table of Acceptance Criteria and Reported Device Performance

This document does not present a table of quantitative acceptance criteria (e.g., minimum accuracy, sensitivity, or specificity) or numerical performance metrics for the Anythink® PACS Workstation. The "acceptance criteria" for a 510(k) submission are fundamentally about demonstrating substantial equivalence, meaning the device is as safe and effective as a predicate device. The performance is largely implied to be equivalent to the predicate through the comparative analysis of features.

The document mainly focuses on comparing the functions of the Anythink® with the Siemens Syngo-Multimodality Workplace:

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

• Sample Size: Not specified. The submission does not detail a specific test set of medical cases used for performance evaluation in the traditional sense (e.g., a cohort of patients/studies).
• Data Provenance: Not specified. Since no specific test set study is presented, the country of origin or whether the data was retrospective or prospective is not mentioned. The device's intended market is the U.S.

3. Number of Experts Used to Establish Ground Truth and Qualifications

• Number of Experts: Not specified. As there's no described performance study with ground truth establishment, this information is not provided.
• Qualifications of Experts: Not specified.

4. Adjudication Method for the Test Set

• Adjudication Method: Not applicable. No study with an adjudication process is described in this summary.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done

• MRMC Study: No. This type of study (comparing human readers with and without AI assistance) is not mentioned. The device is a PACS workstation, not an AI-assisted diagnostic tool in the sense of providing specific lesion detection or characterization beyond standard viewing and manipulation functions. The comparative effectiveness is implicitly demonstrated by comparing the functionalities and technical characteristics to the predicate, not by a human-in-the-loop study.
• Effect Size: Not applicable as no MRMC study was performed.

6. If a Standalone (Algorithm Only) Performance Study Was Done

• Standalone Study: No. The device is a PACS workstation, which is a tool for viewing and manipulating images, not a standalone diagnostic algorithm that would generate outputs independent of a human operator. Its performance is based on its functionality and ability to process and display images robustly, not on an "algorithm only" diagnostic accuracy.

7. The Type of Ground Truth Used

• Type of Ground Truth: Not applicable. Since no performance study measuring diagnostic accuracy against a ground truth is described, this information is not provided. The "ground truth" for a PACS workstation primarily relates to its ability to accurately and reliably display and process DICOM images according to established standards.

8. The Sample Size for the Training Set

• Sample Size: Not applicable/Not specified. The device is a software solution with "Basic Functions" and "Optional Functions" designed for image processing. It's not described as a deep learning or machine learning system that would require a large training set in the typical sense for algorithms that learn patterns for diagnostic tasks. Its development likely involved standard software engineering validation and verification processes rather than data-driven model training.

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

• Ground Truth Establishment: Not applicable/Not specified. As there's no mention of a training set for a machine learning model, the method for establishing its "ground truth" is not relevant or provided in this document.

In conclusion:

The provided 510(k) summary for the Anythink® PACS Workstation demonstrates substantial equivalence primarily through a comparison of functional and technical characteristics with a legally marketed predicate device, the Siemens Syngo Multimodality Workplace. It does not present quantitative performance data from a clinical study, involve specific test sets of patient cases, or refer to machine learning model training/evaluation, which are common for AI/ML-based diagnostic devices today. The "acceptance criteria" here are met by demonstrating that the new device performs its intended functions (viewing, manipulation, communication, storage of DICOM images) reliably and does not raise new issues of safety or effectiveness compared to the predicate.

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