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PICTools Medical Compression Toolkit is intended to be used by software developers in the development of applications for the compression of the data in digital medical images.

PICTools Medical is a software development kit written in 'C' for software developers to write applications interfaces to integrate particular image data compression algorithms into their medical imaging applications. It provides an array of high performance image compression libraries intended for use by developers of medical devices in the fields of radiology, cardiology, digital dentistry, ultrasound, and telepathology.

This is a 510(k) summary for a software development kit (SDK) rather than a medical device that directly interacts with patients or provides diagnostic information. Therefore, the typical acceptance criteria and study design for medical imaging AI/ML devices, which focus on diagnostic accuracy metrics, are not applicable here.

The document describes PICTools Medical Compression Toolkit, a software development kit designed for developers to integrate image compression algorithms (Wavelet and JPEG) into medical imaging applications. The submission is focused on demonstrating substantial equivalence to predicate devices for its functionality as a compression toolkit, not its diagnostic efficacy.

Here's an analysis of the provided information in the context of a software toolkit:

1. Table of Acceptance Criteria and Reported Device Performance

Given that this is a software toolkit for image compression, the "acceptance criteria" are not related to diagnostic performance (e.g., sensitivity, specificity, AUC). Instead, they are about the existence and functionality of the compression algorithms compared to predicate devices.

Note: The document explicitly states "The clinical functionalities of the three devices are compared below" and then provides a table simply indicating which devices have which compression methods. This implies the "performance" for equivalence is the presence of these functionalities. There are no quantitative performance metrics (e.g., compression ratios, image quality impact) discussed in this summary.

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

Not applicable. This is a software toolkit for compression algorithms. The submission does not describe a clinical "test set" of images for evaluating diagnostic performance or image quality impacts. The evaluation is at the functional level of providing compression algorithms.

3. Number of Experts Used to Establish Ground Truth for the Test Set and Qualifications

Not applicable. As there's no clinical test set for diagnostic evaluation or image quality assessment by experts, no ground truth experts were used for this type of submission.

4. Adjudication Method for the Test Set

Not applicable. There was no clinical test set requiring adjudication.

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

Not applicable. This software toolkit does not directly involve human readers in making diagnostic interpretations, nor does it aim to improve their performance in a diagnostic context in the way AI/ML diagnostic tools do. Therefore, an MRMC study is not relevant to this submission.

6. Standalone Performance Study

Not applicable in the typical sense of a diagnostic AI algorithm. The "standalone" performance here refers to the SDK providing compression algorithms, which is its intended function. There's no separate "standalone" performance study for diagnostic accuracy outlined. The study outlined is a comparison of functionality to predicate devices.

7. Type of Ground Truth Used

Not applicable. As this device provides compression algorithms and is not for diagnostic interpretation, there is no "ground truth" in the clinical sense (e.g., pathology, outcomes data). The "ground truth" for the submission is the reported functionality of the predicate devices.

8. Sample Size for the Training Set

Not applicable. This is not an AI/ML algorithm that learns from a training set of data. It's a set of pre-defined compression algorithms.

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

Not applicable. No training set was used.

Summary of the Study for Substantial Equivalence:

The "study" presented is a comparison of functional equivalence between the PICTools Medical Compression Toolkit and two predicate devices:

• Access Radiology (Framewave) K972925 for Wavelet image compression.
• WinRad Teleradiology System (K936179) for JPEG image compression.

The evidence for this substantial equivalence is primarily a direct comparison of the documented "clinical functionalities" (i.e., the presence of specific compression algorithms) as shown in the table provided in the 510(k) summary. The submission asserts that because the PICTools Medical Compression Toolkit offers both Wavelet and JPEG compression, it is functionally equivalent to or encompasses the compression capabilities of the two predicate devices.

This type of submission relies on demonstrating that the device performs the same intended use as legally marketed predicate devices and does not raise new questions of safety or effectiveness. The core of the "proof" is the declaration of included compression types matching those of already cleared devices. No clinical trials, performance studies on image quality, or diagnostic accuracy evaluations are mentioned or required for this specific type of software toolkit.

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