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The PacsSCAN Medical Imaging Software with QC Module is intended to be used by authorized staff to perform various quality control operations on PACSGEAR imaging studies before they are made available to other locations in the network. These operations include scanning film images, capturing video images, confirming or editing patient demographics, reviewing the history of the study, adding or removing images, combining with another study, renumbering images, editing patient orientation information, and setting or editing routing information.

The PacsSCAN Medical Imaging Software with OC Module is intended to be used by authorized staff to perform various quality control operations on PacsSCAN imaging studies before they are made available to other locations in the network. These operations include digitizing film images, capturing video images, confirming or editing patient demographics, reviewing the history of the study, adding or removing images, combining with another study, renumbering images, editing patient orientation information, and setting or editing routing information.

Here's an analysis of the acceptance criteria and study information for the PacsSCAN Medical Imaging Software with QC Module, based on the provided 510(k) summary:

Summary of Device Performance and Acceptance Criteria

A primary function of a 510(k) submission is to demonstrate substantial equivalence to a legally marketed predicate device. For the PacsSCAN Medical Imaging Software with QC Module, the acceptance criteria are implicitly defined by directly comparing its functionalities to those of the predicate device, RadWorks Medical Imaging Software with Quality Control Module (K982862). The study described for this submission is a software validation that verifies the device meets its intended use and established specifications, which are intrinsically linked to the predicate's capabilities.

1. Table of Acceptance Criteria and Reported Device Performance

Interpretation of Acceptance: The device is considered to meet the acceptance criteria if it functions similarly or equivalently to the predicate device for all listed capabilities. In some cases, like JPEG2000 compression and automatic LUT compensation, the PacsSCAN device demonstrates enhanced functionality compared to the predicate, which is acceptable in a 510(k) as long as it doesn't introduce new safety or effectiveness concerns.

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

The provided 510(k) summary does not explicitly state a specific sample size for a "test set" in terms of patient data or images. The study described is a "Software validation" which typically involves testing the software's functionalities against established requirements and specifications. This would usually be a functional and performance testing approach rather than a clinical study with a patient data test set.

• Sample Size (Test Set): Not specified in terms of patient data/images. The "test set" would be the software itself and its various functions.
• Data Provenance: Not applicable in the context of a clinical test set from patient data. The validation would involve internal testing scenarios and simulated data to verify software functionality.

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

This information is not provided in the 510(k) summary. Given the nature of the device (medical imaging software for QC operations), the "ground truth" for software validation would be the functional requirements and specifications derived from the predicate device and the intended use. These are typically established by software engineers, quality assurance personnel, and potentially input from clinical users (e.g., radiologists, PACS administrators) to define how the software should perform.

4. Adjudication Method for the Test Set

This information is not provided. Adjudication methods like 2+1 or 3+1 are typically used in clinical studies where multiple readers interpret images to establish a consensus ground truth. For software validation of a QC module, the 'adjudication' would be against the defined software requirements and expected outputs.

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

No, an MRMC comparative effectiveness study was not done or at least not reported in this 510(k) summary. This type of study is usually conducted for diagnostic AI/CAD devices to demonstrate improved reader performance with AI assistance. The PacsSCAN device is a QC module, whose primary function is to manage and process images, not to provide diagnostic interpretations.

• Effect Size of Human Readers Improvement with AI: Not applicable, as an MRMC study was not performed.

6. If a Standalone (Algorithm Only Without Human-in-the-Loop Performance) Study was Done

Yes, in essence. The "Software validation" described would inherently evaluate the algorithm's performance in a standalone manner. The device's functions (digitizing, capturing, editing, combining, etc.) are performed by the software itself, and the validation would confirm that these operations execute as intended and meet their specifications, independent of real-time human diagnostic input.

7. Type of Ground Truth Used

The ground truth used for the software validation would be:

• Functional Requirements and Specifications: Each feature (e.g., "Digitize film," "Edit patient demographics") has a defined expected behavior and output. The software validation tests against these pre-defined behaviors.
• Comparison to Predicate Behavior: The predicate device's known functionalities serve as the benchmark for many of the "acceptance criteria."

The ground truth is therefore based on engineering specifications and comparative functional parity with the predicate, rather than medical outcomes, pathology, or expert consensus on clinical findings.

8. Sample Size for the Training Set

This information is not applicable and not provided. The PacsSCAN Medical Imaging Software with QC Module is not described as an AI/ML-based device that requires a training set in the conventional sense (i.e., for learning from data to make predictions or classifications). It is a software application designed to perform specific quality control operations. Its development would involve traditional software engineering and testing, not training on a dataset.

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

This information is not applicable and not provided for the same reasons as #8. The device is not an AI/ML model that would have a "training set" with associated ground truth for learning.

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