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ProKnow DS is a patient data archive, information management, and analytics software system with a focus on the data and images specific to radiation oncology patients. Users may upload digital patient data created by other devices to ProKnow DS to securely archive, display, and analyze the data. Users can view and navigate patient images, drawn anatomy, calculated dose, and plan details derived from the source files. Users can create or edit anatomy structures to be used either prospectively (e.g., as an input to treatment planning) or retrospectively (e.g., for data analysis, research, and outcomes studies). Users can extract metrics for any single patient, or across a collection of patients, then view results as tables or graphically. ProKnow DS is to be used as an accessory system to perform data archive, review, and analysis, and is not to be used for diagnosis, treatment, or as the sole form of plan approval.

ProKnow DS is a Radiation Therapy Picture/Patient Archiving and Communication System (RT-PACS). It allows users to archive, inspect, analyze, and interact with radiation therapy patient data for both retrospective and prospective studies. Its features are centered on two primary areas of interaction: (1) single patient datasets and (2) collections of patient datasets, i.e., patient cohorts. Users are able to import patient data from existing imaging, contouring, and treatment planning systems (via DICOM formats). Once imported, patient data is archived for long-term storage and is also available for inspection and analysis (examples of supported inspection and analysis tasks include: visualizing images, structures, and dose distributions; inspecting plan information; inspecting dose volume histograms; and extracting metrics). ProKnow DS also allows users to edit anatomical contour data associated with a patient for use in retrospective studies or to be exported to commercially available radiation treatment planning systems. Once a set of patients have been established in ProKnow DS, users may create collections of related patients allowing them to analyze and correlate dosimetric values of interest and clinical endpoints across large treatment populations.

The provided text does not contain detailed acceptance criteria or a comprehensive study report with specific performance metrics for the ProKnow DS device. It primarily focuses on the regulatory submission process, the device's intended use, and a general summary of non-clinical testing.

Therefore, I cannot provide a table of acceptance criteria and reported device performance, nor can I detail specific sample sizes, expert qualifications, or study methodologies as requested. The document states that "The verification and validation test results showed that ProKnow DS met all clinical requirements in terms of usability and accuracy of any/all data stored and displayed" and "the device is at least as safe and effective as the legally marketed predicate devices," but it does not provide the quantitative data to support these claims.

Here's what can be extracted from the text, but a complete answer to your request is not possible with the given information:

Key Information from the Document (and limitations):

• Device Name: ProKnow DS
• Regulation Name: Picture Archiving And Communications System (PACS)
• Regulatory Class: Class II
• Product Code: LLZ
• Intended Use: Archive, inspect, analyze, and interact with radiation therapy patient data (DICOM RT focus). Not for diagnosis, treatment, or sole plan approval.
• Users: Trained medical professionals (radiologists, oncologists, physicians, medical technologists, dosimetrists) familiar with input data and interpreting metrics.

What is NOT Present in the Document to Answer Your Request:

• Specific Acceptance Criteria: The document mentions "functional specifications and performance requirements" and "clinical requirements in terms of usability and accuracy" but does not define these with quantitative or qualitative thresholds.
• Reported Device Performance: No specific metrics like sensitivity, specificity, accuracy, or any comparative performance data are provided.
• Sample Sizes: No details on the number of patient datasets used for verification or validation testing.
• Data Provenance: No information on the country of origin or whether the data was retrospective or prospective.
• Number of Experts/Qualifications/Adjudication for Ground Truth: Not mentioned, as no detailed ground truth establishment is described for a test set.
• MRMC Comparative Effectiveness Study: No mention of a study involving human readers or their improvement with AI assistance. This device is described as a PACS and analysis tool, not an AI-assisted diagnostic tool.
• Standalone Performance: While it performs "archive, review, and analysis," no standalone diagnostic performance metrics (e.g., for an algorithm identifying a specific disease) are discussed, as this is not its intended use.
• Type of Ground Truth: The document refers to "published and analytical gold standard datasets" for verification and "clinical expert" review for validation but doesn't specify if this involved pathology, outcomes, or expert consensus in a structured manner for a specific clinical task.
• Training Set Sample Size/Ground Truth Establishment: Not applicable as ProKnow DS is described as a PACS and data analysis software, not a machine learning model that requires a "training set" in the typical sense. It processes existing medical data rather than learning from it to make predictions.

General Statements on Testing:

• Verification Testing: Used "published and analytical gold standard datasets wherever possible."
• Validation Testing: Performed by "a clinical expert in accordance with the intended clinical use in a simulated clinical environment, as well as by hospital-based and 3rd party vendor validation partners." Utilized "a variety of data types and combinations that were judged to be representative of the types of data the software will encounter in clinical use."

In summary, the provided text is a regulatory clearance letter and a summary of the device for FDA submission. It confirms that the device meets some unspecified "clinical requirements" and is "at least as safe and effective" as predicate devices based on non-clinical testing, but it does not contain the detailed, quantitative study results that would allow me to fill in the requested table and describe the study in detail.

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