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Prowess Pro-Sim is a modularly designed Radiation Treatment Planning Computer Program used to prepare individual treatment plans for cancer patients undergoing therapeutic radiation treatment. The system is utilized to develop treatment plans for Brachytherapy and External Beam (photon or electron) therapy. Completed treatment plans can be simulated using Prowess Pro-Sim in 3D on the computer's display prior to actual treatment.

The Prowess treatment-planning product is a series of independent software programs that are used to plan a course of radiation therapy. The treatment plan is an estimated dose distribution for a patient. The dose is computed by applying known and tested algorithms. Measured treatment machine data combined with geometric and tissue information, for a particular patient, are processed by the algorithm into a dose distribution.

The TPS software is designed to run on a personal computer (PC). Depending on the configuration of the program, the PC requires one of the following operating system: Windows NT, DOS or DOS under Windows-95. Other system components and peripherals include: 160 Mb of RAM, high resolution graphics display video card, network card, printer, plotter, digitizer, 3.5 inch floppy disk and tape backup drives.

The provided text describes the "Prowess Pro-Sim" radiation therapy treatment planning system, but it does not contain acceptance criteria or details of a study that proves the device meets specific performance criteria.

The document is a 510(k) summary, which focuses on demonstrating substantial equivalence to predicate devices rather than proving specific performance against predefined acceptance criteria through a detailed study with quantitative results.

Here's what can be extracted based on your request, highlighting the missing information:

1. Table of Acceptance Criteria and Reported Device Performance

Missing Information: The document states that functional requirements were met and specifications were adhered to, but it does not specify what those requirements or specifications are (e.g., accuracy of dose calculation within X%, speed of plan generation within Y seconds, etc.). Therefore, a direct comparison table cannot be created.

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

• Sample Size for Test Set: Not specified.
• Data Provenance: Not specified. The document mentions acquiring CT and MR image data from DICOM compliant sources, but does not specify the origin of any data used for testing.

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

• Number of Experts: Not specified.
• Qualifications of Experts: Not specified.

4. Adjudication Method for the Test Set

• Adjudication Method: Not specified.

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

• MRMC Study Done?: No. The document does not describe any MRMC comparative effectiveness study, nor does it mention effect sizes of human readers with or without AI assistance. The device is a planning system, not an AI diagnostic tool for human readers.

6. Standalone (Algorithm Only Without Human-in-the-Loop Performance) Study

• Standalone Study Done?: Yes, conceptually, the entire testing seems to be focused on the algorithm's performance in generating treatment plans. The conclusion states "testing demonstrates that the functional requirements were met, that the system meets its published specifications," which implies evaluation of the algorithm's output. However, specific details of this "standalone" testing are not provided in terms of metrics or methodologies.

7. Type of Ground Truth Used

• Type of Ground Truth: Not explicitly stated and likely not applicable in the typical sense for a diagnostic device. For a radiation therapy planning system, "ground truth" would relate to the accuracy of dose calculations against known physics principles and measured machine data, or the accuracy of simulations against physical phantoms. The document states: "The dose is computed by applying known and tested algorithms. Measured treatment machine data combined with geometric and tissue information, for a particular patient, are processed by the algorithm into a dose distribution." This implies that the 'ground truth' for dose calculation accuracy relies on established physics models and machine-specific measurements.

8. Sample Size for the Training Set

• Sample Size for Training Set: Not applicable / Not specified. The Prowess Pro-Sim is described as a software system that applies "known and tested algorithms" and process "measured treatment machine data." This suggests a deterministic system based on physics models rather than a machine learning model that requires a "training set" in the modern AI sense.

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

• How Ground Truth for Training Set Was Established: Not applicable / Not specified. As noted above, the system doesn't appear to be based on a machine learning paradigm requiring a training set. Its "ground truth" for its algorithms would be derived from standard physics principles and empirical measurements of radiation machines.

Summary of what is present:

• Testing Philosophy: The product was developed and tested according to a documented and signed plan, including validation and verification protocols (Section 7).
• Conclusion: Testing demonstrated that functional requirements were met, the system meets its published specifications, and performs as well as or better than predicate devices (Section 8).
• Substantial Equivalence: The primary focus of the 510(k) is to establish substantial equivalence to existing legally marketed devices (Section 3).

In conclusion, while the document confirms that testing was performed and deemed successful, it lacks the specific quantitative details regarding acceptance criteria, study design, sample sizes, expert involvement, and ground truth establishment that you requested for a comprehensive understanding of the device's proven performance. This is common for 510(k) submissions, which often focus on equivalence rather than detailed performance studies like those required for PMA devices or modern AI/ML submissions.

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