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Pinnacle3® Radiation Therapy Planning System is a software package intended to provide planning support for the treatment of disease processes. Pinnacle3® Radiation Therapy Planning System incorporates a number of fully integrated subsystems, including Pinnacle3 Proton, which supports proton therapy planning. The full Pinnacle3® Radiation Therapy Planning System software package provides planning support for the treatment of disease processes, utilizing photon, proton, electron and brachytherapy techniques.

Pinnacle3® Radiation Therapy Planning System assists the clinician in formulating a treatment plan that maximizes the dose to the treatment volume while minimizing the dose to the surrounding normal tissues. The system is capable of operating in both the forward planning and inverse planning modes. Plans generated using this system is used in the determination of the course of a patient's radiation treatment. They are to be evaluated, modified and implemented by qualified medical personnel.

The Pinnacle3® Radiation Therapy Planning System (hereafter Pinnacle3® RTP System) provides radiation treatment planning for the treatment of benign or malignant diseases. When using the Pinnacle3® RTP System, qualified medical personnel may generate, review, verify, approve, print and export the radiation therapy plan prior to patient treatment. The Pinnacle3® RTP System can provide plans for various radiation therapy modalities including utilizing photon, proton, electron and brachytherapy techniques.

The Pinnacle3® RTP System is a software package that runs on an Oracle Server and is accessed through one (or more) client(s) or an Oracle UNIX workstation. The software package consists of a core software module (Pinnacle3) and optional software features, which are available through a licensing scheme. The device has network capability to other Pinnacle® RTP System workstations, thin client, and to both input and output devices via local area network (LAN) or wide area network (WAN).

Image data is imported from CT, MR, PET, PET-CT and SPECT devices using a DICOM-compliant interface. A qualified medical professional uses the Pinnacle3® RTP System for functions such as viewing and analyzing the patient's anatomy, and generating a radiation therapy plan.

The provide text is a 510(k) premarket notification for the Philips Medical Systems (Cleveland), Inc. Pinnacle3® Radiation Therapy Planning System. This document focuses on demonstrating substantial equivalence to a predicate device and does not contain detailed information about the acceptance criteria and study proving device performance as typically understood for AI/ML devices.

Here's an analysis based on the information provided, highlighting what is present and what is not present:

Key Takeaways from the Document:

• Device Type: This is a Radiation Therapy Planning System, a software package. It's not explicitly an AI/ML device in the sense of making diagnostic predictions or interpretations, although some features (like Auto-segmentation, Deformable Image Registration) could potentially leverage AI/ML techniques. The document describes it as "software" and emphasizes "physics modeling," "dose computation," and "optimization processes."
• Focus on Substantial Equivalence: The primary goal of this 510(k) is to demonstrate that the updated Pinnacle3® RTP System (Version 16.0) is substantially equivalent to its predicate device (K130992). This pathway typically relies on comparing new features to existing, approved functionalities rather than extensive novel clinical effectiveness studies.
• Verification Testing, Not Clinical Trials: The document explicitly states: "Clinical trials were not performed as part of the development of this product. Clinical testing on patients is not advantageous in demonstrating substantial equivalence or safety and effectiveness of the device since testing can be performed such that no human subjects are exposed to risk. Verification testing was performed as required per the risk analyses and demonstrated that no new risks were introduced with the modifications in this submission." This strongly indicates that the "study" proving the device meets acceptance criteria involved non-clinical verification testing rather than a traditional performance study with a test set, ground truth, and human experts.
• Acceptance Criteria Mentioned but Details Lacking: The document states that "The results of these tests demonstrate that the Pinnacle3® RTP System met the acceptance criteria and is adequate for its intended use." However, what those specific acceptance criteria were (e.g., specific quantitative thresholds for dose calculation accuracy, registration accuracy, etc.) and the detailed results are not described in this summary.

Given the document's nature as a 510(k) summary focused on substantial equivalence and non-clinical verification, many of the requested items (especially those related to clinical performance studies, expert consensus, sample sizes for test/training sets, and AI-specific metrics) are not present or applicable in the provided text.

Here's how to fill out your requested table and information, with many entries indicating "Not Applicable" or "Not Provided" based on the document:

1. Table of Acceptance Criteria and Reported Device Performance

2. Sample size used for the test set and the data provenance

• Sample Size: Not provided. The verification testing likely used various defined test cases or phantoms rather than a "test set" of patient data in the typical sense of a clinical performance study for an AI device.
• Data Provenance: Not applicable/not provided for patient data. The development and verification likely used simulated or phantom data, or possibly a limited set of de-identified patient data for specific test cases.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts

• Number of Experts: Not provided.
• Qualifications of Experts: Not provided.
• Note: Since this was primarily verification testing of a planning system rather than a diagnostic AI device, the "ground truth" would likely be established by engineering specifications, known physical properties (e.g., for dose calculations in phantoms), or clinical standards, rather than direct expert annotation of medical images for a test set.

4. Adjudication method (e.g. 2+1, 3+1, none) for the test set

• Adjudication Method: Not applicable/none. The document describes "verification testing" against specified requirements and risk analyses, not a human reader study requiring adjudication.

5. If a multi reader multi case (MRMC) comparative effectiveness study was done, If so, what was the effect size of how much human readers improve with AI vs without AI assistance

• MRMC Study: No. The document explicitly states "Clinical trials were not performed." This type of study is typically done for AI-assisted diagnostic devices.
• Effect Size: Not applicable.

6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done

• Standalone Performance: While the system calculates and optimizes plans, the performance study described is non-clinical verification testing of the software's adherence to specifications and risk mitigation. The device is intended to "assist the clinician," and plans "are to be evaluated, modified and implemented by qualified medical personnel," indicating it's not a standalone diagnostic device but a tool for medical professionals. The verification testing itself would be "standalone" in the sense that the software's outputs were checked against expected results as part of the engineering validation.

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.)

• Type of Ground Truth: Not explicitly stated but inferred from "verification testing" and "risk analyses." Likely involved:
  • Engineering Specifications: Outputs (e.g., calculated dose, image registration accuracy) compared against predefined numerical tolerances.
  • Physics Models: Accuracy of dose calculations verified against established radiation physics models and experimental data from phantoms.
  • Clinical Standards: Verification that the system generates plans that align with accepted clinical practices and safety parameters for radiation therapy.

8. The sample size for the training set

• Sample Size for Training Set: Not applicable/not provided. This is a software planning system with physics models and optimization algorithms, not a machine learning model that relies on a "training set" in the conventional sense for image classification or prediction tasks. While some features like "Atlas Auto-Segmentation" or "Model-Based Segmentation" might involve pre-trained models, the document does not provide details on their training data.

9. How the ground truth for the training set was established

• How Ground Truth was Established: Not applicable/not provided for a training set in the AI/ML context. If certain sub-features (e.g., auto-segmentation) use models, the method for establishing their "ground truth" for training is not disclosed in this document.

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