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The application can be used in clinical workflows that benefit from the co-registration of vascular image data as a planning or preplanning step.

Image Fusion Angio is intended to co-register digital subtraction angiographies with volumetric medical image data.

The provided text describes the acceptance criteria and a study proving the device meets these criteria for the Brainlab Elements Image Fusion Angio device.

Here's an analysis based on the provided document:

1. Table of Acceptance Criteria and Reported Device Performance

The acceptance criteria are not explicitly stated as quantitative thresholds in a table format within this document. Instead, the document focuses on demonstrating substantial equivalence to predicate and reference devices, particularly in terms of fusion accuracy.
The performance is reported through nonclinical performance testing (accuracy tests).

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

• Phantom Bench Test & MRA Bench Test: "The test was repeated 3 times" for each. The data provenance is controlled lab/bench test scenario.
• Retrospective Study: "We used 35 datasets from 16 different clinical sites (11 different scanner types)." The data provenance is retrospective clinical data from multiple sites. The country of origin is not explicitly stated but implies a multi-center approach.

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

• Retrospective Study: "The gold standard fusions were defined by medical experts." The number of experts is not specified (e.g., "by medical experts" could mean one, two, or more).
• Qualifications of Experts: The document states "medical experts" but does not provide specific qualifications (e.g., "radiologist with 10 years of experience" or "neurosurgeon").

4. Adjudication Method for the Test Set

• Retrospective Study: "The gold standard fusions were defined by medical experts." and "All fusions were further reviewed by medical experts." This suggests expert consensus or review, but the specific adjudication method (e.g., 2+1, 3+1) is not detailed.

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

• No, an MRMC comparative effectiveness study involving human readers improving with AI vs. without AI assistance was not done or reported in this document. The study focuses purely on the accuracy of the algorithm's 2D/3D co-registration.

6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) Was Done

• Yes, the accuracy tests (Phantom Bench Test, MRA Bench Test, Retrospective Study) evaluate the Brainlab Elements Image Fusion Angio and iPlan RT Image algorithms in a standalone manner, measuring their targeting accuracy against a defined gold standard. While medical experts defined and reviewed the ground truth/gold standard, the performance being measured is that of the algorithm itself, not an interaction with a human.

7. The Type of Ground Truth Used

• Phantom Bench Test & MRA Bench Test: "gold standard fusion" (likely established by precise manual registration or by the design of the phantom itself). This represents a highly controlled, measurable ground truth.
• Retrospective Study: "The gold standard fusions were defined by medical experts." This indicates expert consensus as the ground truth for clinical data.

8. The Sample Size for the Training Set

• The document does not provide information regarding the sample size used for the training set for the Image Fusion Angio algorithm. This section only covers performance testing (validation).

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

• Since the training set information is not provided, how its ground truth was established is also not detailed in this document.

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iPlan RT is a radiation treatment planning system that is intended for use in stereotactic, conformal, computer planned, Linac based radiation treatment of cranial, head and neck, and extracranial lesions.

iPlan RT is a software program to generate treatment plans and to simulate the dose delivery for external beam radiotherapy. The system is the evolutionary successor of the predicate devices iPlan RT Image (K080886) and iPlan RT Dose (K080888). It is specialized for stereotactic procedures for cranial as well as extracranial lesions. It includes functions for all relevant steps from outer contour detection to quality assurance. It combines most of its predecessor's functionality iPlan RT Image and iPlan RT Dose together with additional improvements. Therefore, the new version shall be called "iPlan RT".

The device incorporates conformal beams, conformal IMRT beams, circular arcs, and both static and dynamic arc treatments. Moreover, a combination of optimized dynamic arc treatments together with IMRT beams was added to the treatment modalities.

The system calculates dose using a convolution algorithm as the previous version. Alternatively, a Monte Carlo method based calculation algorithm can be used as in iPlan RT Dose (K080888). The documentation & export function facilitates printouts of all parameters and results for the creation of DICOM RT (RT Plan and RT Image) files.

Adapting existing treatment plans during fractionated radiotherapy treatments is facilitated using an elastic deformation algorithm. Existing structures are morphed from an existing treatment plan onto a new follow-up scan. If necessary, these structures can be adapted by the physician and can be used to update the current treatment plan accordingly.

The provided document, a 510(k) summary for Brainlab AG's iPlan RT, does not contain a study that proves the device meets specific acceptance criteria in the manner typically seen for novel medical device algorithms or AI. Instead, it demonstrates substantial equivalence to predicate devices (iPlan RT Image K080886 and iPlan RT Dose K080888) through non-clinical testing.

Here's an analysis based on the provided text, addressing the requested points:

1. Table of Acceptance Criteria and Reported Device Performance

The document does not explicitly present a table of acceptance criteria with corresponding performance metrics. The summary of non-clinical testing only states that the device "has met its specifications" and is "substantially equivalent to the predicate devices" and "safe and effective for its intended use." These are general conclusions rather than detailed performance metrics.

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

The document does not mention the use of a "test set" in the context of clinical data for performance evaluation. The evaluation was based on non-clinical testing and comparison to predicate devices. Therefore, there is no information on sample size or data provenance.

3. Number of Experts Used to Establish Ground Truth and Their Qualifications

Since no clinical test set was used to establish performance against a ground truth, this information is not applicable and not provided in the document.

4. Adjudication Method for the Test Set

As no clinical test set requiring ground truth establishment was used, there is no mention of an adjudication method.

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

The document explicitly states: "Clinical testing is not required to demonstrate substantial equivalence or safety and effectiveness." Therefore, an MRMC comparative effectiveness study was not performed.

6. Standalone (Algorithm Only) Performance Study

The document describes non-clinical "Verification and Validation tests" which confirmed the device "met its specifications." This implicitly refers to the algorithm's performance in generating treatment plans. However, no specific metrics like sensitivity, specificity, accuracy, or a detailed study design for standalone performance are provided beyond the general statement of meeting specifications. The focus is on functionality and equivalence to predicate devices.

7. Type of Ground Truth Used

For the non-clinical testing, the "ground truth" would likely have been the expected computational output based on known physics and engineering principles for radiation dose calculation and planning. This is inferred from the statement that the device "has met its specifications." There is no mention of external clinical ground truth (e.g., pathology, outcomes data, or expert consensus on clinical cases) for this 510(k) submission.

8. Sample Size for the Training Set

The document does not mention a "training set" in the context of machine learning or AI models with data-driven training. The iPlan RT is described as an "evolutionary successor" of previous devices, suggesting a development and refinement process rather than a machine learning training paradigm. The core dose calculation uses a convolution algorithm or Monte Carlo method, which are physics-based models rather than models trained on large datasets.

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

As no explicit training set for a machine learning model is mentioned, this question is not applicable based on the provided text.

In summary, the 510(k) for iPlan RT focuses on demonstrating substantial equivalence to existing predicate devices through non-clinical verification and validation, rather than extensive clinical studies with specific acceptance criteria tables and ground truth evaluations typically associated with novel AI/ML devices.

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