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The TeraRecon Neuro Algorithm is an algorithm for use by trained professionals, including but not limited to physicians, surgeons and medical clinicians.

The TeraRecon Neuro Algorithm is a standalone image processing software device that can be deployed as a Microsoft Windows executable on off-the-shelf hardware or as a containerized application (e.g., a Docker container) that runs on off-the-shelf hardware or on a cloud platform. Data and images are acquired via DICOM compliant imaging devices. DICOM results may be exported, combined with, or utilized by other DICOM-compliant systems and results.

The TeraRecon Neuro Algorithm provides analysis capabilities for functional, dynamic, and derived imaging datasets acquired with CT or MRI. It can be used for the analysis of dynamic brain perfusion image data, showing properties of changes in contrast over time. This functionality includes calculation of parameters related to brain tissue perfusion, vascular assessment, tissue blood volume, and other parametric maps with or without the ventricles included in the calculation. The algorithm also include volume reformat in various orientation, rotational MIP 3D batch while removing the skull. This "tumble view" allows qualitative review of vascular structure in direct correlation to the perfusion maps for comprehensive review.

The results of the TeraRecon Neuro Algorithm can be delivered to the end-user through image viewers such as TeraRecon's Aquarius Intuition system, TeraRecon's Eureka AI Results Explorer, TeraRecon's Eureka Clinical AI Platform, or other image viewing systems like PACS that can support DICOM results generated by the TeraRecon Neuro Algorithm.

The TeraRecon Neuro Algorithm results are designed for use by trained healthcare professionals and are intended to assist the physician in diagnosis, who is responsible for making all final patient management decisions.

The TeraRecon Neuro algorithm version 2.0.0 is a modification of the predicate device Neuro.AI Algorithm (K200750), which was a modification of the predicate device, Intuition-TDA, TVA, Parametric Mapping (which was cleared under K131447). The predicate device Intuition -TDA, TVA, Parametric Mapping is an optional module/workflow for the Intuition system (K121916). The TeraRecon Neuro algorithm is an image processing software device that can be deployed as a Microsoft Windows executable on off-the-shelf hardware or as a containerized application (e.g., Docker container) that runs on off-the-shelf hardware or on a cloud platform. The device has limited network connectivity or external medical support.

TeraRecon Neuro allows motion correction and processes, calculates and outputs brain perfusion analysis results for functional, dynamic, and derived imaging datasets acquired with CT or MRI. TeraRecon Neuro results are used for the analysis of dynamic brain perfusion image data, showing properties of changes in contrast over time. This functionality includes calculation of parameters related to brain tissue perfusion, vascular assessment and tissue blood volume.

Outputs include parametric map of measurements including time to peak (TTP), take off time (TOT), recirculation time (RT), mean transit time (MTT), blood volume (BV/CBV), blood flow (BF/CBF), time to maximum (Tmax) and penumbra/umbra maps that are derived from combinations of measurement parameters, such as mismatch maps and hypoperfusion maps with volumes and ratios, as well as 2D and 3D visualization of brain tissues and brain blood vessels (Note: Tmax, mismatch and hypoperfusion maps are only available for images of CT modality).

When TeraRecon Neuro results are used in external viewer devices such as TeraRecon's Intuition or Eureka medical devices, all the standard features offered by Intuition or Eureka are employed such as image manipulation tools like drawing the region of interest, manual or automatic segmentation of structures, tools that support creation of a report, transmitting and storing this report in digital form, and tracking historical information about the studies analyzed by the software.

The TeraRecon Neuro algorithm outputs can be used by physicians to aid in the diagnosis and for clinical decision support including treatment planning and post treatment evaluation. The software is not intended to replace the skill and judgment of a qualified medical practitioner and should only be used by individuals that have been trained in the software's function, capabilities and limitations. The device is intended to provide supporting analytical tools to a physician, to speed decision-making and to improve communication, but the physician's judgment is paramount, and it is normal practice for physicians to validate theories and treatment decisions multiple ways before proceeding with a risky course of patient management.

Here's a breakdown of the acceptance criteria and the study that proves the device meets them, based on the provided text:

Acceptance Criteria and Device Performance

Study Details

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

The document does not explicitly state the numerical sample size for the test set used in the qualitative clinical user evaluation or the quantitative Tmax measurement accuracy study. It refers to "comparison maps generated by the subject device, the predicate device and two additional reference devices." Without specific numbers, it's impossible to determine the precise size of the test set cases.

Regarding data provenance, the document does not provide details on the country of origin or whether the data was retrospective or prospective.

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

• Number of Experts: One expert was used.
• Qualifications: Dr. Robert Falk, MD. No additional details about his specific experience or sub-specialty (e.g., radiologist with X years of experience) are provided in the text.

4. Adjudication Method for the Test Set

The adjudication method used for the clinical user evaluation was not explicitly specified as 2+1, 3+1, or any other formal method. The study involved a single evaluator (Dr. Robert Falk, MD) who was "asked to confirm through qualitative assessment." This suggests a single-expert review, rather than a multi-expert adjudication process.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done, and the Effect Size of Improvement with AI vs. Without AI Assistance

No, a Multi-Reader Multi-Case (MRMC) comparative effectiveness study was not explicitly described. The evaluation involved a single expert providing a qualitative assessment. The study was focused on demonstrating substantial equivalence to predicate and reference devices, not on measuring the improvement of human readers with AI assistance. Therefore, there is no reported effect size of how much human readers improve with AI vs. without AI assistance.

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

Yes, a standalone performance evaluation was conducted for the quantitative Tmax measurement. The acceptance criteria for Tmax accuracy were based on comparing the subject device's measurements directly against the ground truth (average of reference devices) in ROIs, without explicit human intervention in the measurement process for the test cases. While the "ground truth" itself is derived from other devices (which are used by humans), the comparison of the algorithm's output to this ground truth represents a standalone assessment of the algorithm's quantitative accuracy.

7. The Type of Ground Truth Used

• Qualitative Clinical User Evaluation: The ground truth for this evaluation appears to be the performance of the predicate and reference devices, as the subject device's maps were compared to these for substantial equivalence. It's a comparative assessment rather than an absolute ground truth (e.g., pathology).
• Quantitative Tmax Measurement Accuracy: The ground truth for Tmax measurements was defined as the average Tmax measurement of the two reference devices (GE Medical Systems FastStroke CT Perfusion 4D (K193289) and ISchemaView RAPID (K182130)) for a given ROI.

8. The Sample Size for the Training Set

The document does not provide any information regarding the sample size used for the training set for the TeraRecon Neuro algorithm.

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

The document does not provide any information on how the ground truth for the training set was established. Training set details are not discussed.

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