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

The Neuro.Al 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 Neuro.AI algorithm provides analysis capabilities for static, functional, dynamic and derived imaging datasets acquired with CT or MRI. It can be used for the analysis of dynamic brain image data, showing properties of changes in contrast over time. This functionality includes calculation of parameters related to brain tissue perfusion, vasular assessment and tissue blood volume and other parametric maps with or without the ventricles included in the calculation. The algorithm also includes volume reformat in various orientions, 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 Neuro.AI Algorithm can be delivered to the end-user through image viewers such as TeraRecon's Aquarius iNtuition system, TeraRecon's Northstar AI Results Explorer, or other image viewing systems like PACS that can support DICOM results generated by Neuro.AI.

The Neuro.AI 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 Neuro.Al Algorithm is a modification of the predicate device, iNtuition-TDA, TVA, Parametric Mapping which was cleared under K131447. The predicate device is an optional module/workflow for the iNtuition system (K121916). The Neuro.Al 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., Docker container) that runs on off-the-shelf hardware or on a cloud platform. The device has limited network connectivity or external medical support.

The Neuro.Al Algorithm allows motion correction and processes, calculates and outputs brain perfusion analysis results for static, functional, dynamic and derived imaging datasets acquired with CT or MRI. Neuro.Al results are used for visualization and 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 displayed in rotational Maximum Intensity Projection (MIP) called the tumble view, and tissue blood volume and other parametric maps with or without brain ventricles included in the calculation.

Outputs include text and parametric map displays 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), classification maps, reformatted images and rotational MIPs for 2D and 3D visualization of brain tissues and blood vessels, and for correlation to the perfusion maps.

The results of the Neuro.Al Algorithm can be delivered to the end-user through image viewers such as TeraRecon's iNtuition system, TeraRecon's Northstar Al Results Explorer ("Northstar"), or other third-party image viewing systems like PACS that can display the DICOM results generated by Neuro.Al output does not depend on the viewing system's capabilities as the results are self-contained and the only interface is through DICOM.

When the Neuro.Al Algorithm results are used on iNtuition, all the standard features offered by iNtuition 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 Neuro.Al algorithm can be used by physicians to aid in the diagnosis. 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.

The provided document describes the Neuro.AI Algorithm and its substantial equivalence to a predicate device, iNtuition-TDA, TVA, Parametric Mapping (K131447). However, it does not contain a detailed performance study with specific acceptance criteria and reported device performance in the format requested. The document focuses on regulatory compliance, outlining the device's indications for use, technological characteristics, and a general statement about software verification and validation.

Therefore, many of the requested items cannot be extracted directly from this document.

Here's a breakdown of what can and cannot be answered based on the provided text:

1. A table of acceptance criteria and the reported device performance

Not provided in the document. The text states: "During software testing, all predefined acceptance criteria for the Neuro.Al Algorithm were met and all software test cases passed." However, it does not specify what those acceptance criteria were or provide a table of performance metrics.

2. Sample size used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)

Not provided in the document. The document mentions "software testing and performance evaluation" but does not detail the test set's sample size or data provenance.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g. radiologist with 10 years of experience)

Not provided in the document. The document describes the device's intended use by "trained professionals, including but not limited to physicians, surgeons and medical clinicians" but doesn't specify how ground truth was established for testing.

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

Not provided in the document.

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

Not provided in the document. The document does not describe a comparative effectiveness study involving human readers with and without AI assistance. The focus is on the device's substantial equivalence to a predicate device.

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

Yes, implicitly. The document describes the "Neuro.AI Algorithm as a standalone image processing software device." The testing mentioned ("software testing and performance evaluation") would inherently be evaluating the algorithm's standalone performance against its predefined acceptance criteria, even if those criteria aren't explicitly detailed. The statement "The Neuro.AI Algorithm is as safe and effective as the predicate device" implies standalone testing for functional equivalence.

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

Not explicitly provided in the document. While the device assists in diagnosis, the method for establishing ground truth for testing is not described.

8. The sample size for the training set

Not provided in the document. The document describes a "510(k) summary," which focuses on demonstrating substantial equivalence to a predicate device rather than detailing AI model development specifics like training set size.

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

Not provided in the document. Similar to the training set size, the method for establishing ground truth for training is not included in this regulatory summary.

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iNtuition-T1 Mapping and T2/T2* Mapping are software modules that support the derivation and quantification of T1, T2 and T2* values from MR DICOM image pixel intensities and header information. The quantification of these parameters can be used to characterize tissues. Support is provided to overlay the T1, T2, and T2* values using colormaps on related MR images.

Support is provided for using different colormaps to overlay different ranges of T1, T2 or T2* values and restrict the overlay to region of interest on the images can be of simple planar scan like a single slice or volumetric or 4D scans of a body part. iNtuition-T1 Mapping and T2/T2* Mapping are iNtuition software features that can be used in multiple workflows or be used as basic tools for cardiac functionality, the overlaid images can be captured and forwarded to other systems using standards such as DICOM or http protocol. Quantitative analysis is derived and available as text and graphical display.

iNtuition- T1 Mapping and T2/T2* Mapping qualitation can be used in a clinical setting on MR images of an individual patient and can be used to support the clinical decision making for the patient. iNtuition- T1 Mapping and T2/T2* Mapping are designed for use by healthcare professionals and are intended to assist the physician in diagnosis, who is responsible for making all final patient management decisions.

iNtuition-T1 Mapping and T2/T2* Mapping is an optional image post-processing module, part of iNtuition (K121916), which is software only device generally used with the off-the-shelf hardware, offered in various configuration, with the simplest configuration being a stand-alone workstation capable of image review, communications, archiving, database maintenance, remote review, reporting and basic 3D capabilities. It can also be configured as a server with some, all, or none of its optional features disabled. Whether provided as a workstation or a server, the iNtuition software is designed to provide access by a local user physically sitting at the computer hosting the iNtuition server software, and/or by one or more remote users who concurrently connect to the server using a freely-downloadable thin client application or through a zero-footprint web viewer (with conference capabilities) over local network or internet.

iNtuition-T1 Mapping and T2/T2* Mapping feature can derive quantitative values from intensities and header information of specific MR scan sequences that are specifically coded to enable such derivation (such as Look-Locker and MOLLI for T1.) The quantification of these parameters can be used to derive clinical value such as T2*.. Post-processing such as computation of statistics like volume, area, min/max or various combinations of the derived values, over regions of interests or overlay the derived values using a colormap on related images or a region of the images. The region of interests can be defined by the user through manual, semi-automatic or automatic segmentation techniques provided by iNtuition. The derivation and post-processing can be used with planar, volumetric or 4D scan sequences for cardiac functionality.

iNtuition-T1 Mapping and T2/T2* Mapping is an iNtuition based optional features, and employ all standard features offered by iNtuition such as convenient image manipulation tools like drawing region of interest, manual or automatic segmentation of structures and 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.

This device is intended only to assists the operator in making decisions. The software is not intended to replace the skill and judgment of a qualified medical practitioner and should only be used by people that have been trained in the software's function (iNtuition), 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.

iNtuition-T1 Mapping and T2/T2* Mapping modules may be sold separately or as an extension of iNtuition.

This submission for K180916 does not provide specific acceptance criteria or a study demonstrating that the device meets those criteria, as it is a Traditional 510(k) stating the product is substantially equivalent to a predicate device and did not require clinical studies.

Therefore, many of the requested details cannot be extracted from the provided text.

However, based on the principle of substantial equivalence, the "acceptance criteria" are generally that the device performs as well as the predicate device across its intended use and technological characteristics.

Here's a breakdown of what can be inferred or stated from the document:

1. Table of Acceptance Criteria and Reported Device Performance:

Since no specific performance metrics or acceptance criteria are listed, this table cannot be populated directly. The document repeatedly states that the device is "substantially equivalent" to its predicate and "performs as well as the predicate device" in terms of its intended use and technological characteristics.

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

• Sample Size for Test Set: Not specified.
• Data Provenance: Not specified (e.g., country of origin, retrospective/prospective). The document mentions "Software verification and validation was completed in accordance with internal processes" and "Performance testing was carried out according to internal company procedures." This implies internal testing rather than a large-scale external test set with specific patient data.

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

• Number of Experts: Not specified.
• Qualifications of Experts: Not specified. Software testing was reviewed by "designated technical professionals."

4. Adjudication Method for the Test Set:

• Adjudication Method: Not specified.

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

• MRMC Study: No. The document explicitly states: "The subject of this traditional 510k notification, iNtuition-T1 Mapping and T2/T2* Mapping, did not require clinical studies to show safety and effectiveness of the software." Therefore, no MRMC study was conducted.
• Effect Size of Human Readers Improvement with AI vs. Without AI Assistance: Not applicable, as no MRMC study was performed.

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

• Standalone Study: Not explicitly detailed as a separate "study" with performance metrics. The document describes the software's functionality in deriving and quantifying T1, T2, and T2* values, and mentions "Software verification and validation" and "Performance testing" were conducted internally to ensure it met design intent and was equivalent to the predicate. This would constitute standalone testing of the algorithm's output against expected results, but the specifics are not provided.

7. Type of Ground Truth Used:

• Type of Ground Truth: Not explicitly stated. For internal performance testing of quantitative measurements (like T1/T2/T2* values), ground truth would likely be established through:
  • Reference standards/phantoms: Using known values.
  • Comparison to predicate device's output: Ensuring the new device's output matches that of the already cleared predicate.
  • Manual calculations/expert evaluation: For regions of interest.

The document indicates the software supports "derivation and quantification of T1, T2 and T2* values from MR DICOM image pixel intensities and header information," suggesting the ground truth for these values would be based on the principles of MRI physics and potentially established clinical methods for calculating these parameters.

8. Sample Size for the Training Set:

• Sample Size for Training Set: Not specified. The document describes the software as an "optional image post-processing module" that derives quantitative values from specific MR scan sequences. This doesn't inherently suggest a machine learning model that requires a "training set" in the common sense (i.e., for supervised learning). It's more about algorithmic derivation and quantification. If any machine learning components were involved, the training set details are not disclosed.

9. How the Ground Truth for the Training Set was Established:

• How Ground Truth for Training Set was Established: Not applicable/not specified, as training set details are not provided and the primary function described is algorithmic derivation rather than machine learning inference.

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