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510(k) Data Aggregation
(58 days)
IB Neuro™ software allows the post-processing and display of dynamically acquired MR datasets to evaluate image intensity variations over time. IB Neuro™ v1.0 plug-in accepts data from existing MRI systems, performs quality control checks and generates parametric perfusion maps such as Relative Cerebral Blood Volume (rCBV), Cerebral Blood Flow (CBF), Mean Transit Time (MTT) and Time to Peak (TTP) and sends the maps to a PACS for subsequent viewing. These images when interpreted by a trained physician may yield information useful in clinical applications. Our advanced technology is designed to be compliant with healthcare standards such as DICOM and is easily and rapidly integrated into existing medical image visualization applications.
IB Neuro "" OsiriX Plugin is software designed to analvze dynamically acquired datasets. Using well-established algorithms, parametric perfusion maps can be generated such as Relative Cerebral Blood Volume (rCBV), Cerebral Blood Flow (CBF), Mean Transit Time (MTT) and Time to Peak (TTP). The strength of our software is its ability to extend the productivity of any existing viewer, CAD workstation or PACS via a platform-independent base library that allows for quick and seamless integration into existing server and workstation applications. It also includes other critical features such as:
- Enables rapid creation of a complete array of critical perfusion parameter o maps of rCBV, CBF, MTT, TTP
- o Automated correction of contrast agent leakage for rCBV maps
- o Automated brain mask generation
- Ability to normalize parameters to normal appearing white matter (NAWM) o
- o Automated report generation
- View dynamic signal time course on a per-voxel basis o
- Interactive Arterial Input Function (AIF) selection o
- o Automatic export of perfusion parameter maps to DICOM images within the same study
Acceptance Criteria and Device Performance Study for IB Neuro™ v1.0
The provided document describes the 510(k) submission for IB Neuro™ v1.0. This submission primarily focuses on establishing substantial equivalence to predicate devices, rather than presenting a performance study with specific acceptance criteria and detailed performance metrics.
1. Table of Acceptance Criteria and Reported Device Performance
The submission does not provide specific, quantifiable acceptance criteria or a table of reported device performance in terms of diagnostic accuracy metrics (e.g., sensitivity, specificity, AUC). The primary performance claim is that the device "performs quality control checks and generates parametric perfusion maps" and for "image analysis and processing and generation of parametric maps to provide additional information beyond standard imaging."
Since there are no explicit acceptance criteria or quantitative performance metrics reported in the provided text, the table below reflects the general claims and the basis for the 510(k) clearance:
| Acceptance Criteria (Implied) | Reported Device Performance |
|---|---|
| Functional Equivalence: Ability to accept data from existing MRI systems. | "IB Neuro™ v1.0 plug-in accepts data from existing MRI systems" |
| Parametric Map Generation: Ability to generate rCBV, CBF, MTT, and TTP maps. | "generates parametric perfusion maps such as Relative Cerebral Blood Volume (rCBV), Cerebral Blood Flow (CBF), Mean Transit Time (MTT) and Time to Peak (TTP)" |
| Quality Control: Performs quality control checks. | "performs quality control checks" |
| Data Export: Sends maps to PACS for subsequent viewing. | "sends the maps to a PACS for subsequent viewing." and "Automatic export of perfusion parameter maps to DICOM images within the same study" |
| Additional Features: Automated correction of contrast agent leakage, automated brain mask, normalization to NAWM, automated report generation, view dynamic signal time course, interactive AIF selection. | Device description lists these features. |
| Substantial Equivalence: Features and intended use are similar to predicate devices, and differences do not raise new safety/effectiveness questions. | "The intended use and performance characteristics for IB Neuro™ are substantially equivalent to the predicate devices" and "documentation supplied in this submission demonstrates that any difference in technological characteristics do not raise any new questions of safety or effectiveness." |
| Software Validation: Compliance with FDA's software validation guidance. | "Performance testing included software validation, verification and testing per FDA's software validation guidance." |
2. Sample Size Used for the Test Set and Data Provenance
The provided document states: "Discussion of Clinical Tests Performed: N/A". This indicates that no clinical tests, and therefore no specific test set with a defined sample size, data provenance, or ground truth, were performed for this 510(k) submission. The clearance was based on demonstrating substantial equivalence to predicate devices and non-clinical software validation.
3. Number of Experts Used to Establish Ground Truth for the Test Set and Qualifications
As no clinical tests were performed, there was no test set requiring expert-established ground truth.
4. Adjudication Method for the Test Set
As no clinical tests were performed, there was no test set requiring an adjudication method.
5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study
No MRMC study was conducted as indicated by "Discussion of Clinical Tests Performed: N/A". Therefore, there is no reported effect size of how much human readers improve with AI vs. without AI assistance.
6. Standalone Performance Study (Algorithm only without human-in-the-loop performance)
No standalone performance study of the algorithm's diagnostic accuracy metrics was conducted for this submission, as indicated by "Discussion of Clinical Tests Performed: N/A". The focus was on the software's functional capabilities and substantial equivalence.
7. Type of Ground Truth Used
No clinical ground truth (e.g., expert consensus, pathology, outcomes data) was used for this submission, as clinical tests were not performed.
8. Sample Size for the Training Set
The document does not provide information about a training set or its sample size. This is typical for submissions based on substantial equivalence and software validation, where the focus is on the software's ability to process and generate standard outputs rather than its performance against a diagnostic gold standard learned from data. The algorithms are described as "well-established."
9. How the Ground Truth for the Training Set Was Established
As no training set is mentioned or implied, the method for establishing ground truth for a training set is not applicable here.
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