(73 days)
Quantib AI Node is a software platform that provides visualization tools and enables external postprocessing extensions for medical images.
The software platform is designed to support in qualitative and quantitative measurement, analysis, and reporting of clinical data.
The software platform provides means for storing of data and transferring data from and into other systems such as PACS. The software platform provides an interface to integrate processing extensions and custom input/output modules.
Quantib AI Node functionality includes:
- · Interface for multi-modality and multi-vendor input/output of data, such as DICOM data
- · Initiation of extensions to process the data based on properties of the data
- · Interface for extensions that provide custom data import/output, post-processing, and user interface functionality
- · User interface for visualization and annotation of medical images, and for correction and confirmation of results generated by post-processing extensions
Quantib AI Node is intended to be used by trained medical professionals.
Quantib Al Node (QBX) is a stand-alone software platform that enables external post-processing extensions for medical images and provides visualization and annotation tools. It can automatically process data received via a DICOM connection and automatically export results to external DICOM nodes. It allows for configuring workflows that can contain user-interaction steps to review and correct automatic results.
The provided document is a 510(k) summary for the Quantib AI Node. It does not contain detailed information about a specific study comparing device performance against acceptance criteria with quantitative results, sample sizes, expert qualifications, or adjudication methods.
This document describes the Quantib AI Node as a software platform that provides visualization and annotation tools and enables external post-processing extensions for medical images. It's intended to support trained medical professionals in qualitative and quantitative measurement, analysis, and reporting of clinical data. It is not an AI algorithm that performs diagnostic tasks itself, but rather a platform to integrate such extensions.
Therefore, the typical metrics and study design elements you've requested (e.g., sensitivity, specificity, reader improvement with AI, standalone performance of an algorithm) are not applicable to this specific 510(k) submission, as it concerns a software platform, not a diagnostic AI algorithm.
However, I can extract information related to its "performance" in the context of a software platform:
1. Table of Acceptance Criteria and Reported Device Performance:
Since this is a software platform, the "performance" relates to its functionality, adherence to standards, and verification/validation.
| Acceptance Criteria Category | Reported Device "Performance" (Meeting Criteria) |
|---|---|
| Non-clinical Performance / Functionality | Bench testing performed to test the functionality of the system and measurement tools. Did not reveal any issues, demonstrating performance is as safe and effective as predicate devices. |
| Standards Met | Compliance with: |
- ANSI AAMI ISO 14971:2007/(R)2010 (Risk Management)
- ANSI AAMI IEC 62304:2006/A1:2016 (Software Life Cycle Processes)
- ANSI AAMI IEC 62366-1:2015 (Usability Engineering) |
| Software Verification and Validation | Tested in accordance with verification and validation processes and planning. Testing results support that all system requirements have met their pre-defined acceptance criteria. |
| Safety Implications | Differences from predicate devices do not affect safety. Based on Failure Mode and Effects Analysis (FMEA) and risk category classification. Does not introduce new safety issues. |
| Compatibility | Conforms to NEMA PS 3.1-3.20 (2016) DICOM set. DICOM conformance statement included. |
| Ruler Tool | Available. |
| ROI Volume Measurement | Available and equal to ROI measurements in predicate devices. |
| Required Input | DICOM compatible data. |
2. Sample Size Used for the Test Set and Data Provenance:
This information is not provided in the document as it's not relevant for a software platform's 510(k) where the primary focus is on functionality, safety, and equivalence to predicates, rather than diagnostic accuracy on a specific disease with a test set of medical images. The testing described is "bench testing" of the software system.
3. Number of Experts Used to Establish Ground Truth for the Test Set and Qualifications:
This information is not provided and is not applicable to this type of software platform submission. Ground truth established by experts is typically a requirement for AI algorithms performing interpretations.
4. Adjudication Method for the Test Set:
This information is not provided and is not applicable.
5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was done:
No, an MRMC comparative effectiveness study was not done and is not applicable for this software platform. The document focuses on demonstrating substantial equivalence in terms of functionality and safety to predicate devices, not on the clinical effectiveness or reader improvement of a specific AI algorithm.
6. If a Standalone (algorithm only without human-in-the-loop performance) was done:
While the document states the Quantib AI Node is a "stand-alone software platform," this refers to its independence as a software application, not a standalone diagnostic algorithm performance. The document itself makes it clear that the platform "does not make diagnoses" and provides tools for human professionals. Therefore, a standalone performance study in the context of diagnostic accuracy was not performed or needed for this submission.
7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.):
This information is not provided and is not applicable. The "ground truth" for this software platform relates to whether its functionalities (e.g., measurement tools, data handling, interfaces) perform as designed, not whether it accurately detects a disease.
8. The Sample Size for the Training Set:
This information is not provided and is not applicable. The Quantib AI Node is a platform; it is not itself an AI algorithm that undergoes training. Any external post-processing extensions integrated into the platform might be AI-based and would have their own training sets, but that is outside the scope of this platform's 510(k) submission.
9. How the Ground Truth for the Training Set was Established:
This information is not provided and is not applicable.
§ 892.2050 Medical image management and processing system.
(a)
Identification. A medical image management and processing system is a device that provides one or more capabilities relating to the review and digital processing of medical images for the purposes of interpretation by a trained practitioner of disease detection, diagnosis, or patient management. The software components may provide advanced or complex image processing functions for image manipulation, enhancement, or quantification that are intended for use in the interpretation and analysis of medical images. Advanced image manipulation functions may include image segmentation, multimodality image registration, or 3D visualization. Complex quantitative functions may include semi-automated measurements or time-series measurements.(b)
Classification. Class II (special controls; voluntary standards—Digital Imaging and Communications in Medicine (DICOM) Std., Joint Photographic Experts Group (JPEG) Std., Society of Motion Picture and Television Engineers (SMPTE) Test Pattern).