(244 days)
CADstream is intended to be used in the visualization, analysis, and reporting of magnetic resonance imaging (MRI) studies. CADstream supports evaluation of dynamic MR data acquired during contrast administration. CADstream performs other user selected processing functions (such as image registration, subtractions, measurements, 3D renderings, and reformats).
CADstream also includes user-configurable features for reporting on findings in breast or general MRI studies. Additionally, CADstream assists users in planning MRI guided interventional procedures.
When interpreted by a skilled physician, this device provides information that may be used for screening, diagnosis, and interventional planning. Patient management decisions should not be made based solely on the results of CADstream.
CADstream may also be used as an image viewer of multi-modality, digital images, including ultrasound and mammography. CADstream is not intended for primary interpretation of digital mammography images.
CADstream is an image processing system designed to assist in the visualization, analysis, and reporting of magnetic resonance imaging (MRI) studies. CADstream also is intended to provide workflow efficiency and interventional planning tools.
CADstream receives DICOM magnetic resonance images from a PACS or directly from the MRI scanner. As they are received, CADstream processes and displays the results on the CADstream server or a client personal computer.
Available features support:
- Visualization (standard image viewing tools, MIPs, and reformats)
- Analysis (registration, subtractions, coil inhomogeneity correction, kinetic curves, parametric image maps, and 3D volume rendering)
- Reporting of user-selected findings and assessment
- Interventional planning
- Workflow efficiency
- Communication and storage (DICOM import/export, query/retrieve, and study storage)
The CADstream system consists of proprietary software developed by Confirma installed on an off-the-shelf computer.
Here's a breakdown of the acceptance criteria and study information for the CADstream Version 5 device, based on the provided text:
1. Table of Acceptance Criteria and Reported Device Performance:
The provided text does not explicitly state specific numerical acceptance criteria for performance metrics (e.g., sensitivity, specificity, accuracy, or quantitative measures of visualization/analysis improvement). Instead, the performance testing is described in a more general sense, focusing on verification and validation against requirements and substantial equivalence to predicate devices.
Therefore, the table will reflect the general statements made about performance rather than specific, measurable criteria.
| Acceptance Criteria (Implied from text) | Reported Device Performance |
|---|---|
| Device meets input requirements (Verification Testing) | CADstream version 5 has successfully undergone extensive verification testing to verify the device meets input requirements. |
| Product modifications do not raise new safety or effectiveness concerns | Bench validation testing was also performed to demonstrate the product modifications from version 4 resulted in a substantially equivalent product and did not raise any new safety or effectiveness concerns. |
| Device conforms to user needs and intended uses (Clinical Validation) | Additionally, the completed clinical validation testing demonstrates the device conforms to user needs and intended uses per 21CFR820.30(g). |
| Device is safe and effective | "The successful completion of verification and validation testing has demonstrated conformance to design controls, user needs, and intended use, and that the device is safe and effective." |
| "Based on the information supplied in this 510(k), we conclude that the subject device is safe, effective, and substantially equivalent to the predicate devices." | |
| Substantially equivalent to predicate devices | Bench validation testing demonstrated substantial equivalence to version 4 and other predicate devices in terms of features (as shown in Table 1 and Table 2 comparison) and overall safety and effectiveness. The 510(k) summary explicitly concludes substantial equivalence to predicate devices based on the submitted information. |
| Provides features for visualization, analysis, and reporting of MR images | Device description and feature comparison tables illustrate the capability to perform: |
- Visualization (standard image viewing tools, MIPs, reformats, 3D volume rendering)
- Analysis (registration, subtractions, coil inhomogeneity correction, kinetic curves, parametric image maps)
- Reporting of user-selected findings and assessment
- Interventional planning
- Workflow efficiency
- Communication and storage (DICOM import/export, query/retrieve, and study storage) |
2. Sample Size Used for the Test Set and Data Provenance:
- Sample Size for Test Set: The document does not specify the sample size (number of patients or cases) used for the "clinical validation testing."
- Data Provenance: The document does not specify the country of origin of the data or whether it was retrospective or prospective.
3. Number of Experts Used to Establish Ground Truth for the Test Set and Qualifications:
- Number of Experts: The document does not specify the number of experts used to establish ground truth for the test set.
- Qualifications of Experts: The document does not specify the qualifications of any experts involved in establishing ground truth. The "Indications for Use" section mentions interpretation "by a skilled physician," but this is a general statement about clinical use, not a description of ground truth establishment during testing.
4. Adjudication Method for the Test Set:
- The document does not mention any specific adjudication method (e.g., 2+1, 3+1, none) used for the test set.
5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done:
- The document does not mention a multi-reader multi-case (MRMC) comparative effectiveness study, nor does it discuss the effect size of human readers' improvement with or without AI assistance. The focus is on device features and general validation, not comparative reader performance.
6. If a Standalone (Algorithm Only Without Human-in-the-Loop Performance) Was Done:
- The document describes "Performance Testing" including "extensive verification testing," "bench validation testing," and "clinical validation testing." While these tests would involve evaluating the algorithm's functionality, the document does not explicitly state that a standalone performance study comparing the algorithm's diagnostic accuracy against a ground truth was conducted without a human in the loop. The device is described as an "image processing system designed to assist," implying a human-in-the-loop context for its intended use and evaluation.
7. The Type of Ground Truth Used:
- The document does not explicitly state the type of ground truth used for any testing (e.g., expert consensus, pathology, outcomes data). It broadly refers to "user needs and intended uses" for clinical validation.
8. The Sample Size for the Training Set:
- The document does not mention a training set or its sample size. This is typical for 510(k) submissions of this era for image processing systems, as they often leverage established algorithms or feature sets rather than reporting on deep learning model training.
9. How the Ground Truth for the Training Set Was Established:
- Since a training set is not mentioned, the document does not describe how ground truth for a training set was established.
§ 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).