(57 days)
CADstream is a Computer Aided Detection (CAD) system intended for use in analyzing magnetic resonance imaging (MRI) studies. CADstream automatically registers serial patient image acquisitions to minimize the impact of patient motion, segments and labels tissue types based on enhancement characteristics (parametric image maps), and performs other user-defined post-processing functions (image subtractions, multiplanar reformats, maximum intensity projections).
When interpreted by a skilled physician, this device provides information that may be useful in screening and diagnosis. CADstream can also be used to provide accurate and reproducible measurements of the longest diameters and volume of segmented tissues. Patient management decisions should not be made based solely on the results of CADstream analysis.
The CADstream device relies on the assumption that pixels having similar MR signal intensities represent similar tissues. The CADstream software simultaneously analyzes the pixel signal intensities from multiple MR sequences and applies multivariate pattern recognition methods to perform tissue segmentation and classification.
The CADstream system consists of proprietary software developed by Confirma installed on an off-the-shelf personal computer and a monitor configured as an CADstream display station.
The provided document is a 510(k) summary for the CADstream Version 2.0 MRI Image Processing Software. It does not contain detailed information about specific acceptance criteria or an explicit study proving performance against such criteria. The document focuses on the device's intended use, description, software development processes, and regulatory substantiation.
Here's an analysis based on the information provided, highlighting what is present and what is missing:
Description of Acceptance Criteria and Study to Prove Device Meets Them
1. Table of Acceptance Criteria and Reported Device Performance:
The document mentions that "Performance testing of the features described in the user manual has been successfully completed utilizing clinical datasets" and "Software beta testing also has been completed, validating that the requirements for these features have been met." However, it does not explicitly list the acceptance criteria in terms of specific performance metrics (e.g., sensitivity, specificity, accuracy, precision of measurements) or the quantitative results of these tests.
The document describes the device's functions:
- Automatically registers serial patient image acquisitions to minimize motion impact.
- Segments and labels tissue types based on enhancement characteristics (parametric image maps).
- Performs user-defined post-processing functions (image subtractions, multiplanar reformats, maximum intensity projections).
- Provides accurate and reproducible measurements of the longest diameters and volume of segmented tissues.
Without explicit acceptance criteria and corresponding performance metrics, a table cannot be constructed.
2. Sample Size Used for the Test Set and Data Provenance:
The document states "Performance testing... has been successfully completed utilizing clinical datasets." However, it does not specify the sample size (number of cases or patients) used for this testing. It also does not provide information on the data provenance (e.g., country of origin, retrospective or prospective nature).
3. Number of Experts Used to Establish Ground Truth for the Test Set and Their Qualifications:
The document does not provide details about the number of experts, their qualifications, or how ground truth was established for the clinical datasets used in performance testing.
4. Adjudication Method for the Test Set:
The document does not describe any adjudication method (e.g., 2+1, 3+1 consensus) used for the test set.
5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study:
The document does not mention a multi-reader multi-case (MRMC) comparative effectiveness study comparing human readers with and without AI assistance, nor does it specify any effect size or improvement. The "Intended Use Statement" notes that "When interpreted by a skilled physician, this device provides information that may be useful in screening and diagnosis" and "Patient management decisions should not be made based solely on the results of CADstream analysis," implying human oversight but not a formal comparative study of reader performance.
6. Standalone (Algorithm Only) Performance Study:
The document describes the device's features and states "CADstream automatically registers... segments and labels... and performs other user-defined post-processing functions... CADstream can also be used to provide accurate and reproducible measurements..." This implies standalone algorithmic capabilities. However, it does not present a dedicated standalone performance study with quantitative metrics (e.g., sensitivity, specificity, F1-score) in isolation from human interpretation. The primary use case described involves interpretation by a skilled physician.
7. Type of Ground Truth Used:
The document refers to "clinical datasets" but does not specify the type of ground truth used (e.g., expert consensus, pathology reports, follow-up outcomes data) for evaluating the device's performance.
8. Sample Size for the Training Set:
The document does not specify the sample size of the training set used for developing the multivariate pattern recognition methods.
9. How the Ground Truth for the Training Set Was Established:
The document states that "CADstream software simultaneously analyzes the pixel signal intensities from multiple MR sequences and applies multivariate pattern recognition methods to perform tissue segmentation and classification." However, it does not describe how the ground truth for training these methods was established.
In summary, the provided document is a high-level regulatory submission that attests to developmental processes and general performance testing but lacks the specific quantitative details typically found in a clinical study report regarding acceptance criteria, sample sizes, expert involvement, and explicit performance metrics.
§ 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).