Search Results

BreView is a tool to aid clinicians with the review of multi-parametric breast magnetic resonance (MR) images. The combination of acquired images, reconstructed images, annotations, and measurements performed by the clinician are intended to provide the referring physician with clinically relevant information that may aid in diagnosis and treatment planning.

BreView is a dedicated advanced visualization review and post-processing tool that facilitates optimized post-processing of MR breast data review and assessment workflows for radiologists - including organizing images and composing reports. Adding the techniques of automatic motion correction and subtraction improves the review process. Software functionalities include:

• Ability to load 2D, 3D and 4D MR datasets as well as DICOM Secondary Captures (SCPT) .
• Optimized display of multi-parametric images within a dedicated layout
• Display customization: ability to choose layout, orientation, rendering mode
• Guided workflows for reviewing and processing MR breast exams
• Automated motion correction and/or subtraction of multi-phase datasets
• . Multi-planar reformats and maximum intensity projections (MIPs)
• Semi-automated segmentation and measurement tools
• Graph view for multi-phase datasets
• Save and export capabilities though DICOM Secondary Captures
• Data export in the form of a summary table

The provided text does not contain detailed information about specific acceptance criteria or a comprehensive study that proves the device meets acceptance criteria. The document is primarily a 510(k) premarket notification, focusing on demonstrating substantial equivalence to a predicate device rather than presenting a detailed performance study with specific acceptance metrics.

However, based on the type of information typically expected for such a submission and the limited details provided, I can infer some aspects and highlight what is missing.

Here's an analysis based on the provided text, structured to address your questions as much as possible, while also noting where information is explicitly not present:

Context: The device, "BreView," is a medical diagnostic software intended to aid clinicians in reviewing multi-parametric breast MR images. Its core functionalities include image organization, display customization, automated motion correction and/or subtraction, multi-planar reformats, semi-automated segmentation and measurement tools, graph view, and data export. It is marketed as a Class II device.

1. Table of Acceptance Criteria and Reported Device Performance

Critique: The document does not provide a specific table of acceptance criteria with corresponding performance metrics. It mentions "Performance testing (Verification, Validation)" and "Bench testing" but lacks quantifiable results against predefined acceptance thresholds.

However, we can infer some criteria from the "Comparison" table against the predicate device, although these are qualitative comparisons rather than quantitative performance data.

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

• Sample Size for Test Set: Not specified. The document mentions "Bench testing was conducted on a database of exams representative of the clinical scenarios where BreView is intended to be used, with consideration of acquisition protocols and clinical indicators." However, no numerical sample size (e.g., number of cases, number of images) is provided for this test set.
• Data Provenance: Not specified. There is no mention of the country of origin of the data or whether the data was retrospective or prospective.

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. The document mentions "End user evaluation of the brush," implying radiologists or similar clinical professionals, but their specific qualifications or number are not detailed.

4. Adjudication Method for the Test Set

• Adjudication Method: None stated. The document does not describe any specific adjudication method (e.g., 2+1, 3+1 consensus) used for establishing ground truth or evaluating performance.

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

• MRMC Study Conducted: No. The document does not describe a formal MRMC study where human readers' performance with and without AI assistance was evaluated. The evaluation appears to be primarily focused on the device's technological equivalence rather than its impact on human reader effectiveness.
• Effect Size of Improvement: Not applicable, as no MRMC study was described.

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

• Standalone Performance Evaluation: Implied, but not explicitly quantified with metrics. The statement, "BreView Non-Rigid Registration algorithm was tested and found to be successfully implemented," suggests a standalone evaluation of this specific algorithm. However, no quantitative performance metrics (e.g., accuracy, precision, dice score for segmentation, registration error) are provided for any standalone algorithm. The "equivalence or better" statement for the Smart Brush tool is a comparative qualitative assessment based on "End user evaluation," not a formal standalone study result.

7. Type of Ground Truth Used

• Type of Ground Truth: The document does not explicitly define how ground truth was established for "bench testing." For functionalities like non-rigid registration and image subtraction, the "ground truth" might be implied by successful implementation against a known good algorithm (the predicate's method). For the "Smart Brush tool," the ground truth seems to be based on expert (end-user) evaluation comparing its behavior to the predicate's tool. There's no mention of pathology, clinical outcomes data, or a formal consensus process.

8. Sample Size for the Training Set

• Sample Size for Training Set: Not specified. The document only mentions "Bench testing was conducted on a database of exams representative of the clinical scenarios," which refers to a test set, not a training set. Given that this is a 510(k) submission and the device is presented as applying "the same fundamental scientific technology as its predicate device," it is possible that new training data (in the context of deep learning) was not a primary focus of this submission, if the core algorithms are established or rule-based.

9. How Ground Truth for the Training Set Was Established

• Ground Truth Establishment for Training Set: Not applicable / Not specified. Since no training set or related machine learning components requiring explicit ground truth labeling were discussed in detail, this information is not provided.

Ask a specific question about this device