(216 days)
MammoScreen® 4 is a concurrent reading and reporting aid for physicians interpreting screening mammograms. It is intended for use with compatible full-field digital mammography and digital breast tomosynthesis systems. The device can also use compatible prior examinations in the analysis.
Output of the device includes graphical marks of findings as soft-tissue lesions or calcifications on mammograms along with their level of suspicion scores. The lesion type is characterized as mass/asymmetry, distortion, or calcifications for each detected finding. The level of suspicion score is expressed at the finding level, for each breast, and overall for the mammogram.
The location of findings, including quadrant, depth, and distance from the nipple, is also provided. This adjunctive information is intended to assist interpreting physicians during reporting.
Patient management decisions should not be made solely based on the analysis by MammoScreen 4.
MammoScreen 4 is a concurrent reading medical software device using artificial intelligence to assist radiologists in the interpretation of mammograms.
MammoScreen 4 processes the mammogram(s) and detects findings suspicious for breast cancer. Each detected finding gets a score called the MammoScreen Score™. The score was designed such that findings with a low score have a very low level of suspicion. As the score increases, so does the level of suspicion. For each mammogram, MammoScreen 4 outputs the detected findings with their associated score, a score per breast, driven by the highest finding score for each breast, and a score per case, driven by the highest finding score overall. The MammoScreen Score goes from one to ten.
MammoScreen 4 is available for 2D (FFDM images) and 3D processing (FFDM & DBT or 2DSM & DBT). Optionally, MammoScreen 4 can use prior examinations in the analysis.
The results indicating potential breast cancer, identified by MammoScreen 4, are accessible via a dedicated user interface and can seamlessly integrate into DICOM viewers (using DICOM-SC and DICOM-SR). Reporting aid outputs can be incorporated into the practice's reporting system to generate a preliminary report.
Note that the MammoScreen 4 outputs should be used as complementary information by radiologists while interpreting mammograms. For all cases, the medical professional interpreting the mammogram remains the sole decision-maker.
The provided text describes the acceptance criteria and a study to prove that MammoScreen® 4 meets these criteria. Here is a breakdown of the requested information:
Acceptance Criteria and Device Performance
1. Table of Acceptance Criteria and Reported Device Performance
Rationale for using "MammoScreen 2" data for comparison: The document states that the standalone testing for MammoScreen 4 compared its performance against "MammoScreen 2 on Dimension". While MammoScreen 3 is the predicate device, the provided performance data in the standalone test section specifically refers to MammoScreen 2. The PCCP section later references performance targets for MammoScreen versions 1, 2, and 3, but the actual "Primary endpoint" results for the current device validation are given in comparison to MammoScreen 2. Therefore, the table below uses the reported performance against MammoScreen 2 as per the "Primary endpoint" section.
| Metric | Acceptance Criteria | Reported Device Performance (MammoScreen 4 vs. MammoScreen 2) |
|---|---|---|
| Primary Objective | Non-inferiority in standalone cancer detection performance compared to the previous version of MammoScreen (specifically MammoScreen 2 on Dimension). | Achieved. |
| AUC at the mammogram level | Positive lower bound of the 95% CI of the difference in endpoints between MammoScreen 4 and MammoScreen 2. | MS4: 0.894 (0.870, 0.919) |
| MS2: 0.867 (0.839, 0.896) | ||
| Δ: 0.027 (0.002, 0.052), p |
§ 892.2090 Radiological computer-assisted detection and diagnosis software.
(a)
Identification. A radiological computer-assisted detection and diagnostic software is an image processing device intended to aid in the detection, localization, and characterization of fracture, lesions, or other disease-specific findings on acquired medical images (e.g., radiography, magnetic resonance, computed tomography). The device detects, identifies, and characterizes findings based on features or information extracted from images, and provides information about the presence, location, and characteristics of the findings to the user. The analysis is intended to inform the primary diagnostic and patient management decisions that are made by the clinical user. The device is not intended as a replacement for a complete clinician's review or their clinical judgment that takes into account other relevant information from the image or patient history.(b)
Classification. Class II (special controls). The special controls for this device are:(1) Design verification and validation must include:
(i) A detailed description of the image analysis algorithm, including a description of the algorithm inputs and outputs, each major component or block, how the algorithm and output affects or relates to clinical practice or patient care, and any algorithm limitations.
(ii) A detailed description of pre-specified performance testing protocols and dataset(s) used to assess whether the device will provide improved assisted-read detection and diagnostic performance as intended in the indicated user population(s), and to characterize the standalone device performance for labeling. Performance testing includes standalone test(s), side-by-side comparison(s), and/or a reader study, as applicable.
(iii) Results from standalone performance testing used to characterize the independent performance of the device separate from aided user performance. The performance assessment must be based on appropriate diagnostic accuracy measures (
e.g., receiver operator characteristic plot, sensitivity, specificity, positive and negative predictive values, and diagnostic likelihood ratio). Devices with localization output must include localization accuracy testing as a component of standalone testing. The test dataset must be representative of the typical patient population with enrichment made only to ensure that the test dataset contains a sufficient number of cases from important cohorts (e.g., subsets defined by clinically relevant confounders, effect modifiers, concomitant disease, and subsets defined by image acquisition characteristics) such that the performance estimates and confidence intervals of the device for these individual subsets can be characterized for the intended use population and imaging equipment.(iv) Results from performance testing that demonstrate that the device provides improved assisted-read detection and/or diagnostic performance as intended in the indicated user population(s) when used in accordance with the instructions for use. The reader population must be comprised of the intended user population in terms of clinical training, certification, and years of experience. The performance assessment must be based on appropriate diagnostic accuracy measures (
e.g., receiver operator characteristic plot, sensitivity, specificity, positive and negative predictive values, and diagnostic likelihood ratio). Test datasets must meet the requirements described in paragraph (b)(1)(iii) of this section.(v) Appropriate software documentation, including device hazard analysis, software requirements specification document, software design specification document, traceability analysis, system level test protocol, pass/fail criteria, testing results, and cybersecurity measures.
(2) Labeling must include the following:
(i) A detailed description of the patient population for which the device is indicated for use.
(ii) A detailed description of the device instructions for use, including the intended reading protocol and how the user should interpret the device output.
(iii) A detailed description of the intended user, and any user training materials or programs that address appropriate reading protocols for the device, to ensure that the end user is fully aware of how to interpret and apply the device output.
(iv) A detailed description of the device inputs and outputs.
(v) A detailed description of compatible imaging hardware and imaging protocols.
(vi) Warnings, precautions, and limitations must include situations in which the device may fail or may not operate at its expected performance level (
e.g., poor image quality or for certain subpopulations), as applicable.(vii) A detailed summary of the performance testing, including test methods, dataset characteristics, results, and a summary of sub-analyses on case distributions stratified by relevant confounders, such as anatomical characteristics, patient demographics and medical history, user experience, and imaging equipment.