Search Results

The Mammomat Fusion system generates digital mammographic images that can be used for screening, diagnosis and stereotactic biopsies of the breast under supervision of medical professionals. The Mammomat Fusion system is intended to be used in the same clinical applications as traditional film-screen mammography systems. The Mammomat Fusion system may also be used for additional diagnostic workup of the breast.

Mammomat Fusion is a floor-mounted full field digital mammography system for screening and diagnostic procedures on standing, seated or recumbent patients.
The stereotactic biopsy accessory uses a computer and imaging performed in at least two planes to localize a target lesion (such as a tumor or micro calcifications in the breast) in three-dimensional space and guide the removal of tissue for examination by a pathologist under a microscope.
The biopsy is attached to the Mammomat Fusion examination stand and comprises a control panel for automatic motorized movement of the needle holder to the pre-set position. There are special biopsy compression plates as well as a face shield to protect the patient from movements of the swivel arm.
The display and control of the biopsy unit are performed via the workstation. Biopsy examinations can be executed with vertical and horizontal needle guidance. For stereo exposures the swivel arm of the Mammomat Fusion is tilted +/- 15 degrees to both sides.

Here's a breakdown of the acceptance criteria and study information for the Mammomat Fusion with Stereotactic Biopsy, based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

Note: The document primarily focuses on demonstrating substantial equivalence to predicate devices and adherence to voluntary standards. The "Targeting accuracy" bench test is the only specific performance criteria with an explicit numerical acceptance criterion and reported result.

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

• Test Set Sample Size: The document does not specify a "test set" in the context of clinical data or a large dataset of patient images. The performance evaluation discussed relates to a bench test, not a clinical study with a patient-derived test set.
• Data Provenance: Not applicable as the reported performance comes from bench testing of the device itself, not from retrospective or prospective patient data from specific countries.

3. Number of Experts Used to Establish Ground Truth for the Test Set and Qualifications of Those Experts

• Not applicable. The "ground truth" for the targeting accuracy bench test would be established by the physical measurements during the test, not by expert consensus on clinical data.

4. Adjudication Method for the Test Set

• Not applicable. The targeting accuracy bench test would have defined measurement protocols, not an adjudication method for conflicting interpretations.

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

• No mention of an MRMC comparative effectiveness study being performed. The document focuses on demonstrating substantial equivalence of the device's technical specifications and safety to predicate devices.

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

• Not applicable. The device is a full-field digital mammography system with a stereotactic biopsy attachment. Its performance is inherent to the physical system and its software controls, not a standalone algorithm that could be evaluated without human interaction (as a medical professional operates the device).

7. Type of Ground Truth Used

• For the "Targeting accuracy" bench test, the ground truth is based on physical measurement standards. The accuracy of the needle tip's position relative to a selected target point is determined by direct measurement, not by expert consensus, pathology, or outcomes data.

8. Sample Size for the Training Set

• Not applicable. This document describes a medical device (hardware and integral software), not a machine learning algorithm that requires a separate "training set" in the conventional sense of AI development. The software capabilities are validated through engineering tests and adherence to standards.

9. How Ground Truth for the Training Set Was Established

• Not applicable, as there is no mention of a traditional training set for a machine learning algorithm. The "training" for the device's functionality is inherent in its design, manufacturing, and calibration processes, validated through compliance with standards and bench tests.

Ask a specific question about this device