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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.

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The Mammomat Fusion system generates digital mammographic images that can be used for screening and diagnosis 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 system consists of an examination stand with integrated microprocessor-controlled high-voltage generator as well as an optional radiation shield with a height-adjustable control desk with an integrated Acquisition Workstation (AWS). The moveable swivel arm on the examination stand contains the X-ray tube on the top end and the object table with the detector on the bottom end. The detector is a full field digital mammoqraphy detector.

The provided text describes the Siemens Mammomat Fusion Full Field Digital Mammography System and its substantial equivalence to predicate devices. However, the document does not contain specific acceptance criteria, reported device performance metrics, or the details of a study designed to explicitly prove the device meets said acceptance criteria. Instead, it outlines the basis for substantial equivalence for FDA 510(k) clearance.

Here's an analysis of what can be extracted and what is missing based on your request:

Missing Information:

• A table of explicit acceptance criteria and specific reported device performance metrics. The document states "All test results were satisfactory" but does not quantify them.
• Sample size used for a test set in the context of proving acceptance criteria.
• Data provenance (e.g., country of origin, retrospective/prospective) for a test set.
• Number of experts and their qualifications used to establish ground truth for a test set.
• Adjudication method for a test set.
• Whether a multi-reader multi-case (MRMC) comparative effectiveness study was done, or any effect size for human readers improving with AI assistance. (This device is a mammography system, not an AI CAD system, so such a study would not be expected in this context).
• Details of a standalone (algorithm only) performance study. (Again, this is a hardware system, not an AI algorithm).
• The type of ground truth used (pathology, outcomes data, etc.) for any specific performance study related to acceptance criteria.
• Sample size for a training set. (This pertains to AI/machine learning, which is not the primary focus of this submission for a hardware device).
• How ground truth for a training set was established.

Information that can be inferred or is directly stated (though not in the format you requested for acceptance criteria):

1. Acceptance Criteria and Reported Device Performance (Inferred/General)

The document primarily relies on "substantial equivalence" to predicate devices and compliance with recognized standards.

2. Sample Size for Test Set and Data Provenance:

• Not specified. The document mentions "Clinical and Non-clinical testing was conducted in accordance with the 'Guidance for industry and FDA Staff: Class II Special Control Guidance Document: Full-Field digital Mammography System,' (issued on March 27, 2012) and 'Guidance for the Submission of 510(k)'s for Solid State X-ray Imaging Devices' (issued August 6, 1999)." These guidances would outline typical testing requirements, but the specifics for this particular submission's test set (e.g., number of images, patients, type of breast density, etc.) are not provided. The data provenance is also not mentioned.

3. Number of Experts and Qualifications for Ground Truth:

• Not specified. No mention of expert consensus or qualifications for a test set's ground truth. The submission focuses on device performance data compared to predicates and adherence to technical standards, not diagnostic accuracy studies with expert review.

4. Adjudication Method:

• Not applicable/Not specified. Not relevant to the type of device performance testing described (primarily technical and safety compliance).

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

• No. This type of study is typically performed for Computer-Aided Detection (CAD) or AI systems where human reader performance is a direct outcome measure. The Mammomat Fusion is a hardware imaging system, and the submission does not describe an MRMC study comparing human readers with and without AI assistance from this device.

6. Standalone Performance Study:

• Yes (for technical parameters). The document claims "The subject device has the same or better performance data as the secondary predicate" and that "The performance of the detector has been compared to the secondary predicate the GE Senographe DS." This implies standalone technical performance testing (e.g., image quality metrics like spatial resolution, contrast-to-noise ratio, detective quantum efficiency related to the detector technology change from Selenium to Cesium Iodide TFT). However, the specific metrics and quantitative results are not provided in this summary.

7. Type of Ground Truth Used:

• Technical Benchmarks / Predicate Device Performance. For the technical performance aspects, the "ground truth" seems to be established through measurements against recognized technical standards (e.g., IEC standards for imaging performance, safety) and direct comparison to the physical detector characteristics of the GE Senographe DS predicate device. It is not an assessment of diagnostic accuracy against pathology or outcomes data.

8. Sample Size for Training Set:

• Not applicable/Not specified. This pertains to AI/machine learning model training. The Mammomat Fusion is a digital mammography system (hardware and associated control/processing software), not an AI algorithm requiring a training set in the typical sense.

9. How Ground Truth for Training Set Was Established:

• Not applicable/Not specified. See point 8.

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