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The MULTIX Impact system is a radiographic system used in hospitals, clinics, and medical practices. MULTIX Impact enables radiographic exposures of the whole body including: skull, chest, abdomen, and extremities and may be used on pediatric, adult and bariatric patients. Exposures may be taken with the patient sitting, standing, or in the prone position. The MULTIX Impact system is not meant for mammography.

The MULTIX Impact uses digital detectors for generating diagnostic images by converting X-rays into image signals. The MULTIX Impact is also designed to be used with conventional film/screen or Computed Radiography (CR) cassettes.

The MULTIX Impact (VA11) Radiography X-ray system is a modular system of x-ray components (floor-mounted x-ray tube, bucky wall stand, bucky table, x-ray generator, portable wireless and fixed detectors) based on the predicate device, the MULTIX Impact (K182517). The following modifications have been made to the predicate device:

• 1. A new 43*43cm wireless detector, Mars1717VS manufactured by iRay
• A new 43*43cm fixed detector, Venu1717X manufactured by iRay 2.
• 3. A new Remote Interface used for patient examination management
• 4. Upgraded software version from VA10 to VA11 to support hardware modifications and Remote Interface.
• న. New Bucky Wall Stands
• 6. New patient table

The new system will be branded as the MULTIX Impact.

This is an FDA 510(k) summary for the MULTIX Impact (VA11) radiography x-ray system, which is a modification of an existing device (MULTIX Impact, K182517). The submission aims to demonstrate substantial equivalence to the predicate device, rather than proving the device meets a new set of performance acceptance criteria through a comparative clinical study.

Therefore, many of the typical acceptance criteria and study details for novel AI medical devices are not explicitly stated in this document. The focus is on demonstrating that the modifications made do not negatively impact safety and effectiveness and that the new device performs comparably to the predicate.

However, I can extract the relevant information regarding performance aspects that were tested and compared.

Here's a breakdown based on the provided text:

1. Table of "Acceptance Criteria" and Reported Device Performance

Since this is a substantial equivalence submission for modifications to an existing device, there aren't new specific quantifiable acceptance criteria in the typical sense for a brand new AI device. Instead, the "acceptance criteria" are implied to be at least equivalent or improved performance compared to the predicate device in key technical specifications.

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

This document describes a 510(k) submission for modifications to a hardware device (X-ray system), not an AI software. The testing mentioned is primarily non-clinical performance testing (integration, functional, regulatory compliance, risk analysis) rather than clinical studies with patient data.

• Test Set Sample Size: Not applicable in the context of patient image data for an AI algorithm. The testing involves hardware components and integrated system performance. No specific "test set" of patient images is referenced for algorithm evaluation.
• Data Provenance: Not applicable for an AI algorithm. The testing would have occurred internally at Siemens manufacturing and R&D facilities.

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

• This is not applicable as the submission focuses on hardware and software modifications of an X-ray system, not an AI algorithm that requires expert-established ground truth for performance evaluation.

4. Adjudication Method for the Test Set

• Not applicable for this type of submission.

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

• Not performed and not applicable. This is not an AI-assisted diagnostic device undergoing a comparative effectiveness study with human readers.

6. Standalone (i.e., algorithm only without human-in-the-loop performance) Study

• Not performed and not applicable. This is not an AI algorithm but a hardware X-ray system with embedded software.

7. Type of Ground Truth Used

• Not applicable for an AI algorithm. The "ground truth" in this context refers to engineering specifications, regulatory standards compliance, and functional performance benchmarks for hardware and system software.

8. Sample Size for the Training Set

• Not applicable as this is not an AI algorithm that requires a training set of patient data.

9. How the Ground Truth for the Training Set was Established

• Not applicable as there is no mention of a training set or AI algorithm in the provided document.

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The DigiX FDX radiographic systems are used in hospitals, clinics and medical practices. DigiX FDX enables radiographic exposure of the whole body including: Skull, chest, abdomen, and extremities and may be used on pediatric, adult and bariatic patients. It can also be used for intravenous, small interventions (like biopsy, punctures, etc.) and emergency (trauma critical ill) applications. Exposure may be taken with the Patient's sitting, standing, or in the prone/ supine position.

The DigiX FDX System is not meant for mammography.

The DigiX FDX uses an integrated or portable or fixed or wi-fi digital detector for generating diagnostic images by converting X-Ray into electronics signals. DigiX FDX is also designed to be used with conventional film/screen or Computed Radiography (CR) Cassettes.

The DigiX FDX system is a diagnostic X-Ray system intended for general purpose radiographic imaging of the human body. It is not intended for mammographic imaqing.

The DigiX FDX system is comprised of a combination of devices that include a ceiling mounted X-Ray tube suspension, vertical Bucky stand, fixed or mobile patient Bucky table, X-Ray generator, X-Ray tube, beam limiting device, and a solid-state image receptor.

The DigiX FDX systems are not intended to be operated with any other cleared devices, or to be integrated with other software/hardware devices via direct or indirect connections.

Here's a breakdown of the acceptance criteria and study information based on the provided document:

1. Table of Acceptance Criteria and Reported Device Performance

The document focuses on demonstrating substantial equivalence to predicate devices rather than defining specific quantitative performance metrics as "acceptance criteria" for a new AI/software feature in the way clinical performance studies usually do. Instead, the "acceptance criteria" are implied to be that the modifications do not negatively impact safety or effectiveness, and that the device performs comparably to the predicate devices and meets relevant safety standards.

For the new features explicitly mentioned (Automatic Stitching and Dual Energy Subtraction), the document states they add functionality without affecting patient safety or effectiveness. For the other components, the criteria are often "Same" or "Similar functionality with same imaging results" or "doesn't affect the safety or effectiveness."

To represent this in a table, we'll extract performance comparisons from the "Functional and Specification Differences" table (Table 4) and the "Justification for Differences" (Table 5).

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

The document states:

• "Non-clinical testing included verification and validation testing, image evaluation, testing, and safety testing."
• "Performance testing included functional testing of all motions of the system(s) with respect to the design specifications. Image performance testing was conducted and results included in the submission."
• "Clinical testing is not applicable due to the fact that no new clinical applications were introduced to the system."

The document does NOT specify a sample size for any test set (clinical or otherwise) in terms of patient data or images used for validation of the radiographic system itself, nor does it mention data provenance (country of origin, retrospective/prospective). It primarily relies on hardware and software equivalence, and compliance with industry standards.

3. Number of Experts Used to Establish Ground Truth and Qualifications

Not applicable. The document explicitly states "Clinical testing is not applicable." Therefore, there was no clinical study conducted that would necessitate expert readers to establish ground truth for a test set. The evaluation focuses on technical performance and equivalence to predicate devices.

4. Adjudication Method for the Test Set

Not applicable. As no clinical test set requiring expert adjudication was performed, no adjudication method is mentioned.

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

No MRMC comparative effectiveness study was done. The document states "Clinical testing is not applicable due to the fact that no new clinical applications were introduced to the system." Therefore, there is no information on how much human readers improve with or without AI assistance. The new software features (Automatic Stitching, Dual Energy Subtraction) are presented as additional functionalities that don't affect safety or effectiveness, not as AI-assisted diagnostic tools requiring a comparative effectiveness study.

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

Not applicable. The device is a radiographic system, not an AI algorithm intended for standalone diagnostic performance. While it includes new software features (Automatic Stitching, Dual Energy Subtraction), these are integrated functionalities of the imaging system and not described as standalone diagnostic algorithms requiring independent performance evaluation without human interaction.

7. Type of Ground Truth Used

The "ground truth" for the evaluation is primarily based on:

• Compliance with technical specifications and design requirements: Functional testing, image performance testing.
• Adherence to safety and performance standards: IEC 60601 series, EN ISO 14971, IEC 62366-1, IEC 62304, 21 CFR 1020.30, 21 CFR 1020.31.
• Substantial equivalence to predicate devices: Demonstration that the new device has the same intended use, fundamental technological characteristics, and that any differences do not raise new questions of safety or effectiveness.
• Software documentation assessment: For the software components, including the new features, documentation was provided for a "Moderate Level of Concern" software as per FDA guidance.

No clinical ground truth (e.g., expert consensus, pathology, outcomes data) was used or required given the nature of this submission.

8. Sample Size for the Training Set

Not applicable. The document describes an X-ray imaging system, not an AI/ML device that requires a training set of data. The new software features (Automatic Stitching, Dual Energy Subtraction) are described as functionalities, not adaptive algorithms that learn from data.

9. How the Ground Truth for the Training Set Was Established

Not applicable. As there was no training set, there was no ground truth to establish for it.

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