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MULTIX Impact 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. MULTIX Impact is not intended for mammography.

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

MULTIX Impact C is a radiographic system used in hospitals, clinics, and medical practices. MULTIX Impact C 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. MULTIX Impact C is not intended for mammography.

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

The MULTIX Impact (VA21) Radiography X-ray system is a floor mounted, modular system of x-ray components (x-ray tube, bucky wall stand, patient table, x-ray generator, portable wireless and fixed detectors) based on the predicate device, the MULTIX Impact (VA20, K203345).

The MULTIX Impact C (VA21) Radiography X-ray system is a ceiling suspended, modular system of x-ray components (x-ray tube, bucky wall stand, patient table, x-ray generator, portable wireless and fixed detectors) based on the predicate device, the MULTIX Impact C (VA20, K203340).

The following modifications have been made to the predicate devices:

• 1. Upgrade software version from VA20 to VA21 to support the new features: Auto TOD Measurement, Auto Thorax Collimation, Virtual Collimation, Hybrid Image Documentation (HID).
• 2. New mobile UI: Smart Remote Control (SRC).
• 3. New accessory: myExam 3D Camera, to support the new software features. The myExam 3D Camera has been cleared in YSIO X.pree (K201670).

The provided document is a 510(k) Summary for the Siemens MULTIX Impact and MULTIX Impact C radiographic systems. This document primarily focuses on demonstrating substantial equivalence to predicate devices based on modifications in software and accessories, rather than presenting a standalone clinical study for an AI-powered diagnostic device. Therefore, much of the requested information regarding acceptance criteria, study design for AI performance, and ground truth establishment is not present in this document.

However, based on the information provided, here's what can be extracted and what cannot:

General Statement:
The document does not describe an AI-powered diagnostic device that requires specific clinical performance acceptance criteria based on accuracy, sensitivity, or specificity. Instead, it describes a radiographic system (X-ray machine) with new software features (Auto TOD Measurement, Auto Thorax Collimation, Virtual Collimation, Hybrid Image Documentation (HID)), a new mobile UI (Smart Remote Control), and a new accessory (myExam 3D Camera), and demonstrates its substantial equivalence to previously cleared predicate devices. The "acceptance criteria" discussed are related to the safety and functional performance of the radiographic system itself, and its consistency with regulatory standards and previous versions.

Detailed Breakdown based on your Request:

1. A table of acceptance criteria and the reported device performance

The document does not provide a table of performance acceptance criteria in the context of an AI diagnostic study (e.g., specific thresholds for sensitivity, specificity, or AUC). The "acceptance criteria" it refers to are regulatory compliance and functional testing for the X-ray system.

2. Sample sized used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)

This information is not provided in the document. The document describes "non-clinical tests" (integration and functional) and "bench testing" to support the modifications and substantial equivalence. It does not mention a "test set" in the context of diagnostic image data for performance evaluation.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g. radiologist with 10 years of experience)

This information is not provided in the document. Ground truth establishment by experts is relevant for diagnostic AI performance evaluation, which is not the subject of this 510(k) summary.

4. Adjudication method (e.g. 2+1, 3+1, none) for the test set

This information is not provided in the document. Adjudication methods are relevant for establishing ground truth in diagnostic studies, which is not described.

5. If a multi reader multi case (MRMC) comparative effectiveness study was done, If so, what was the effect size of how much human readers improve with AI vs without AI assistance

There is no indication that an MRMC comparative effectiveness study was done. The device is an X-ray system, not an AI-assisted diagnostic software that directly affects human reader performance in interpreting images. The software updates relate to workflow and system operation (e.g., collimation, measurement, documentation) rather than diagnostic image interpretation assistance.

6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done

There is no indication that a standalone algorithm performance study was done. The device is an X-ray imaging system, not a standalone diagnostic algorithm.

7. The type of ground truth used (expert concensus, pathology, outcomes data, etc)

This information is not provided in the document, as it is not an AI diagnostic device requiring such ground truth for performance evaluation. The "ground truth" in this context would be related to the functional correctness and safety of the X-ray system and its new features.

8. The sample size for the training set

This information is not provided in the document. Training sets are relevant for machine learning models, which are not explicitly described as part of the "software version: VA21" updates in a way that suggests a need for specific training set sizes for diagnostic performance claims. The software changes appear to be for operational enhancements of the X-Ray system rather than a diagnostic AI.

9. How the ground truth for the training set was established

This information is not provided in the document, for the reasons mentioned above.

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