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MULTIX Impact E is a radiographic system used in hospitals, clinics, and medical practices. MULTIX Impact E enables radiographic exposures of the whole body including: skull, chest, abdomen, and extremities and may be used on pediatric, adult and obese patients. Exposures may be taken with the patient sitting, standing, or in the prone position. MULTIX Impact E uses digital detectors for generating diagnostic images by converting X- rays into image signals. MULTIX Impact E is also designed to be used with conventional film/screen or Computed Radiography (CR) cassettes. MULTIX Impact E is not intended for mammography.

The MULTIX Impact E 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 detector) based on the predicate device, the MULTIX Impact E (VB10) (K220919). The following modifications have been made to the predicate device: 1) A new elevating table (option) 2) Upgraded software version to VB20 to support hardware modifications. The modified system will be branded as the MULTIX Impact E.

Please note, this document pertains to the MULTIX Impact E, which is a stationary X-ray system, and not an AI-powered diagnostic device. The submission focuses on demonstrating substantial equivalence to a predicate device, highlighting hardware modifications and software upgrades to support these changes. Therefore, the questions related to AI device performance, such as MRMC studies, ground truth establishment for AI, and training/test set sample sizes, are not applicable to this specific submission.

The acceptance criteria and performance are based on general safety and effectiveness of X-ray systems, primarily through compliance with recognized standards and non-clinical testing.

Here's a breakdown based on the provided document, addressing the applicable points:

Acceptance Criteria and Device Performance for MULTIX Impact E (X-Ray System)

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

The document does not present explicit "acceptance criteria" in a quantitative performance table for a diagnostic outcome (like sensitivity/specificity for a disease). Instead, the acceptance is based on demonstrating substantial equivalence to a predicate device through meeting regulatory standards, functional performance of components, and safety considerations. The performance is reported in terms of comparison to the predicate device for various attributes.

Here's an inferred "acceptance criteria" based on the document's structure, which is "Substantially Equivalent to Predicate Device (MULTIX Impact E VB10, K220919) and Reference Device (MULTIX Impact VA21, K213700) with no new safety risks":

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

This document describes a premarket notification (510(k)) for a conventional X-ray system, not an AI/ML-driven device. The "test set" here refers to non-clinical verification and validation testing of the device hardware and software against engineering specifications and regulatory standards.

• Sample Size: Not applicable in the context of patient imaging data for an AI algorithm. The testing involves system-level and component-level verification, functional tests, and safety tests performed on representative units of the device. The document does not specify a "sample size" of devices tested, but rather indicates that such testing was "successfully completed."
• Data Provenance: Not applicable in the context of patient data. The "data" comes from engineering tests and measurements on the device itself, not from clinical images.

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)

Not applicable. Ground truth as typically defined for medical image analysis (e.g., diagnosis of disease) is not established for this type of device submission. The "ground truth" for the performance of an X-ray system revolves around its physical characteristics, image quality parameters, and safety compliance, which are measured and evaluated by engineers and quality assurance personnel against established technical specifications and regulatory standards.

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

Not applicable. Adjudication methods are relevant for clinical studies where expert consensus is needed to establish ground truth for image interpretation. This submission is based on engineering and manufacturing verification and validation, not clinical image interpretation.

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

Not applicable. This is not an AI-powered device, and no MRMC study was performed or required for this type of submission.

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

Not applicable. This is not an AI algorithm.

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

The "ground truth" for this device's performance is established by engineering specifications, compliance with recognized industry standards (e.g., IEC, ISO, NEMA for X-ray systems), and adherence to manufacturing quality controls. This is demonstrated through physical measurements, electrical tests, safety circuit validation, software functionality tests, and image quality measurements, rather than clinical outcomes or diagnostic interpretations.

8. The sample size for the training set

Not applicable. This document does not describe an AI/ML device with a training set.

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

Not applicable. This document does not describe an AI/ML device with a training set.

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MULTIX Impact E is a radiographic system used in hospitals, clinics, and medical practices. MULTIX Impact E enables radiographic exposures of the whole body including: skull, chest, abdomen, and extremities and may be used on pediatric, adult and obese patients. Exposures may be taken with the patient sitting, standing, or in the prone position. MULTIX Impact E uses digital detectors for generating diagnostic images by converting X- rays into image signals. MULTIX Impact E is also designed to be used with conventional film/screen or Computed Radiography (CR) cassettes. MULTIX Impact E is not intended for mammography.

The MULTIX Impact E 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 detector) based on the predicate device, the MULTIX Impact (K203345). The following modifications have been made to the predicate device:

• A new X-ray Tube Assembly
• A new Collimator
• A new Generator
• Fixed patient table
• Tube-side control module (TCM)
• Upgraded software version to VB10 to support hardware modifications
  The modified system will be branded as the MULTIX Impact E.

This document describes modifications to an existing X-ray system, the MULTIX Impact E, and asserts its substantial equivalence to a predicate device, the MULTIX Impact (K203345).

Here's the breakdown of the acceptance criteria and study information provided:

1. Table of Acceptance Criteria and Reported Device Performance

The acceptance criteria here are implicitly equivalence in safety and effectiveness to the predicate device, despite modifications. The performance is assessed through verification and validation testing of the modified components to ensure they do not negatively impact safety and effectiveness and perform as intended.

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

The document extensively refers to "verification and validation testing" without specifying a distinct "test set" in terms of patient data or image data. The testing appears to be focused on hardware and software performance/functionality testing of the new components and the integrated system. There are no mentions of patient images or clinical data as a test set.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts

Not applicable. The provided document does not describe a clinical study involving experts establishing ground truth for a diagnostic test set. The testing described is technical verification and validation of the device's components and system functionality.

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

Not applicable. There is no mention of a diagnostic test set requiring adjudication.

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

Not applicable. This device is an X-ray system, not an AI-assisted diagnostic tool.

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

Not applicable. This is a hardware system, not a standalone algorithm.

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

Not applicable in the context of a diagnostic test set. For the verification and validation described, the "ground truth" would be the engineering specifications, performance standards (e.g., IEC, ANSI, NEMA), and safety requirements that the new/modified components and the integrated system are assessed against.

8. The sample size for the training set

Not applicable. This is not a machine learning model requiring a training set. The software mentioned is system control software, validated through standard software development lifecycle processes.

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

Not applicable.

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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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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.

The MULTIX Impact (VA20) 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 (K193089). The detectors for the subject device, MULTIX Impact (VA20), are the same as the detectors of the predicate device. The following modifications have been made to the predicate device:

• Modified tube stand (motorized) to support Ortho Function 1.
• Modified automatic collimator 2.
• 3. New bucky wall stand
• 4. Upgraded software version from VA11 to VA20 to support hardware modifications
• Modified patient table న్.
• 6. Modified touch user interface (TUI)
• Modified wireless remote control console (WRCC) with new control design 7.

The modified system will be branded as the MULTIX Impact.

The provided text is a 510(k) summary for the Siemens MULTIX Impact X-ray system. It details modifications made to a previously cleared predicate device (MULTIX Impact K193089). This document does not include information about specific acceptance criteria or the study that proves the device meets those criteria in the typical sense of a clinical performance study with defined metrics like sensitivity, specificity, or reader performance.

Instead, the submission focuses on demonstrating substantial equivalence to a predicate device, meaning the new device is as safe and effective as a legally marketed device. The "acceptance criteria" here are primarily conformity to recognized standards and successful completion of non-clinical verification and validation testing, ensuring the modified device performs as intended and introduces no new safety risks.

Here's a breakdown of the requested information based on the provided document:

1. Table of Acceptance Criteria and Reported Device Performance

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

This document describes a premarket notification for a modification to an existing X-ray system. The testing mentioned is primarily non-clinical verification and validation testing of hardware and software components against engineering specifications and regulatory standards. There is no mention of a "test set" in the context of clinical image data or patient studies from specific countries or whether they were retrospective/prospective. The assessment focuses on the performance of the system as a medical device, not a diagnostic algorithm.

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)

Not applicable. As this is a 510(k) submission for an X-ray system, the "ground truth" (in the sense of expert-labeled medical images for an AI algorithm) is not relevant to the type of testing performed. The documentation focuses on engineering and regulatory compliance, not diagnostic accuracy of an AI component.

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

Not applicable. No "test set" with expert adjudication is mentioned in the context of this 510(k) submission.

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

No. This 510(k) pertains to an X-ray imaging system itself, not an AI-powered diagnostic aide. Therefore, an MRMC study and effects on human reader performance are not described.

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

No. This document does not describe an AI algorithm or its standalone performance. The device is an X-ray system, which requires human operators.

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

Not applicable. The ground truth concept, as it relates to expert-labeled medical data for AI algorithms, is not part of this submission for an X-ray system itself. The "ground truth" for the engineering tests would be the design specifications and expected functional behavior of the components.

8. The sample size for the training set

Not applicable. This document does not describe an AI algorithm that undergoes training.

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

Not applicable. This document does not describe an AI algorithm or its training process.

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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 C Radiography X-ray system is a modular system of x-ray components (ceiling suspension with x-ray tube, bucky wall stand, bucky table, x-ray generator, and portable wireless and fixed detectors) based on the predicate device, the MULTIX Impact (K193089). The detectors for the subject device, MULTIX Impact C, are the same as the detectors of the predicate device. The following modifications have been made to the predicate device:

• 1. New ceiling suspension with motorized tube tilting support for ortho function and new ceiling suspension with manual tube tilting
• Modified automatic collimator 2.
• 3. New Bucky Wall Stand
• Upgraded software version from VA11 to VA20 to support hardware modifications 4.
• Modified patient table న్.
• Modified touch user interface (TUI) 6.
• Modified wireless remote-control console (WRCC) with new control design 7.

The new system will be branded as the MULTIX Impact C.

The provided text is a 510(k) summary for a medical device called MULTIX Impact C, which is a radiographic X-ray system. It describes the device, its intended use, and claims substantial equivalence to a predicate device (MULTIX Impact K193089).

However, the document focuses on demonstrating substantial equivalence through comparisons of technical characteristics and adherence to standards for the device itself (X-ray system), rather than providing details about an AI algorithm's performance or a clinical study that would involve acceptance criteria for such an algorithm. The MULTIX Impact C is designed to generate diagnostic images, but there is no mention of an AI component or software that interprets images or assists human readers in a diagnostic capacity.

Therefore, many of the requested details about acceptance criteria, study design, expert involvement, and AI performance cannot be extracted from this document, as it describes a hardware-focused X-ray system rather than an AI-driven image analysis tool.

Here's a breakdown based on the information available in the provided text:

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

No acceptance criteria and reported device performance directly related to an AI algorithm's diagnostic output (e.g., sensitivity, specificity, AUC) are provided. The tests described are non-clinical performance and safety tests for the X-ray system hardware and associated software.

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

Not applicable, as no AI-specific test set or data provenance is mentioned. The testing described is for the X-ray system's hardware and software functionality, not for AI diagnostic performance.

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)

Not applicable, as no ground truth for an AI diagnostic algorithm's performance is established or discussed. The document mentions "operators are healthcare professionals familiar with and responsible for the x-ray examinations to be performed," but this refers to the users of the X-ray system, not experts establishing ground truth for AI.

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

Not applicable, as no AI-specific test set or adjudication method is mentioned.

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

Not applicable. This document describes an X-ray imaging system, not an AI-assisted diagnostic tool that would typically undergo an MRMC study.

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

Not applicable. The device is a radiographic X-ray system; there is no standalone algorithm performance described.

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

Not applicable. Ground truth for an AI diagnostic algorithm is not discussed. The "ground truth" for the device's performance would be its conformity to engineering specifications and safety standards, as demonstrated by non-clinical testing.

8. The sample size for the training set

Not applicable, as there is no mention of an AI model or a training set. The software mentioned (VA20) is an upgraded version to support hardware modifications and new control features, not an AI for image analysis.

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

Not applicable, as there is no mention of an AI model or a training set.

Summary of what is described regarding the device's "acceptance criteria" and testing:

The document indicates that the device (MULTIX Impact C X-ray system) meets its "acceptance criteria" through:

• Non-clinical performance testing: "Non-clinical tests were conducted for the MULTIX Impact C during product development. The modifications described in this Premarket Notification are supported with verification and validation testing."
• Adherence to Standards: The device conforms to numerous international and national standards, including:
  • ES60601-1:2005/(R)2012 and A1:2012 (Medical electrical equipment – General requirements for basic safety and essential performance)
  • IEC 60601-1-3:2008+A1:2013 (Medical electrical equipment – General requirements for basic safety and essential performance – Collateral Standard: Radiation protection in diagnostic X-ray equipment)
  • IEC 60601-1-2:2014 (Medical electrical equipment – General requirements for basic safety and essential performance – Collateral Standard: Electromagnetic disturbances – Requirements and tests)
  • IEC 62366-1:2015 (Medical devices – Application of usability engineering to medical devices)
  • ISO 14971:2007 (Medical devices – Application of risk management to medical devices)
  • IEC 60601-1-6:2013 (Medical electrical equipment – General requirements for basic safety and essential performance – Collateral Standard: Usability)
  • IEC 62304:2015 (Medical device software – Software life cycle processes)
  • IEC 60601-2-28:2017 (Medical electrical equipment – Particular requirements for the basic safety and essential performance of X-ray tube assemblies for medical diagnosis)
  • IEC 60601-2-54:2018 (Medical electrical equipment – Particular requirements for the basic safety and essential performance of X-ray equipment for radiography and radioscopy)
  • NEMA PS 3.1-3.20 (2016) (DICOM standards)
  • ISO 10993-1:2009 (Biological evaluation of medical devices – Part 1: Evaluation and testing within a risk management process)
• Software Documentation and Testing: "Software Documentation for a Moderate Level of Concern software... The performance data demonstrates continued conformance with special controls for medical devices containing software. Non-clinical tests (integration and functional) were conducted on the MULTIX Impact C during product development." All software specifications "met the acceptance criteria."
• Risk Analysis: "The risk analysis was completed and risk controls were implemented to mitigate identified hazards. The test results support that all the software specifications have met the acceptance criteria."
• Substantial Equivalence: The primary "acceptance criterion" for clearance appears to be demonstrating substantial equivalence to a predicate device (MULTIX Impact K193089), which is achieved by showing similar indications for use, mechanical design, and that "non-clinical test data demonstrate that the MULTIX Impact C device performance is comparable to the predicate device."

In summary, the provided text is for the regulatory submission of an X-ray machine. It details the device's features, compliance with general safety and performance standards for X-ray equipment, and its similarity to a predicate device. It does not describe an AI diagnostic algorithm or a clinical study for such an algorithm's performance.

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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.

Ask a specific question about this device

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 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 detectors) similar to the predicate the Multix Fusion Max. This 510(k) submission describes modifications to the predicate device the Multix Fusion Max cleared via K162971. The following modifications have been made to the cleared predicate device and the new system will be branded the MULTIX Impact:

1. A new 43x35cm Wireless detector, 3543DR 1.
2. A new X-ray tube and a new generator
3. An optional 40 line grid with grid suppression algorithm
4. Wireless Remote Control Console
5. An optional All-in-one PC containing touch screen function
6. An optional positioning assistance camera
7. Upgrade software to VA10
8. Upgrade operator system from Windows XP to Windows 10

The Siemens MULTIX Impact X-ray system is a modification of the predicate device, the Multix Fusion Max (K162971). The submission focuses on demonstrating substantial equivalence rather than presenting an effectiveness study for a new clinical claim. Therefore, the information provided primarily concerns performance testing to confirm that the modifications do not negatively impact safety or effectiveness.

Here's an analysis based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

Since this is a submission demonstrating substantial equivalence to a predicate device for an X-ray system, the acceptance criteria are generally focused on demonstrating that various component modifications do not degrade performance below the predicate's established levels, and that the device meets relevant safety and performance standards. Formal "acceptance criteria" for a specific clinical task are not explicitly stated for the device as a whole in the way they would be for a diagnostic AI algorithm. Instead, the comparison is to the predicate and established standards.

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

The document describes modifications to an existing X-ray system and its components. The "test set" here refers to the actual physical device and its components undergoing verification and validation testing, rather than a dataset of medical images for a diagnostic algorithm.

• Sample Size for Test Set: Not applicable in the context of a dataset of cases. The testing was performed on the MULTIX Impact system itself and its components.
• Data Provenance: Not applicable in the clinical data sense. The testing is described as "Non-clinical tests... during product development" and "testing for verification and validation." This implies internal testing by the manufacturer.

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

Not applicable. This is not a study requiring expert clinical read-outs for ground truth. The "ground truth" for this submission is adherence to technical specifications, performance standards (like DQE, MTF), and safety requirements, which are evaluated by engineering and quality control processes.

4. Adjudication Method for the Test Set

Not applicable. There is no mention of clinical image adjudication for ground truth establishment. Technical performance and safety are verified through engineering tests.

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

No MRMC study was performed or is mentioned. This submission is for an X-ray system, not an AI-powered diagnostic device, and it focuses on demonstrating substantial equivalence of modified hardware and software components, not on comparative effectiveness with human readers.

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

Not applicable. The device is an X-ray system, which is inherently used with human operators. There is no "algorithm only" performance claim. The software modifications are part of the overall X-ray system's functionality.

7. The Type of Ground Truth Used

The "ground truth" for this submission relates to:

• Technical Specifications: Ensuring components meet their specified performance parameters (e.g., DQE, MTF for the detector, power output for the generator).
• Compliance with Standards: Verification that the device adheres to recognized national and international standards (IEC, ISO, NEMA).
• Safety and Effectiveness: Demonstration that modifications do not introduce new safety risks or degrade the overall effectiveness of the predicate device, as evaluated through risk analysis and functional testing.

8. The Sample Size for the Training Set

Not applicable. This is not an AI/machine learning device that requires a training set of data.

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

Not applicable, as there is no training set for an AI algorithm.

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