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The device is designed to perform general radiography x-ray examinations on all pediatric and all adult patients, in all patient treatment areas.

Treatment areas are defined as professional health care facility environments where operators with medical training are continually present during patients' examinations.

The !M1 mobile X-ray system is a diagnostic mobile x-ray system utilizing digital radiography (DR) technology. The device consists of a self-contained x-ray generator, image receptor(s), imaging display and software for acquiring medical diagnostic images both inside and outside of a standard stationary x-ray room. The !M1 system incorporates a flat-panel detector(s) that can be used wirelessly for exams such as in-bed projections. The system can also be used to expose CR phosphor screens or film.

The provided FDA 510(k) clearance letter and documentation describe a submission for an updated mobile X-ray system, !M1. However, the document primarily focuses on demonstrating substantial equivalence to a predicate device rather than presenting a detailed study proving the device meets specific performance acceptance criteria for an AI/algorithm-based medical device.

The clearance is for a mobile X-ray system and its components (generators, X-ray tubes, collimators, and new lines of digital detectors and associated imaging software), not an AI algorithm for diagnostic interpretation that would typically have specific performance metrics like sensitivity, specificity, or AUC against a ground truth.

Therefore, many of the requested items related to AI/algorithm performance (e.g., sample size for test set, number of experts, adjudication method, MRMC studies, standalone performance, ground truth for training/test sets, training set sample size) are not applicable or not provided in this type of 510(k) submission for an imaging device itself. The "performance" being improved here refers to the image acquisition capabilities (DQE, MTF, pixel size, kV, mAs, kW) of the new hardware components, not the output of a diagnostic AI algorithm.

Based on the provided document, here's what can be extracted:

Acceptance Criteria and Reported Device Performance

The "acceptance criteria" for this device are largely implied by demonstrating that the new components (collimator, generator, X-ray tube, software, and detectors) either maintain or improve upon the technical specifications and image quality parameters of the predicate device, while maintaining the same Indications for Use. The "study" proving this largely relies on non-clinical testing and compliance with relevant performance standards for X-ray equipment and image quality.

Study Details (As applicable to an X-ray System 510(k))

1. Sample size used for the test set and the data provenance:
   This 510(k) is for an X-ray imaging system and its components, not a diagnostic AI algorithm. Therefore, there isn't a "test set" of patient cases in the context of an AI study.

   • Test Data: The testing involves non-clinical verification and validation of hardware performance (e.g., kV, mAs, kW measurements, image quality metrics like DQE, MTF, and spatial resolution using phantoms or test targets) and software functionality.
   • Provenance: Not explicitly stated, implied to be internal testing by the manufacturers (Solutions for tomorrow AB, Konica Minolta, Canon Inc., Vieworks Co., Ltd) in their development and quality assurance processes. This is typical for device component integration.

2. Number of experts used to establish the ground truth for the test set and the qualifications of those experts:
   Not applicable (N/A). Ground truth in this context refers to the measured physical properties and image quality metrics of the X-ray system components, not clinical diagnoses made by experts. For example, DQE and MTF are measured using standardized methods and phantoms, not adjudicated by clinical experts.

3. Adjudication method (e.g., 2+1, 3+1, none) for the test set:
   N/A. This is relevant for clinical studies where human readers establish ground truth for diagnostic AI. Here, performance is verified through engineering and physics measurements.

4. 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:
   N/A. This is not an AI diagnostic assistance device. It's an X-ray imaging system.

5. If a standalone (i.e. algorithm only without human-in-the loop performance) was done:
   N/A. Not an AI diagnostic algorithm.

6. The type of ground truth used (expert consensus, pathology, outcomes data, etc.):
   For the components of the X-ray system, the "ground truth" consists of physical measurements and standardized tests of parameters such as:

   • Output of the X-ray generator (kV, mAs, kW).
   • Image quality metrics of the detectors (DQE, MTF, pixel size, spatial resolution) measured using phantoms and established procedures (e.g., IEC standards).
   • Functionality of the collimator.
   • Compliance with electrical and radiation safety standards (EN 60601 series).

7. The sample size for the training set:
   N/A. The document does not describe an AI algorithm that requires a "training set" of patient data in the conventional sense. The "software" mentioned is operational imaging software, not a deep learning model for image interpretation.

8. How the ground truth for the training set was established:
   N/A. See point 7.

In summary, the provided document details a 510(k) submission for an updated mobile X-ray system and its components. The "acceptance criteria" and "proof" relate to meeting the performance specifications for hardware and integrated software functionality, and compliance with medical device standards, rather than the diagnostic performance of an AI algorithm.

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The device is designed to perform general radiography x-ray examinations on all pediatric and all adult patients, in all patient treatment areas.

Treatment areas are defined as professional health care facility environments where operators with medical training are continually present during patients' examinations.

The ModelOne mobile X-ray system is a diagnostic mobile x-ray system utilizing digital radiography technology. The device consists of a self-contained x-ray generator, image receptor(s), imaging display and software for acquiring medical diagnostic images both inside and outside of a standard stationary x-ray room. The ModelOne system incorporates a flat-panel(s) detector that can be used wirelessly for exams as in-bed projections. The system is intended to be marketed with two options with flat-panel digital images from Canon and Konica Minolta.

Based on the provided text, the device is an X-ray system, and the "study" described is a non-clinical performance evaluation rather than a traditional clinical study with human patients. The information provided is for regulatory clearance (510(k) summary) rather than a comprehensive research paper on AI performance.

Therefore, many of the typical acceptance criteria and study details for an AI/ML device (e.g., ground truth establishment for a test set, MRMC studies, standalone AI performance) are not applicable or not provided in this document. The device is a mobile X-ray system, not an AI-powered diagnostic tool. The focus is on the safety and performance of the hardware and integrated previously-cleared digital imagers, demonstrating substantial equivalence to a predicate device.

Here's an attempt to answer the questions based only on the provided text, noting where information is absent or not relevant for this type of device:

Acceptance Criteria and Device Performance (Non-AI X-ray System)

The document describes performance tests for a mobile X-ray system, NOT an AI/ML device. The acceptance criteria are implicit in the performance tests verifying the functionality and safety of the hardware. The "reported device performance" refers to the successful completion of these non-clinical tests.

1. Table of Acceptance Criteria and Reported Device Performance

2. Sample Size for the Test Set and Data Provenance

• Sample Size for Test Set: Not explicitly stated in terms of number of "cases" or "patients" as this is a device performance test, not a clinical study on diagnostic accuracy. The tests involve the device itself and its components.
• Data Provenance: The tests are "non-clinical testing" and performed on the device hardware. Usability tests involved "professional personal" at a "hospital," but this is for evaluating the device's operation in a real-world setting, not an evaluation of diagnostic output. It's a "retrospective" view of testing results provided to the FDA.

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

• Not Applicable / Not Provided. This document describes a mobile X-ray system, not an AI/ML diagnostic algorithm that requires expert-established ground truth for image interpretation. The "ground truth" here is the device's functional performance against its design specifications and compared to a predicate, not clinical diagnostic accuracy.

4. Adjudication Method for the Test Set

• Not Applicable / None. No adjudication method is mentioned as this is not a study assessing human or AI diagnostic performance based on image interpretation.

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

• No. "No clinical testing was performed on the subject device." Therefore, no MRMC study was conducted to evaluate human readers with or without AI assistance.

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

• Not Applicable / No. The device itself is an X-ray imaging system. It produces images, but the document does not describe a new AI algorithm for interpreting those images. The "software" mentioned is for acquiring and displaying images, and its risk is classified. The post-processing is defined by protocols from previously cleared Canon and Konica Minolta image software.

7. Type of Ground Truth Used

• Functional Performance Specifications and Predicate Comparison. The "ground truth" for this regulatory submission is that the device functions according to its design specifications (e.g., battery life, driving distance, exposure time) and performs "as well as or better than the predicate device" in non-clinical settings.

8. Sample Size for the Training Set

• Not Applicable. This is not an AI/ML algorithm that requires a training set of data.

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

• Not Applicable. As above, no AI/ML training set is mentioned or implied.

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