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510(k) Data Aggregation
(138 days)
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
| Acceptance Criteria Category | Specific Test/Evaluation | Reported Device Performance |
|---|---|---|
| Usability | Acceptance test on customer site | "Performance tests confirm that the device is as effective and performs as well as or better than the predicate device." (Implies meeting usability expectations) |
| Performance test at hospital by professional personnel | "Performance tests confirm that the device is as effective and performs as well as or better than the predicate device." (Implies meeting usability expectations) | |
| Battery Performance | Beginning of life/end of life test | "Performance tests confirm that the device is as effective and performs as well as or better than the predicate device." (Implies battery life meets operational needs) |
| Mobility | Driving distance test (full to empty battery) | "The driving distance test was performed to verify maximum distance of driving from full to empty battery." (Implies meeting or exceeding required driving distance for mobile operation) |
| Generator Performance | Comparison of exposure time with competitors | "The aim of generator performance test was to compare the time of exposure of !M1 and its competitors." (Implies competitive or equivalent exposure times, contributing to "performs as well as or better than the predicate device.") |
| System Integration | Integration test with previously cleared flat-panel imagers | "Integration test was performed on the previously cleared flat-panel digital imagers in order to demonstrate that all components of the device function in a reproductive way according to the design specifications." (Confirms successful integration and functional operation of the complete system) |
| Software Risk | Software risk classification | "The software risk is classified as moderate level of concern device according to the Guidance for the Content of Premarket Submissions for Software Contained in Medical Devices." (Acceptance is compliance with software risk guidelines, not a performance metric in this context, but a regulatory requirement met) |
| Safety | Overall safety assessment | "Technological differences do not raise questions of safety and the device is as safe as legally marketed DRX-Revolution by Carestream." (Overall safety acceptance based on non-clinical tests and comparison to predicate) |
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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