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

The DRX-Revolution Mobile X-ray System is a mobile diagnostic x-ray system that utilizes digital technology for bedside or portable exams. Key components of the system are the x-ray generator, a tube head assembly (includes the x-ray tube and collimator) that allows for multiple axes of movement, a maneuverable drive system, touchscreen user interface(s) for user input. The system is designed with installable software for acquiring and processing medical diagnostic images outside of a standard stationary X-ray room. It is a mobile diagnostic system intended to generate and control X-rays for examination of various anatomical regions.

The provided text describes a 510(k) premarket notification for the DRX-Revolution Mobile X-ray System, which includes changes such as the addition of Smart Noise Cancellation (SNC) functionality and compatibility with a new detector (Lux 35). The study focuses on demonstrating the substantial equivalence of the modified device to a previously cleared predicate device (DRX-Revolution Mobile X-ray System, K191025).

Here's an analysis of the acceptance criteria and the study that proves the device meets them, based on the provided information:

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

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The device is indicated for use in obtaining diagnostic images to aid the physician with diagnosis. The system can be used to perform radiographic imaging of various portions of the human body, including the skull, spinal column, extremities, chest, abdomen and other body parts. The device is not indicated for use in mammography

The DRX-Compass System is a general purpose x-ray system used for acquiring radiographic images of various portions of the human body. The system consists of a combination of components including various models of high voltage x-ray generators, control panels or workstation computers, various models of patient support tables, wall-mounted image receptors/detectors for upright imaging, various models of tube support devices, x-ray tube, and collimator (beam-limiting device). The DRX-Compass can be used with digital radiography (DR) and computed radiography (CR) receptors. Smart Features are added to the DRX-Compass system to provide remote capabilities for existing functions of the DRX-Compass system. These remote capabilities simplify exam set up and improve workflow for the operator while preparing for the patient exposure. The "smart features", described below, are designed to reduce the technologist's manual tasks and to speed up workflow for existing features of the system. These improvements are referred to as "smart features" in the product documentation. Implementation of these "smart features" does not change the intended use of the system.

The provided text does not contain detailed information about specific acceptance criteria and a study that comprehensively proves the device meets those criteria for the DRX-Compass system. The document is a 510(k) summary for the FDA, which focuses on demonstrating substantial equivalence to a predicate device rather than a comprehensive efficacy study for new features.

However, based on the information provided, I can extract the relevant details that are present and explain why some requested information is not available in this document.

Here's a breakdown of what can be inferred and what is missing:

1. Table of Acceptance Criteria and Reported Device Performance

The document mentions that "Predefined acceptance criteria were met and demonstrated that the device is as safe, as effective, and performs as well as or better than the predicate device." However, the specific acceptance criteria themselves (e.g., specific thresholds for DQE/MTF, or performance metrics for the "smart features") are not explicitly detailed in this 510(k) summary. Similarly, the reported device performance values against those specific criteria are also not provided.

The closest information related to performance is:

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

This information is not provided in the 510(k) summary. The document states "Non-clinical testing such as standards testing are the same as that of the predicate. The verification and validation testing of the modified device demonstrates that the modified device performs as well as the predicate and is substantially equivalent." without detailing the specific sample sizes or data provenance for these tests. For imaging performance, it mentions DQE/MTF data for detectors, but not the sample size of images or patients used for performance evaluation of the overall system or its new "smart features."

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

This information is not provided in the 510(k) summary. The document focuses on technical verification and validation, and comparison to a predicate device, rather than a clinical study requiring expert consensus on ground truth.

4. Adjudication Method for the Test Set

This information is not provided in the 510(k) summary. Given the absence of specific clinical study details or expert ground truth establishment, no adjudication method would be mentioned.

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

A MRMC comparative effectiveness study is not mentioned in this document. The submission's focus is on demonstrating substantial equivalence through technical testing and compliance with recognized standards, particularly for the "smart features" which are described as workflow enhancements rather than diagnostic AI tools requiring reader performance studies. There is no mention of AI assistance for human readers or associated effect sizes.

6. Standalone (Algorithm Only) Performance Study

A standalone performance study of an algorithm without human-in-the-loop is not explicitly mentioned in this document. The "smart features" are described as functionalities to assist the operator, implying human-in-the-loop operation, rather than a standalone diagnostic algorithm. The document mentions "Flat panel detector DQE/MTF data shows that the additional detectors supported by the modified device (DRX-Compass) are equivalent in image quality to that of the DRX Plus detectors cleared with the predicate," which is a technical performance metric for the detector component, not an algorithm's diagnostic performance.

7. Type of Ground Truth Used

The type of ground truth used for any performance evaluation is not explicitly stated. For the detector performance, DQE/MTF data refers to physical image quality metrics rather than a diagnostic ground truth (like pathology or clinical outcomes). For the "smart features," their evaluation appears to be based on functional verification and validation of their workflow enhancement capabilities, rather than comparison to a ground truth for diagnostic accuracy.

8. Sample Size for the Training Set

This information is not provided in the 510(k) summary. The document does not describe the use of machine learning algorithms that would typically require a training set. The "smart features" appear to be rule-based or real-time processing functionalities rather than learning algorithms.

9. How Ground Truth for the Training Set Was Established

Since there is no mention of a training set or machine learning, details on establishing its ground truth are not provided.

In summary, the 510(k) submission for the DRX-Compass focuses on demonstrating substantial equivalence to a predicate device by:

• Ensuring the modified device's indications for use are identical.
• Confirming compliance with recognized electrical safety and performance standards (AAMI ES60601-1, IEC 60601-1-6, IEC 60601-1-3, IEC 60601-2-54, IEC 62366).
• Applying risk management (ISO 14971) to ensure no new risks are introduced.
• Showing that new components (e.g., additional detectors) maintain equivalent image quality (e.g., DQE/MTF data).
• Asserting that new "smart features" improve workflow without changing the device's intended use or safety profile.

The document does not provide the kind of detailed clinical study data often found for AI/ML-based diagnostic devices, including specific acceptance criteria values, sample sizes for test or training sets, expert qualifications, or adjudication methods, as these may not be typically required for modifications to a stationary X-ray system primarily focused on workflow enhancements and component upgrades.

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The device is designed to perform radiographic x-ray examinations on all pediatric and adult patient treatment areas.

The DRX-Rise Mobile X-ray System is a diagnostic mobile X-ray system utilizing digital radiography technology. The DRX-Rise consists of a self-contained X-ray generator, image receptor(s), imaging display and software for acquiring medical diagnostic images outside of a standard stationary X-ray room. These components are mounted on a motorized cart that is battery powered to enable the device to be driven from location to location by user interaction. The DRX-Rise system incorporates a flat-panel detector that can be used wirelessly for exams such as in-bed chest projections. The device acquires images using Carestream's clinical acquisition software platform (ImageView) and digital flat panel detectors. ImageView is considered software that is of Moderate Level of Concern and not intended for manipulation of medical images. The DRX-Rise Mobile X-ray System is designed for digital radiography (DR) with Carestream detectors.

The provided document is a 510(k) premarket notification for the DRX-Rise Mobile X-ray System, asserting its substantial equivalence to a predicate device (DRX-Revolution Mobile X-ray System, K191025). The document does not describe a study involving acceptance criteria for an AI/CADe device's performance when assisting human readers or evaluating standalone AI performance.

Instead, the document focuses on demonstrating that the DRX-Rise Mobile X-ray System itself, as a physical medical device, is substantially equivalent to an already cleared device. This is achieved through comparisons of technological characteristics and compliance with consensus standards.

Therefore, I cannot provide the requested information regarding acceptance criteria and studies for an AI/CADe device's performance (points 2-9) because the submission does not pertain to such a device or study.

Here's a breakdown of what can be extracted from the provided text, related to the device itself:

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

The document doesn't present acceptance criteria in the typical "performance target" vs. "achieved performance" format for an AI/CADe. Instead, it compares the modified device's specifications to the predicate device's specifications, arguing that any differences do not impact safety or performance.

2. Sample sized used for the test set and the data provenance: Not applicable. This submission concerns a hardware medical device, not a performance study on a test set of images. The "test set" in this context refers to the device itself being tested against its specifications and existing standards.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts: Not applicable. Ground truth for image interpretation by experts is not relevant to this submission.

4. Adjudication method (e.g. 2+1, 3+1, none) for the test set: Not applicable.

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. No AI assistance mentioned.

6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done: Not applicable.

7. The type of ground truth used (expert consensus, pathology, outcomes data, etc): Not applicable. The "ground truth" for this device's acceptance is its compliance with recognized consensus standards and its functional equivalence to a predicate device.

8. The sample size for the training set: Not applicable. There is no mention of a training set as this is not an AI/ML device submission.

9. How the ground truth for the training set was established: Not applicable.

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The software performs digital enhancement of a radiographic image generated by an x-ray device. The software can be used to process adult and pediatric x-ray images. This excludes mammography applications.

Eclipse software runs inside the ImageView product application software (also namely console software). The Eclipse image processing software II with Smart Noise Cancellation is similar to the predicate Eclipse image processing software (K180809). Eclipse with Smart Noise Cancellation is an optional feature that enhances projection radiography acquisitions captured from digital radiography imaging receptors (Computed Radiography (CR) and Direct Radiography (DR). The modified software is considered an extension of the software (it is not stand alone and is to be used only with the predicate device supports the Carestream DRX family of detectors, this includes all CR and DR detectors. The primary difference between the predicate and the subject device is the addition of a Smart Noise Cancellation module. The Smart Noise Cancellation module consists of a Convolutional Network (CNN) trained using clinical images with added simulated noise to represent reduced signal-to-noise acquisitions. Eclipse with Smart Noise Cancellation (modified device) incorporates enhanced noise reduction prior to executing Eclipse II image processing software.

Here's a breakdown of the acceptance criteria and the study proving the device meets them, based on the provided text:

Based on the provided text, the device Eclipse II with Smart Noise Cancellation is considered substantially equivalent to its predicate Eclipse II (K180809) due to modifications primarily centered around an enhanced noise reduction feature. The acceptance criteria and the study that proves the device meets these criteria are inferred from the demonstrated equivalence to the predicate device and the evaluation of the new Smart Noise Cancellation module.

1. Table of Acceptance Criteria and Reported Device Performance

The acceptance criteria are implicitly tied to the performance of the predicate device and the new feature's ability to maintain or improve upon key image quality attributes without introducing new safety or effectiveness concerns.

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

• Sample Size for Test Set: Not explicitly stated. The text mentions "a range of exams, detector types and exposure levels" for the clinical evaluation, and "clinical images with added simulated noise" for the CNN training.
• Data Provenance: Not explicitly stated. The text mentions "clinical images," implying real-world patient data, but does not specify the country of origin or whether it was retrospective or prospective.

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

• Number of Experts: Not explicitly stated. The text mentions a "clinical evaluation was performed by board certified radiologists." It does not specify the number involved.
• Qualifications of Experts: "Board certified radiologists." No specific years of experience are provided.

4. Adjudication Method for the Test Set

• Adjudication Method: Not explicitly stated. The text mentions images were evaluated using a "5-point visual difference scale (-2 to +2) tied to diagnostic confidence" and a "4-point RadLex scale" for overall diagnostic capability. It does not describe a method for resolving discrepancies among multiple readers, such as 2+1 or 3+1.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was done

• MRMC Comparative Effectiveness Study: Yes, a clinical evaluation was performed by board-certified radiologists comparing the investigational software to the predicate software. While it doesn't explicitly use the term "MRMC," the description of a clinical evaluation by multiple radiologists comparing two versions of software suggests this type of study was conducted.
• Effect Size of Human Readers Improvement with AI vs. without AI Assistance: The text states, "The statistical test results and graphical summaries demonstrate that the investigational software delivers diagnostic quality images that exceed the quality of the predicate software over a range of exams, detector types and exposure levels." This indicates an improvement in diagnostic image quality with the new software (which incorporates AI - the CNN noise reduction), suggesting that human readers benefit from this enhancement. However, a specific effect size (e.g., AUC improvement, percentage increase in accuracy) is not provided in the summary.

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

• Standalone Performance: Partially. The "Ideal Observer Evaluation" seems to be a more objective, algorithm-centric assessment of detectability, stating that "detectability is preserved or improved with the investigational software." Also, the "Analysis of the Difference Images" checked for artifacts without human interpretation as the primary outcome. However, the overall "diagnostic quality" assessment was clinical, involving human readers.

7. The Type of Ground Truth Used

• Type of Ground Truth: The text implies a human expert consensus/evaluation as the primary ground truth for diagnostic quality. The "5-point visual difference scale" and "4-point RadLex scale" evaluated by "board certified radiologists" serve as the basis for assessing diagnostic image quality. For the "Ideal Observer Evaluation," the ground truth likely involved simulated lesions.

8. The Sample Size for the Training Set

• Training Set Sample Size: Not explicitly stated. The text mentions "clinical images with added simulated noise" were used to train the Convolutional Network (CNN).

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

• Ground Truth for Training Set: The ground truth for training the Smart Noise Cancellation module (a Convolutional Network) was established using "clinical images with added simulated noise to represent reduced signal-to-noise acquisitions." This suggests that the model was trained to learn the relationship between noisy images (simulated low SNR) and presumably clean or less noisy versions of those clinical images to perform noise reduction. The text doesn't specify how the "clean" versions were obtained or verified, but it implies a supervised learning approach where the desired noise-free output served as the ground truth.

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The device is designed to perform radiographic x-ray examinations on all pediatric and adult patient treatment areas.

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

The Carestream DRX-Revolution Mobile X-ray System (K191025) underwent modifications compared to its predicate device (K120062). The primary changes include a different X-ray tube supplier, additional support for DRX Plus detectors, updated image acquisition software (ImageView), and a replaced high-voltage X-ray generator.

Here's an analysis of the acceptance criteria and the study proving adherence:

1. Table of Acceptance Criteria and Reported Device Performance:

2. Sample size used for the test set and the data provenance:

• Sample Size: Not explicitly stated as a number of images or cases. The document mentions a "Phantom Imaging study."
• Data Provenance: The study was a "Phantom Imaging study," which implies the use of test phantoms rather than real patient data. This is typically done in a controlled laboratory environment. The country of origin is not specified but given Carestream's location (Rochester, New York), it is likely the US. The study type is retrospective, as it's bench testing to compare a modified device to an already marketed predicate.

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

• This information is not provided in the document. As the study was a non-clinical "phantom imaging study" evaluating technical image quality attributes, it might not have involved human expert readers establishing diagnostic ground truth in the traditional sense. The evaluation likely relied on quantitative measurements of image quality metrics.

4. Adjudication method for the test set:

• This information is not provided as the study was a phantom imaging study focusing on technical image quality. Adjudication methods like 2+1 or 3+1 are typically used in clinical studies with human readers interpreting medical images.

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, an MRMC comparative effectiveness study was not done. The submission explicitly states: "Clinical testing was not required to establish substantial equivalence. Bench testing was sufficient to assess the device safety and effectiveness." This device is a mobile X-ray system, not an AI-powered diagnostic software.

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

• Yes, in a sense, a "standalone" evaluation of the device's image quality was performed through the "Phantom Imaging study" and DQE/MTF data. This tested the device's inherent capability to produce images without direct human interpretation for diagnostic purposes, focusing on technical image quality attributes rather than diagnostic accuracy.

7. The type of ground truth used:

• The ground truth used was based on technical image quality attributes such as detail, sharpness, noise, and appearance of artifacts, as evaluated through a "Phantom Imaging study" and by DQE/MTF data. This is an objective technical assessment against established metrics for image quality, rather than a clinical ground truth like pathology or expert consensus on a diagnosis.

8. The sample size for the training set:

• This information is not applicable/not provided. This device is a hardware X-ray system with standard image processing software, not an AI/Machine Learning algorithm that undergoes a "training" phase with a large dataset. The "ImageView" software is a web-based application to improve usability, and its image processing is stated to be the same as previously cleared versions.

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

• This information is not applicable/not provided for the same reasons as point 8.

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