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The DIGITAL RADIOGRAPHY CXDI-Elite / E1 provides digital image capture for conventional film/screen radiographic examinations. This device is intended to capture, for display, radiographic images of human anatomy, and to replace radiographic film/screen systems in all general purpose diagnostic procedures. This device is not intended for mammography applications.

The DIGITAL RADIOGRAPHY CXDI-Elite, also called the DIGITAL RADIOGRAPHY E1, (hereinafter referred to as CXDI-Elite) is a solid-state x-ray imager. The CXDI-Elite is a series of detectors, and in the predicate device consists of the CXDI-720C Wireless detector (also called the AR-E3543W detector), the CXDI- 420C Wireless detector (also called the AR-E4343W detector), and the CXDI-420C Fixed detector (also called the AR-E4343S detector). The detector intercepts x-ray photons, and the scintillator emits visible spectrum photons that illuminate an array of photodetectors that create electrical signals. After the electrical signals are generated, the signals are converted to digital values. The digital values are sent to the PC via a wired or wireless connection, converted to images with the CXDI Control Software, and then displayed on the PC/monitors. The PC/monitors used with the CXDI-Elite are not a part of this submission. The proposed changes to the predicate device, DIGITAL RADIOGRAPHY CXDI-Elite (K221620), include the addition of a new detector, the CXDI-820C Wireless (also called the AR-E2735W), to the CXDI-Elite series; the standardization of the thickness of CXDI-720C Wireless and CXDI-420C Wireless; the firmware update from F2.00.03.00 to 02.01.04.00; the CXDI Control Software version update from 3.10.2.2 to 3.10.2.6; and the addition of a 25 m of the CXDI-420C Fixed detector cable and a 10 m of Wiring Cable (WC-01). Together, these changes make up the CXDI-Elite.

Here's a breakdown of the acceptance criteria and study information for the Digital Radiography CXDI-Elite / E1, based on the provided text:

Important Note: The provided document is a 510(k) summary, which focuses on demonstrating substantial equivalence to a predicate device rather than a comprehensive performance study of a novel device. Therefore, a traditional "study that proves the device meets the acceptance criteria" in terms of clinical outcomes or diagnostic accuracy with human-in-the-loop is expressly stated as not necessary for this submission. The "acceptance criteria" here are primarily about conformance to safety and performance standards after design changes, and the "study" is the verification/validation activities.

Acceptance Criteria and Reported Device Performance

Study Details:

This submission describes verification/validation activities rather than a clinical study.

1. Sample size used for the test set and the data provenance: Not applicable in the context of a clinical test set. The submission refers to "adequate detector bench testing" and "verification/validation activities" which would involve testing the device itself against various parameters and standards. No specific number of images or patient data is mentioned for testing in the summary provided. The focus is on the device's technical performance and safety, not diagnostic accuracy based on a dataset.

2. Number of experts used to establish the ground truth for the test set and the qualifications of those experts: Not applicable. Ground truth for diagnostic decision-making from expert interpretation is not relevant for this type of submission, as it's not a diagnostic AI device requiring human reader comparison. The "ground truth" here is compliance with engineering specifications and regulatory standards, established through technical testing.

3. Adjudication method for the test set: Not applicable. As there were no human readers establishing ground truth for diagnostic accuracy, no adjudication method was used.

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: Not applicable. This device is a general-purpose digital radiography system, not an AI-assisted diagnostic tool.

5. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done: Yes, in terms of product performance and safety, the device itself was evaluated in a standalone manner (through bench testing and compliance checks). The "device works as intended" conclusion stems from this.

6. The type of ground truth used:

   • Engineering specifications and performance standards: For parameters like pixel pitch, scintillator type, spatial resolution, IP level, wireless communication, and physical dimensions.
   • Regulatory standards: IEC 60601-1, 60601-1-2, 60601-1-3, 60601-2-54, 60601-1-6, and the U.S. Performance Standard for radiographic equipment.

7. The sample size for the training set: Not applicable. This document does not describe the development of an AI algorithm, so there is no "training set."

8. How the ground truth for the training set was established: Not applicable, as there is no training set for an AI algorithm mentioned.

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The DIGITAL RADIOGRAPHY CXDI-Elite / E1 provides digital image capture for conventional film/screen radiographic examinations. This device is intended to capture, for display, radiographic images of human anatomy, and to replace radiographic film/screen systems in all general purpose diagnostic procedures. This device is not intended for mammography applications.

The DIGITAL RADIOGRAPHY CXDI-Elite, also called the DIGITAL RADIOGRAPHY E1, (hereinafter referred to as CXDI-Elite) is a solid-state x-ray imager. The CXDI-Elite is a series of detectors, and in the predicate device consists of the CXDI-720C Wireless detector unit, also called the AR-E3543W detector. The detector intercepts x-ray photons, and the scintillator emits visible spectrum photons that illuminate an array of photodetectors that create electrical signals. After the electrical signals are generated, the signals are converted to digital values, and the images will be displayed on monitors. The digital value can be communicated to the operator console via a wired or wireless connection.

The subject of this Special 510(k) submission is a change to the CXDI-Elite to add new detectors, the CXDI-420C Wireless (also called AR-E4343W) and the CXDI-420C Fixed (also called AR-E4343S), to the CXDI-Elite series of detectors. In addition, a Detector Stand (DS-01) has also been added as a component to be used with both the CXDI-720C Wireless and CXDI-420C Wireless. Compatibility with the Power Box (PB-01), a CXDI-Pro component that was cleared under K220098 has also been added. The CXDI Control Software has been updated from V3.10.0.4 to V3.10.2.2 to fix some bugs, update the GPU driver, and add several minor functional changes. Together, these changes make up the CXDI-Elite.

Upon review of the provided document, it is important to note that the submission is for an imaging device (Digital Radiography CXDI-Elite / E1), not an AI/Software as a Medical Device (SaMD) that performs diagnostic or assistive functions requiring performance studies with acceptance criteria based on accuracy metrics.

The document primarily addresses a Special 510(k) submission for changes to an existing, cleared device. These changes involve adding new detectors, an optional accessory, and software bug fixes/minor functional changes. Therefore, the "acceptance criteria" and "study that proves the device meets the acceptance criteria" in this context refer to engineering and safety performance, rather than clinical efficacy measured by AI model output and human reader performance.

The provided text does not contain information about:

• A table of acceptance criteria and reported device performance related to diagnostic accuracy.
• Sample sizes for test sets (in the context of clinical performance evaluation).
• Data provenance (country of origin, retrospective/prospective).
• Number of experts for ground truth establishment or their qualifications.
• Adjudication methods.
• Multi-Reader Multi-Case (MRMC) comparative effectiveness studies.
• Standalone algorithm performance studies.
• Types of ground truth (expert consensus, pathology, outcomes data).
• Sample size for training sets.
• How ground truth for training sets was established.

Instead, the document focuses on demonstrating substantial equivalence to a predicate device by confirming that the modifications do not raise new questions of safety or effectiveness. This is primarily achieved through non-clinical/bench testing and adherence to relevant standards.

Here's a summary of what the document does provide regarding acceptance criteria and performance, as it pertains to the hardware and basic software functionality of a digital radiography system:

Acceptance Criteria and Study for Digital Radiography CXDI-Elite / E1 (Hardware/Software Updates)

The acceptance criteria are implicitly met by demonstrating continued compliance with relevant international and FDA standards, and by showing that the changes (new detectors, accessory, software update) do not negatively impact the fundamental performance or safety characteristics of the device.

1. Table of Acceptance Criteria (Implicit) and Reported Device Performance (as stated in the document):

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

• The document states "Adequate detector bench testing should be sufficient to demonstrate that the subject detector CXDI-Elite / E1 works as intended." This implies physical testing of the devices, but does not specify sample sizes for "test sets" in the clinical performance sense, as this was not a clinical performance study. The testing appears to be primarily engineering verification and validation of hardware and software components.
• Data provenance: Not applicable in the context of clinical images or patient data. The testing is a validation of the device's technical specifications and compliance.

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

• Not applicable. This was not a study requiring expert readers to establish ground truth on clinical images, as it was not a diagnostic performance study for an AI algorithm.

4. Adjudication Method for the Test Set:

• Not applicable for the reasons stated above.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was done, and what was the effect size of how much human readers improve with AI vs without AI assistance:

• No. This was not an AI-enabled diagnostic device or a comparative effectiveness study involving human readers.

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

• No. This device is a digital radiography system, not a standalone AI algorithm. Its function is to capture and display radiographic images.

7. The Type of Ground Truth Used:

• Not applicable in the clinical diagnostic sense. The "ground truth" for this submission are the established engineering specifications, safety standards (IEC 60601 series), and FDA guidance documents, against which the device's technical performance (e.g., MTF, DQE, IP level, functional modes) is verified via bench testing.

8. The Sample Size for the Training Set:

• Not applicable. This device does not involve machine learning or AI models with training sets.

9. How the Ground Truth for the Training Set was Established:

• Not applicable for the reasons stated above.

In conclusion, the provided document is a 510(k) summary for a digital radiography hardware system with minor modifications. The "acceptance criteria" and "study" described pertain to engineering validation, safety testing, and demonstration of substantial equivalence by showing that the updated device continues to meet established performance and safety standards, and does not pose new risks. It is not an AI/SaMD performance study seeking to establish diagnostic accuracy against clinical ground truth.

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The DIGITAL RADIOGRAPHY CXDI-Elite / E1 provides digital image capture for conventional film/screen radiographic examinations. This device is intended to capture, for display, radiographic images of human anatomy, and to replace radiographic film/screen systems in all general purpose diagnostic procedures. This device is not intended for mammography applications.

The DIGITAL RADIOGRAPHY CXDI-Elite, also called the DIGITAL RADIOGRAPHY E1, (hereinafter referred to as CXDI-Elite) is a solid-state x-ray imager using TFT (thin-film transistor) arrays. The CXDI-Elite is a series of detectors, currently consisting of the CXDI-720C Wireless detector unit, also called the AR- E3543W detector. The detector intercepts x-ray photons, and the Cesium-Iodide scintillator emits visible spectrum photons that illuminate an array of photodetectors that create electrical signals. After the electrical signals are generated, the signals are converted to a digital image, and the images will be displayed on monitors. The digital image can be communicated to the operator console via a wired or wireless connection.

The monitors used with the CXDI-Elite are not a part of this submission.

The CXDI Control Software is updated from V2.16 to V3.10 in this submission. The update to the software includes bug fixes, modification to check-in function, modification to communication with X-ray emission devise, calibration support, addition of the Intelligent NR function (previously cleared under K212269), and the addition of Standard Synchronization Mode with Built in AEC Assistance.

The provided document K213780 is a 510(k) Pre-market Notification for a digital radiography device, the Canon DIGITAL RADIOGRAPHY CXDI-Elite / E1. This document establishes substantial equivalence to a predicate device, and as such, does not contain the detailed acceptance criteria for an AI/ML algorithm or the specific study that would prove the device meets such criteria.

The document makes a brief reference to an "Intelligent NR function (previously cleared under K212269)" in the section "Device Description," indicating that this specific function (likely an AI/ML noise reduction feature) has been previously cleared. However, the current 510(k) submission (K213780) is focused on the overall digital radiography system and its hardware/software updates, not specifically on the detailed performance of the "Intelligent NR function."

Therefore, based solely on the provided text, it is not possible to answer the requested questions about acceptance criteria and study details for an AI/ML device/algorithm, as this document does not provide that specific information.

To address the prompt, I will explain why the information cannot be extracted from this document:

1. A table of acceptance criteria and the reported device performance: Not present. This document is for a general digital radiography system, not specifically an AI/ML diagnostic or assistive algorithm. The "Intelligent NR function" is briefly mentioned as having been "previously cleared," meaning its performance data and acceptance criteria would be in the K212269 submission, not this one.
2. Sample sizes used for the test set and the data provenance: Not present. The document states "Clinical testing is not necessary for the current submission, based on the minor differences from the predicate device. Adequate detector bench testing should be sufficient to demonstrate that the subject detector CXDI-Elite / E1 works as intended." This indicates that the substantial equivalence was based on bench testing and comparisons with predicate devices, not a clinical test set with human subject data for an AI/ML algorithm.
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts: Not present, as no clinical test set for an AI/ML algorithm is described.
4. Adjudication method (e.g. 2+1, 3+1, none) for the test set: Not present, as no clinical test set for an AI/ML algorithm is 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: Not present. The document explicitly states "Clinical testing is not necessary."
6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done: Not present. The focus is on the complete system and its hardware.
7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.): Not present, as no clinical test set for an AI/ML algorithm is described.
8. The sample size for the training set: Not present.
9. How the ground truth for the training set was established: Not present.

In summary, the provided document (K213780) is a 510(k) Pre-market Notification for a general digital radiography system. It establishes substantial equivalence based on hardware and software updates, and bench testing, not on clinical performance studies of an AI/ML algorithm. While an "Intelligent NR function" (likely an AI/ML feature) is mentioned as previously cleared, the details of its specific acceptance criteria and the study proving it meets those criteria are not included in this particular submission.

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