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uDR 380i Pro is a mobile digital radiography device intended to acquire X-ray images of the human anatomy for medical diagnosis. uDR 380i Pro can be used on both adult and pediatric patient by a qualified and trained operator. This device is not intended for mammography.

uDR 380i Pro is a diagnostic mobile x-ray system utilizing digital radiography (DR) technology. It can be moved to different environments for an examination, like emergency room. ICU and ward. It mainly consists of a lifting column - telescopic cantilever frame system, system motion assembly, X-ray System (high voltage generator, x-ray tube, collimator and wireless flat panel detectors which have been cleared in K170332 and K192632), power supply system and software for acquiring and processing the clinical images.

uDR 380i Pro is intended to acquire X-ray images for both adult and pediatric, especially for person who may not be able to be moved to a traditional RAD room. The system offers:

• A 14"×17" or 14"×14" flat panel detector
• . A high-power, 32 kW or 50kW generator
• A maneuverable drive system
• X-ray tube-collimator assembly with flexible movement
• Storage for detectors and supplies
• Image Acquisition Workstation with touchscreen user interface

I am sorry, but the provided text does not contain detailed information about the acceptance criteria or a study that proves the device meets specific acceptance criteria in the way you've outlined with performance metrics, sample sizes, expert qualifications, and adjudication methods.

The document is a 510(k) premarket notification for a medical device (uDR 380i Pro) and focuses on demonstrating substantial equivalence to a predicate device (Carestream DRX-Revolution). While it lists some technical specifications and claims that these differences do not raise new safety and effectiveness concerns, it does not present a formal study with acceptance criteria and reported device performance in the format you requested.

Here's a breakdown of what is available in the provided text in relation to your request:

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

• The document provides a "Comparison of Technological Characteristics with the Predicate Devices" table (pages 4-7) which lists various specifications for both the proposed device (uDR 380i Pro) and the predicate device (DRX-Revolution).
• Instead of acceptance criteria, it provides the specifications of both devices and uses "Remark" notes (Note 1 to Note 13) to discuss any differences and justify why these differences do not raise new safety and effectiveness concerns.
• For example, for "Maximum Output Power," the proposed device lists "32kW/ 50kW" compared to the predicate's "32kW." The remark states that the larger output power represents better capability and does not raise new safety and effectiveness concerns.
• For "DQE" (Detective Quantum Efficiency), it states "Typical: 58% @3uGy,0.5lp/mm" for the proposed device and "Typical: 63% @2.5uGy,0.5lp/mm" for the predicate, noting that "Performance is similar" and "it did not raise new safety and effectiveness concerns."
• This is a comparison for substantial equivalence, not a standalone performance study against pre-defined acceptance criteria.

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

• The document mentions a "Clinical Image Evaluation" on page 10. It states: "Sample image of Head, chest, abdomen, spine, pelvis, upper extremity and lower extremity were provided with a board certified radiologist to evaluate the image quality in this submission."
• However, it does not specify the sample size (number of images or cases) used for this evaluation.
• The data provenance is not explicitly stated (e.g., country of origin, retrospective or prospective).

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

• It states that "a board certified radiologist" was used.
• It refers to "a board certified radiologist" (singular), implying one expert.
• No specific experience level (e.g., 10 years of experience) is mentioned beyond "board certified."

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

• The document states that "Each image was reviewed with a statement indicating that image quality are sufficient for clinical diagnosis."
• This description points to a subjective review rather than a formal adjudication process (like 2+1 or 3+1 consensus). It sounds like a single radiologist's assessment.

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 comparative effectiveness study is mentioned. The device is a mobile X-ray system, not an AI-based diagnostic tool that assists human readers.

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

• The device is a hardware system (mobile X-ray) with accompanying software for image acquisition and processing. It is not an algorithm for standalone diagnostic performance. Therefore, this question is not directly applicable in the context of this device. The "Clinical Image Evaluation" was about the image quality produced by the system for visual assessment by a radiologist.

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

• For the "Clinical Image Evaluation," the "ground truth" was the assessment of image quality by "a board certified radiologist" who determined if the images were "sufficient for clinical diagnosis." This falls under expert opinion/assessment of image quality rather than a definitive diagnosis established by other means like pathology or outcomes data.

8. The sample size for the training set:

• The document does not mention any training set size because the submission is for a medical imaging device (hardware and software for image acquisition), not a machine learning or AI algorithm that requires a training set for its core function of interpretation or diagnosis.

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

• As no training set is discussed or implied for the device's primary function, this information is not available.

In summary, the provided FDA 510(k) summary focuses on demonstrating substantial equivalence based on technical specifications and a general assessment of clinical image quality by a radiologist, rather than a detailed performance study against specific acceptance criteria for an AI-enabled diagnostic device.

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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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The DIGITAL RADIOGRAPHY CXDI-Pro / D1 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-Pro, also called the D1, (hereinafter referred to as CXDI-Pro) is a solid-state x-ray imager. The CXDI-Pro is a series of detectors, currently consisting of the CXDI-703C Wireless detector unit, also called the AR-D3543W 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 DIGITAL RADIOGRAPHY CXDI-710C Wireless (hereinafter referred to as CXDI-710C) to make the CXDI-Pro. This change will remove the Docking Station, Multi Box, and Status Indicator as optional components and add the X-ray Interface Box and Power Box as optional components. The software has been updated from CXDI Control Software V2.16 to CXDI Control Software V3.10. The case material has been changed from fiberglass to magnesium alloy. Bluetooth function has been added, and Standalone mode has been removed as a photographing mode. Together, these changes make up the CXDI-Pro.

The provided text is a 510(k) Summary for the Canon DIGITAL RADIOGRAPHY CXDI-Pro D1. This document focuses on demonstrating substantial equivalence to a predicate device based on modifications, rather than establishing de novo performance criteria for an AI/ML device. Therefore, it does not explicitly detail acceptance criteria and a study proving a new device meets these criteria in the context of AI/ML performance metrics (e.g., sensitivity, specificity, AUC).

Instead, the document emphasizes that the fundamental scientific technology of the device has not been modified, and the changes are limited to:

• Software update (from V2.16 to V3.10)
• Changes to optional accessories
• Change in case material (fiberglass to magnesium alloy)
• Addition of Bluetooth
• Removal of Standalone mode as a photographing mode

The "performance" section states: "Evaluation of the changes to the CXDI-710C confirmed that the changes did not impact CXDI-Pro conformance with the U.S. Performance Standard for radiographic equipment and with relevant voluntary safety standards for Electrical safety and Electromagnetic Compatibility testing, specifically IEC standards 60601-1, 60601-1-2, 60601-2-54, 60601-1-6, and IEC 60529."

This indicates that the acceptance criteria are primarily related to safety and effectiveness standards for general radiographic equipment, as well as maintaining the existing performance characteristics (like spatial resolution) of the previous device. The study proving the device meets these criteria involved verification/validation activities demonstrating continued compliance with these standards despite the modifications.

Given this context, I will extract and infer information relevant to your request, acknowledging that it's not a study designed to prove AI performance in the way you might expect for a diagnostic algorithm.

Here's a breakdown based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

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

The document does not detail a "test set" in the context of clinical images for diagnostic AI/ML performance evaluation. The "study" here is a series of "verification/validation activities" focused on engineering, software, and hardware changes. Therefore, there is no mention of a specific sample size of patient data or its provenance (country of origin, retrospective/prospective). The evidence presented focuses on compliance testing with engineering and safety standards.

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

Not applicable for this type of submission. Ground truth, in the context of medical image interpretation by experts, is not relevant to this submission, which focuses on hardware and software modifications and compliance with engineering standards.

4. Adjudication Method for the Test Set

Not applicable. There is no mention of a test set requiring adjudication in the context of diagnostic interpretation.

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

No. This submission is for a digital radiography system, not an AI-powered diagnostic assistant. Therefore, no MRMC study comparing human readers with and without AI assistance was performed or described.

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

No. While the document mentions "Standalone mode" was removed as a photographing mode for the device itself, this does not refer to an AI algorithm's standalone diagnostic performance. The device is a digital X-ray imager, which functions as a component in the diagnostic workflow, not an autonomous diagnostic algorithm.

7. Type of Ground Truth Used

The "ground truth" here is the adherence to established engineering, safety, and performance standards (e.g., IEC 60601 series, MTF measurements, IP ratings). It's not clinical ground truth derived from expert consensus, pathology, or outcomes data.

8. Sample Size for the Training Set

Not applicable. This document describes hardware and software changes to an established medical device, not the training of a new AI/ML algorithm.

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

Not applicable, as no AI/ML training set is discussed. The "ground truth" for the device's functional integrity is presumably established through rigorous engineering testing, quality control, and adherence to manufacturing specifications, rather than through labeled datasets for machine learning.

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Intended for use by a qualified/trained doctor or technician on both adult and pediatric subjects for taking diagnostic radiographic exposures of the skull, spinal column, chest, abdomen, extremities, and other body parts. Applications can be performed with the patient sitting, standing, or lying in the prone or supine position. Not for mammography.

This is a new generation of Ceiling Suspension Radiographic System. This system is characterized by its simple and functional design. Thanks to its vertical and horizontal displacements, the suspension can cover almost all the room positions in which it is installed allowing all radiographic procedures. The system is modular and supports different configurations, such as radiographic system without radiographic table or without Wall Stand. The X-ray image receptors used in this system are digital detectors. X-ray film and Computed Radiography (CR) image receptors can be used but they rarely are these days. The device software used is the CANON CXDI which is supplied unmodified by CANON (Clearance numbers above). It has a moderate level of concern. The Radiographic System ChallengeX AP is provided with Auto-positioning, Auto-centering and Auto-tracking functions and it is composed of: Ceiling Suspension (OTC), Radiographic Table, Wall Stand, High Voltage X-ray Generator and acquisition image software. Auto-tracking allows the X-ray Tube to follow the Receptor when it changes position or the other way around while the SID remains constant. The "Auto" features were present and validated in the predicate system.

The document provided is a 510(k) premarket notification for a medical device called "Radiographic System Challenge X." This notification aims to demonstrate substantial equivalence to a legally marketed predicate device, rather than proving that the device meets specific acceptance criteria in a clinical study with an AI component.

Therefore, the requested information regarding acceptance criteria, study details, sample sizes, expert involvement, adjudication methods, MRMC studies, standalone performance, and ground truth establishment for AI-based analysis is not present in the provided text.

The document discusses non-clinical testing for compliance with international standards and FDA guidance for traditional medical device aspects such as safety, electrical compatibility, radiation protection, and software validation. It explicitly states that clinical testing was not required to establish substantial equivalence because the digital x-ray receptor panels already had previous FDA clearance.

Key takeaway from the document regarding studies:

• No AI component or AI-specific acceptance criteria are documented.
• The device is a traditional X-ray system, not an AI-powered diagnostic tool.
• Clinical testing was not performed for this submission. The substantial equivalence was established through non-clinical bench testing and compliance with existing standards and previously cleared components (like the detectors and software).

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