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The KDR™ AU-DDR System Advanced U-Arm with Dynamic Digital Radiography and KDR™ AU System Advanced U-Arm with Static Digital Radiography is indicated for use by qualified/trained doctor or technician on both adult and pediatric subjects for taking diagnostic static and serial 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).

The proposed System is a digital radiography diagnostic system that has the capability of obtaining two modes (static mode and dynamic modes) of radiographic exposures of the skull, spinal column, chest, abdomen, extremities, and other body parts. Images may be obtained with the patient sitting, standing, or lying in the prone or supine position. It is not intended for mammographic use. The system is configurable in two options. Both are exactly the same with the exception of the option to select one of two flat panel detectors. One configuration, referred to as KDR™ AU-DDR System Advanced U-Arm with Dynamic Digital Radiography contains a HD/FNB flat panel and the other configuration, referred to as KDR™ AU System Advanced U-Arm with Static Digital Radiography a HQ/KDR panel. The technological feature of each flat panel detector is described below.

The proposed system is a compact, floor and wall mounted radiographic system with proprietary ULTRA software and DICOM 3 connectivity.

The system consists of a combination of several components. The System's hardware consists of the 3 kev components:

1. A floor and wall-mounted Positioner (also referred to as a stand)
2. A generator
3. An off-the-shelf computer with proprietary software (also referred to as an acquisition workstation)

The positioner has a swivel arm that has several rotating and linear movements, and movement controls including an information screen. Mounted on the positioner are:
a) A collimator,
b) An X-ray tube
c) An Automatic Exposure Control (AEC)
d) A flat panel detector (There are 2 configurations available for the end user to select. The KDR™ AU-DDR System Advanced U-Arm with Dynamic Digital Radiography contains a HD/FNB flat panel detector capable of obtaining both static and dynamic images and the KDR™ AU System Advanced U-Arm with Static Digital Radiography, which contains the HQ/KDR flat panel detector capable of obtaining static images only.

Hardware accessories include:

1. A mobile patient table
2. Stitching stand
3. Weight bearing stand

Optional Hardware accessories include:

1. Motorized height adjustable table
2. 3 knob collimator
3. Dose area product meter
4. Advanced weight bearing stand

The proposed system has a proprietary ULTRA software as the central interface of the system. The software for the proposed system enables users to acquire static and dynamic images.

There are two modes within the software package of the proposed system, "static mode," which may be used to generate, a single frame of radiographic images captured at a single time and "dynamic mode" (or "Dynamic Digital Radiography," abbreviated "DDR,") which generates multiple frames in a single series, presenting the physician with a diagnostic view of dynamic density and anatomic motion without using fluoroscopy or cine. The number of images acquired with the proposed system are limited to 300 compared with flouroscopy or cine, which do not limit the number of images (Note: only the configuration with the HD/FNB flat panel detector is capable of obtaining both static and dynamic images. The other configuration may only obtain static images).

The system is also capable of quickly assuming a preprogrammed position when a new exam is selected, saving time when positioning the equipment. This is referred to as "auto positioning," and made possible by the positioner and image processing software working together.

The provided document is a 510(k) summary for the Konica Minolta Healthcare Americas KDR™ AU-DDR System Advanced U-Arm with Dynamic Digital Radiography and KDR™ AU System Advanced U-Arm with Static Digital Radiography.

This document describes the device and its substantial equivalence to predicate devices, focusing on regulatory compliance and technical specifications rather than specific clinical performance data for AI/software components. The primary performance data discussed refers to compliance with safety and performance standards for X-ray systems, not an AI-driven diagnostic or assistive feature.

Therefore, many of the requested points regarding acceptance criteria and study details (like sample size for test/training sets, expert ground truth, adjudication methods, MRMC studies, or standalone performance for an AI component) are not present in this document. The document primarily addresses the safety and effectiveness of the X-ray system hardware and its software for image acquisition, not an AI-based diagnostic tool.

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

1. Table of Acceptance Criteria and Reported Device Performance

The document does not provide a table of acceptance criteria with specific performance metrics for an AI component. Instead, it refers to compliance with various electrical, safety, and imaging standards for the overall X-ray system.

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

• Not explicitly stated in the provided document. The document discusses compliance with technical standards for an X-ray system, not the performance of an AI algorithm on a specific medical image dataset.

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

• Not applicable / not stated. This document focuses on the technical and safety performance of an X-ray imaging system, not on a machine learning model requiring expert-annotated ground truth for diagnostic accuracy.

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

• Not applicable / not stated. The context of this document does not involve diagnostic interpretations requiring adjudication.

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 done / not stated. The device described is an X-ray acquisition system; it does not present itself as an AI-assistive diagnostic tool for human readers in the context of this 510(k) summary.

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

• Not applicable / not stated. The device is an X-ray system, which includes software for image acquisition ("proprietary ULTRA software"), but the document does not describe a standalone AI algorithm for diagnostic interpretation.

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

• Not applicable / not stated. Ground truth, in the context of diagnostic AI models, is not relevant to the compliance testing of an X-ray imaging system described here.

8. The sample size for the training set:

• Not applicable / not stated. The document describes an X-ray system, and there's no mention of a machine learning component requiring a training set in this context.

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

• Not applicable / not stated. As no training set is mentioned for an AI algorithm, ground truth establishment is not relevant to the information provided.

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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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The AeroDR SYSTEM 2 is indicated for use in generating radiographic images of human anatomy. It is intended to replace radiographic film/screen system in general-purpse diagnostic procedures.

The AeroDR SYSTEM 2 is not in mammography. fluoroscopy, tomography and angiography applications.

The AeroDR SYSTEM 2 is a digital imaging system to be used with diagnostic x-ray systems. A new AeroDR Detector (flat panel digital detector: hereafter P-51) and AeroDR Generator Interface Unit2 has been just added to AeroDR SYSTEMS (The predicate devices:K102349, K113248, K120477, K130936) to function together such as with Console CS-7 (operator console), AeroDR Interface Unit, AeroDR Interface Unit2, AeroDR Generator Interface Unit, AeroDR Access Point and AeroDR Battery Charger, AeroDR Battery Charger2 and perform fundamentally same as Aero DR SYSTEMS do in physical and performance characteristics such as in device design, material safety and physical properties. Therefore, images captured with the flat panel digital detector in the AeroDR SYSTEM 2 can be communicated to the operator console via wired connection or wireless, depend on user's choice. The AeroDR SYSTEM 2 is just developed to meet user's compact layout needs without changing fundamental functions of the predicate devices.

AeroDR SYSTEM 2 is only connected with X-ray devices which are regally marketed in the United States of America and are compatible with XGIF, UEC, XIF Board along with certain electronic requirement, Specific signal controls for hardware and software and accessories described in Operation manual and Installation manual which is also fulfilled how to compatibility test at the time of installation also. In addition, for the use of pediatric, X-ray control system for pediatric are required.

The provided document, a 510(k) summary for the AeroDR SYSTEM 2, does not contain detailed information about acceptance criteria and a study proving the device meets those criteria in the format requested. The document focuses on demonstrating substantial equivalence to a predicate device, AeroDR SYSTEMS.

However, based on the information provided, here's what can be extracted and inferred:

1. Table of Acceptance Criteria and Reported Device Performance:

The document doesn't explicitly list specific quantitative acceptance criteria or a performance table. Instead, it states that the AeroDR SYSTEM 2 was evaluated for "equivalent evaluation outcome" to the predicate device. The performance characteristics mentioned are qualitative comparisons to the predicate device.

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

The document mentions "Non clinical and clinical testing" but does not specify the sample size for these tests or the data provenance (e.g., country of origin, retrospective/prospective).

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

This information is not provided in the document.

4. Adjudication Method for the Test Set:

This information is not provided in the document.

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

The document does not mention an MRMC comparative effectiveness study or any effect size of human readers improving with AI vs. without AI assistance. The AeroDR SYSTEM 2 is a digital radiography system, not an AI-assisted diagnostic tool.

6. Standalone Performance:

The document implies standalone performance testing ("Bench Testing," "Non clinical and clinical testing") was conducted to demonstrate equivalence to the predicate device. However, it does not explicitly state "algorithm only without human-in-the-loop performance" as would be relevant for an AI device. As it's a hardware/software system for image generation, its standalone performance refers to its ability to capture and process images equivalently to the predicate.

7. Type of Ground Truth Used:

The document does not explicitly state the type of ground truth used for the "clinical testing." Given the context of a diagnostic imaging system, it would typically involve images reviewed against a clinical standard, but the specific nature (e.g., expert consensus, pathology, outcomes data) is not detailed.

8. Sample Size for the Training Set:

The document does not mention a training set or its size. This is consistent with a device seeking substantial equivalence to a predicate, where the focus is on verification and validation against established standards and predicate performance rather than training a novel algorithm from scratch.

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

Not applicable, as no training set is mentioned for an AI model.

Summary of what is present and absent regarding acceptance criteria and study details:

The document primarily acts as a 510(k) summary, aiming to prove substantial equivalence to existing predicate devices based on various safety, performance, and technical characteristics. It asserts that "equivalent evaluation outcome" was achieved in performance, non-clinical, and clinical testing, and that there were "no safety and effectiveness and performance issue or no differences were found" compared to the predicate. However, it lacks the detailed quantitative acceptance criteria, specific study designs, sample sizes, and expert qualification information that would be typically found for studies evaluating novel AI algorithms or clinical efficacy with precise endpoints.

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