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The DR-XD 3000 is a mobile C-arm system with detachable flat panel detector, which is intended for use in providing medical imaging for general populations including pediatrics. The device provides pulsed fluoroscopic imaging of patients during diagnostic, interventional and surgical procedures and digital radiographic imaging. It is intended for use in procedures such as cholangiography, endoscopic, orthopedic, neurologic, peripheral vascular, critical care, emergency room procedures. This device does not support cardiac procedures and is not intended for use in performing mammography.

The FDR CROSS is mobile X-ray fluoroscopy equipment designed and manufactured by Fujifilm Corporation (FTYO) featuring high mobility arising from small size and light weight. The C-arm cart irradiates X-rays and detects X-rays by the flat panel sensor to perform X-ray fluoroscopy and radiography. A flat panel sensor has higher sensitivity than an image intensifier, which can result in dose reduction. The flat panel sensor is same as FDR D-EVO III Flat Panel Detector System (predicate device) cleared as radiography purpose. (K192932) The system contains the console software (DR-ID 340CL), control cabinet software (DR-ID 3000MC) and X-ray controller software (DR-ID 3000SX). The DR-ID 340CL and DR-ID 3000MC is modified to add fluoroscopic function based on the DR-ID 300CL and DR-ID 1200MC which are used in FDR D-EVO III Flat Panel Detector System cleared as radiography purpose (K192932). The software's Level of Concern is Moderate.

The provided text is a 510(k) Summary for a medical device (FDR CROSS (DR-XD 3000)), primarily focusing on demonstrating substantial equivalence to a predicate device. It details the device's characteristics and indicates what standards and guidance documents were followed for non-clinical performance data.

However, the document does not contain any information about an acceptance criteria table, device performance data against acceptance criteria, sample sizes for test or training sets, data provenance, expert ground truth establishment (number of experts, qualifications, adjudication), MRMC studies, or standalone algorithm performance.

The "SUMMARY OF STUDIES" section states: "Non-clinical Performance Data: The FDR CROSS (DR-XD 3000) conforms to the voluntary standards such as AAMI/ANSI ES60601-1, IEC 60601-1, IEC 60601-1-2, IEC 60601-1-3, IEC 60601-1-6, IEC 62304, IEC 62366-1, DICOM 3.0, IEC 60601-2-43, IEC 60601-2-54. In addition, the FDA's Guidance for the Submission of 510(k)'s for Solid State X-ray Imaging Devices (issued on August 6, 1999) was followed to describe the detector characteristics, and Radio Frequency Wireless Technology in Medical Devices (issued August 14, 2013) was followed to test. As required by the risk analysis, necessary verification and validation activities were performed including software testing, and the results were satisfactory."

This indicates that the performance evaluation was based on conformance to voluntary standards and internal verification and validation activities, rather than a clinical study involving human readers or AI performance metrics against a clinical ground truth. The device listed (FDR CROSS (DR-XD 3000)) appears to be an X-ray imaging system, not an AI/CADe device that would typically have the kind of acceptance criteria and performance study described in your prompt.

Therefore, for your specific request:

1. A table of acceptance criteria and the reported device performance: Not provided in the document. The performance evaluation focuses on conformance to general medical device standards for X-ray systems.
2. Sample sized used for the test set and the data provenance: Not provided. The studies mentioned are non-clinical (conformance to standards, software testing).
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts: Not applicable/Not provided. No human expert review to establish ground truth is described.
4. Adjudication method (e.g. 2+1, 3+1, none) for the test set: Not applicable/Not provided.
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 applicable. This device is an imaging system, not an AI assistance tool.
6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done: Not applicable. This is not an algorithm-only device.
7. The type of ground truth used (expert consensus, pathology, outcomes data, etc): Not applicable. The performance is assessed against engineering and regulatory standards for X-ray devices.
8. The sample size for the training set: Not applicable/Not provided. This device is an X-ray system, not a machine learning model that requires a training set.
9. How the ground truth for the training set was established: Not applicable.

In conclusion, the provided FDA 510(k) summary for the FDR CROSS (DR-XD 3000) does not contain the type of acceptance criteria and study information (e.g., clinical performance metrics, AI-specific studies) you are asking for. The clearance for this device is based on its substantial equivalence to a predicate X-ray system, demonstrated primarily through engineering safety and performance testing against recognized standards.

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Xmaru View V1(Xmaru Chiroview or Xmaru Podview) software carries out the image processing and administration of medical X-ray data which includes adjustment of window leveling, rotation, zoom, and measurements. Ymaru View V (Xmaru Chiroview or Xmaru Podview) is not approved for mammography and is meant to be used by qualified medical personnel only. Xmaru Chiroview or Xmaru Podview) is complying with DICOM standards to assure optimum communications between network systems.

XmaruView V1 is a software program designed to provide image acquisition, processing and operational management functions for Digital Radiography. XmaruView V1 performs connects with Flat-Panel Detectors and Generator to acquire digital images. The software also manages information on patients, tests and images through an internal database. It also supports DICOM which allows excellent compatibility with other radiography equipment and network programs. XmaruView V1 provides a streamlined process of multiple workflows. This optimizes any hospital environment for digital radiography.

The provided document is a 510(k) summary for the XmaruView V1 software, including its variants Xmaru Chiroview and Xmaru Podview. This document focuses on demonstrating substantial equivalence to a predicate device and details the software's functionalities and validation rather than presenting a performance study with specific acceptance criteria and detailed results from a clinical trial or large-scale evaluation.

Therefore, many of the requested details regarding acceptance criteria, study performance, sample sizes, expert involvement for ground truth, and MRMC studies are not explicitly stated or applicable in the context of this 510(k) submission, which is primarily a declaration of equivalence and software validation against internal testing.

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

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

The document states that the software validation test was "designed to evaluate all input functions, output functions, and actions performed by XmaruView V1." It also mentions that "the risk analysis and individual performance results were within the predetermined acceptance criteria." However, the specific acceptance criteria (e.g., quantitative metrics like accuracy, sensitivity, specificity, or specific error rates) and the reported device performance against these criteria are not detailed in this 510(k) summary. These would typically be found in the manufacturer's internal validation reports, which are summarized but not fully presented here.

The main functional acceptance criteria implied are:

• Ability to perform image acquisition and processing (window leveling, rotation, zoom, measurements).
• Compliance with DICOM standards for communication.
• Reliable management of patient, test, and image information.
• Proper functioning of the "Grid ON" feature (for the upgraded version).

2. Sample sizes used for the test set and the data provenance

The document does not specify a "test set" in the sense of a distinct set of clinical images used for a performance study. The validation described is primarily a software validation and risk analysis (IEC 62304, ISO 14971), which involves testing the software's functionality and safety internally. This is not a clinical performance study using patient data with a defined sample size for generalization.

• Sample Size for Test Set: Not specified. The validation described is internal software testing, not a clinical study on a dataset of patient images.
• Data Provenance: Not specified. Given it's internal software validation, it's likely using test data generated by the manufacturer or potentially anonymized internal clinical data, but this is not detailed. It is not specified as retrospective or prospective clinical data.

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

This information is not applicable and not provided as the submission describes software functional and safety validation, not a diagnostic performance study requiring expert-established ground truth on a clinical image set.

4. Adjudication method for the test set

This information is not applicable and not provided as there is no described clinical "test set" requiring adjudication.

5. If a multi-reader multi-case (MRMC) comparative effectiveness study was done, and the effect size of how much human readers improve with AI vs without AI assistance

An MRMC study was not done and is not described in this 510(k) summary. The device is image processing software (a PACS component with additional features), not an AI-assisted diagnostic tool that helps human readers. Its primary function is image display, manipulation (zoom, rotation, etc.), and management, including a "Grid ON" feature for image enhancement. It does not provide diagnostic insights or AI assistance to human readers for interpretation.

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

The device is image processing software; it doesn't perform diagnostic functions as a standalone algorithm. Its "performance" is in its ability to correctly acquire, process, and display images and manage data. The validation described is focused on the correct functioning of the software itself ("evaluate all input functions, output functions, and actions performed by XmaruView V1").

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

Not applicable in the context of a diagnostic ground truth, as this is software validation. The "ground truth" for the software validation would be based on predefined specifications for how each function should operate and the expected output for given inputs. For example, applying a "rotate 90 degrees" function would be validated by checking if the image is indeed rotated by 90 degrees.

8. The sample size for the training set

Not applicable and not specified. This is not an AI/machine learning device that requires a training set. The "Grid ON" function might involve an algorithm, but it's not described as a deep learning model requiring a large training dataset in the context of this submission.

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

Not applicable and not specified, as this is not an AI/machine learning device with a training set.

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The FDR AQRO (DR-XD 1000) is a digital mobile X-ray system intended for use in general purpose radiography for generating radiographic images of human anatomy, including adult, pediatric, and neonatal exams. The FDR AORO is not intended for mammography.

FUJIFILM's FDR AQRO (DR-XD1000) is a compact, economical, lightweight, nonmotorized, low power (2.5 kW), mobile X-ray system designed to work with FUJIFILM's GOS and Csl scintillator FDR D-EVO2 (DR-ID 12XXSE) family of digital X-ray detectors coupled. The D-EVO2 detectors received clearance on 7/23/2014 via 510(k) K142003. The FDR AQRO includes a built-in operation console. The AQRO's console uses Version 10.0 of Fujifilm's FDX Console Software. This software received 510(k) clearance via K170451 on 3/16/2017. The console software includes Virtual Grid 2 (VG2) Image Processing functionality. The VG2 function allows using the mobile X-ray system without a physical grid, resulting in a dose reduction of up to 50% (when compared to using a physical grid). The Virtual Grid 2 Image Processing software received clearance on 4/8/2016 via K153464. The reduction in the external dimensions of FDR AQRO enables smooth movement in the hospital and at the bedside because of an integrated X-ray tube and high-voltage generator (mono-block) that eliminates the need for High Voltage cables and utilizes less space. A high performance Li-ion battery provides up to twelve (12) hours of continuous use (at ~20 exposures/hour) with a quick full charge in four hours. A quick charge of 15 minutes provides one hour of usage. Exposure may also be made when the AC power cord is plugged in.

This document describes the 510(k) premarket notification for the Fujifilm FDR AQRO (DR-XD1000) Mobile X-ray System. As such, it primarily focuses on demonstrating substantial equivalence to a predicate device rather than presenting a detailed clinical study with specific acceptance criteria, comprehensive performance metrics, and a full statistical plan often found in PMA applications or de novo submissions for novel AI/ML devices.

Therefore, many of the typical elements requested in your prompt regarding acceptance criteria and performance studies (e.g., number of experts for ground truth, adjudication methods, MRMC studies, effect sizes, training set details) are not explicitly provided or applicable in this 510(k) summary for a mobile X-ray system. The performance is assessed primarily through non-clinical (phantom images, compliance with standards) and limited clinical data (sample clinical images) to demonstrate that the device is as safe and effective as the predicate.

However, I can extract the information that is present and explain why certain details are missing based on the nature of this submission.

Here's the breakdown based on the provided document:

1. Table of Acceptance Criteria and Reported Device Performance

Note: The document does not present "acceptance criteria" in the sense of specific quantitative thresholds for clinical performance (e.g., sensitivity, specificity, AUC) that a novel diagnostic AI algorithm would typically undergo. Instead, the "performance" is demonstrated through:

• Compliance with recognized standards: This serves as a primary form of "acceptance criteria" for safety and basic performance of X-ray systems.
• Comparison to a predicate device: The core argument is substantial equivalence, meaning the new device performs "as safe and effective as" the legally marketed predicate.
• Image Quality: Assessed via sample phantom and clinical images, which are qualitatively evaluated rather than against quantitative metrics in this summary.
• Technical Specifications: Comparison of technical specs to the predicate.

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

• Test Set Sample Size: The document does not specify a quantitative "test set" sample size in terms of number of cases for a clinical performance study. The evaluation appears to involve "sample phantom images" and "sample clinical images" for qualitative assessment. This is typical for a 510(k) for an X-ray system, which focuses on device safety and basic image generation capabilities, rather than a diagnostic algorithm that analyzes images for specific findings.
• Data Provenance: Not specified within this summary. It's likely general radiography data, but no country of origin or whether it's retrospective/prospective is mentioned.

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

• Not Applicable/Not Specified: For a 510(k) submission of an X-ray system, the "ground truth" is typically the physical output and image quality of the device (compared to a predicate and standards), not a diagnostic finding that requires expert interpretation to establish a gold standard. Without a diagnostic study quantifying performance against a true disease state, there's no mention of experts establishing ground truth for a test set.

4. Adjudication Method for the Test Set

• Not Applicable/Not Specified: As there isn't a stated clinical study with a test set requiring interpretation for specific findings, there is no adjudication method described.

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

• No: The document does not mention an MRMC study comparing human readers with and without AI assistance. This device is an X-ray system, not an AI software for diagnosis. While it includes "Virtual Grid 2" software, its clearance (K153464) and functionality relate to image processing affecting dose and image appearance, not to diagnostic AI assistance for readers that would warrant an MRMC study.

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

• No: This is a physical X-ray system. While it contains software components (FDX Console Software and Virtual Grid 2), the "standalone performance" refers to the system as a whole in generating images, not an AI algorithm analyzing images independently. The performance data is primarily demonstrated through technical specifications, compliance with standards, and visual assessment of sample images.

7. The Type of Ground Truth Used

• Technical Specifications & Compliance Standards/Predicate Comparison: The "ground truth" used for this device is effectively its ability to generate radiographic images safely and effectively, achieving comparable technical performance metrics (e.g., DQE, MTF, tube characteristics, radiation control) to a legally marketed predicate device, and compliance with relevant industry and medical device standards. Qualitative visual assessment of sample phantom and clinical images also contributes.

8. The Sample Size for the Training Set

• Not Applicable/Not Specified: This is not an AI/ML device that learns from a "training set" of images in the conventional sense. It's an X-ray system. The software components like FDX Console and Virtual Grid 2 would have been developed and tested through software validation processes (IEC 62304 compliance is noted), but this does not involve a "training set" of patient images in the way an AI diagnostic algorithm would.

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

• Not Applicable/Not Specified: Since there is no "training set" for an AI/ML model described, there is no ground truth established in this context. Device performance is evaluated against engineering specifications, safety standards, and equivalence to a predicate.

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