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The RADspeed PRO is intended to generate digital or conventional radiographic images of the skull, spinal column, chest, abdomen, extremities, and other body parts of human anatomies in all routine radiography examinations. The RADspeed Pro enables radiographic or tomographic exposures of the whole body of all ages including pediatric patients. Exposures may be taken with the patient sitting, standing, or lying in the prone or supine position. The RADspeed PRO uses portable or integrated flat panel detectors to generate diagnostic images by converting x-rays into electronic signals. The device is also designed to be used with conventional film/screen or computed radiography (CR) cassettes. The Tomosynthesis option is intended to generate tomographic images of human anatomies. Tomosynthesis technique is used to produce a specific cross-sectional plane of the body by reconstruction. The device is intended to be used in hospitals, clinics, imaging centers, and/or other healthcare facilities by qualified/trained professionals. The device is not intended for mammographic applications.

The RADspeed PRO is an X-ray radiography system that is mainly used for the radiography of various regions of the patient's body in a standing or recumbent position. The RADspeed PRO can be used in a wide range of applications from general radiography using X-ray film or Computed Radiography (CR) cassettes, to digital radiography. The RADspeed PRO consists of an X-ray high voltage generator, X-ray tube unit, X-ray tube support and collimator. The system can be configured with radiographic table, radiographic stand and digital radiography system as well. Optionally, the device is also used to perform tomosynthesis radiography by three different reconstruction modes. Filtered Back-Projection (FBP) mode is used to obtain a tomosynthesis image by performing back-projection after correcting the projection data. Shift Addition (SA) mode is used to obtain a tomosynthesis image at an arbitrary slice plane height by shifting each image according to projection angle of the tube based on the reconstruction height, and by applying image addition processing to them. Iteration (IR) mode is used to reduce metal artifact in tomosynthesis image. FBP mode is generally recommended for all body parts. In case an artifact is observed at joints and other similar places, SA mode may remedy this artifact. In case metal artifact is obviously displayed, IR mode is recommended to reduce metal artifact.

The provided document is a 510(k) premarket notification for the Shimadzu Corporation's RADspeed PRO, referencing K233722. It primarily focuses on demonstrating substantial equivalence to a predicate device (K152244) rather than presenting a performance study against specific acceptance criteria for a new clinical indication or AI algorithm.

The modifications to the RADspeed PRO are described as mainly cosmetic and updates to hardware components (larger displays, updated imaging panels that are themselves cleared 510k devices) and software infrastructure (programming language, operating system) without changes to the underlying software functionality or core technological features. The device does not appear to incorporate a new AI algorithm for diagnostic purposes that would require a study with clinical performance metrics like sensitivity, specificity, or reader improvement.

Therefore, the requested information regarding acceptance criteria and performance study results for an AI algorithm (including sample sizes, ground truth establishment, expert adjudication, MRMC studies, or standalone performance) is not present in the provided text.

The document states:

• "The fundamental technological features are the same for the subject and the predicate systems. The modifications are mainly cosmetic in nature." (Page 4)
• "The software functionality remains unchanged." (Page 5, Page 6)
• "The imaging components were replaced by more recently cleared digital x-ray receptor panels made by FUJIFILM. ... All the imaging panels have 510(k) clearance... The integration software, although updated, has the identical functionality to the predicate." (Page 5)
• "The radiation safety aspects of the device have not changed. The device remains compliant with the FDA Radiation Safety Standards." (Page 9)
• "The non-clinical data supports the safety of the device and the hardware and software verification and validation demonstrate that new device should performs as intended in the specified use. Based on our risk analysis and bench testing, the differences do not affect its clinical safety or effectiveness." (Page 9)

The performance testing mentioned (Software Validation, EMC and Electrical Safety Testing) is related to regulatory compliance and safety for the modified device, not to the clinical performance of a new diagnostic algorithm.

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

The Rover product concept was developed under a contract from the Australian Department of Defense to fulfil a need for a full performance digital medical x-ray imager, light enough to be used in deployed medical facilities. Key Design Features: Full trauma imaging capability 40-110kV, 0.2-20mAs; Ultra-light weight at 105 kg; Ground Clearance allows for 75mm step up; Operation on uneven ground; Spare battery tray swap out in under a minute; The unit uses FDA cleared digital image capture panels and software made by FujiFilm OR Varex.

The provided document is a 510(k) summary for a mobile x-ray system (ROVER) and does not describe acceptance criteria for an AI/ML device or detailed studies proving such a device meets those criteria. The document focuses on establishing substantial equivalence for a hardware medical device to previously cleared devices.

Therefore, many of the requested items (e.g., sample size for test set, data provenance, number of experts, adjudication method, MRMC comparative effectiveness, ground truth type, training set size and ground truth establishment methods) are not applicable or cannot be extracted from this document as it pertains to an X-ray system, not an AI/ML diagnostic aid.

Here's the information that can be extracted or inferred:

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

The document does not specify quantitative acceptance criteria in terms of diagnostic performance metrics for an AI/ML device. Instead, it relies on regulatory standards and the equivalence to predicate devices. The "reported device performance" is essentially that it operates properly and produces diagnostic quality images.

2. Sample size used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)

Not applicable. The document states "Clinical testing was not required to establish substantial equivalence because all digital x-ray receptor panels have had previous FDA clearance." The testing described is bench testing and verification of system operation, not a clinical study with a test set of patient data.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g. radiologist with 10 years of experience)

Not applicable, as no clinical test set requiring expert ground truth was used.

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. This device is an X-ray system, not an AI diagnostic aid requiring MRMC studies to assess reader improvement.

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

Not applicable. This is an X-ray system, not an AI algorithm.

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

Not applicable, as no clinical test set requiring ground truth was used. The focus was on engineering verification and compliance with standards.

8. The sample size for the training set

Not applicable. This is not an AI/ML device, so there is no training set mentioned.

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

Not applicable.

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

The Rover product concept was developed under a contract from the Australian Department of Defense to fulfil a need for a full performance digital medical x-ray imager, light enough to be used in deployed medical facilities. Key Design Features:

• Full trauma imaging capability 40-110kV, 0.2-20mAs;
• Ultra-light weight at 105 kg;
• Ground Clearance allows for 75mm step up;
• Operation on uneven ground;
• Spare battery tray swap out in under a minute;
  The unit uses FDA cleared digital image capture panels and software made by FujiFilm.

The provided document is a 510(k) Premarket Notification for the "Rover" mobile x-ray system. It details the device's technical specifications and compares it to a legally marketed predicate device (DRX-Revolution Nano Mobile X-ray System). The acceptance criteria and testing described are focused on demonstrating substantial equivalence to an existing device, rather than proving performance against specific acceptance criteria for an AI/ML-based device.

Therefore, the document does not contain the information requested regarding acceptance criteria related to AI/ML device performance, ground truth establishment, expert adjudication, MRMC studies, or standalone algorithm performance.

Here's why the document doesn't provide the requested information and what it does provide:

• Device Type: The Rover is a mobile x-ray system, a physical medical device for capturing x-ray images. It uses FDA-cleared digital image capture panels and software (specifically, Fujifilm and Fuji FDX Console Advance DR-ID 300CL Software) which are themselves "previously cleared." This submission is about the system integrating these components, not about a novel AI/ML algorithm for image analysis or diagnosis.
• Basis for Clearance: The basis for clearance is "substantial equivalence" to a predicate device, focusing on functional, technical, and safety equivalence of the hardware and integrated pre-cleared software.
• Testing: The testing detailed is primarily non-clinical bench testing to confirm proper system operation and safety standards compliance (e.g., IEC standards, radiation performance, cybersecurity, wireless technology).
• Clinical Testing: The document explicitly states: "Clinical testing was not required to establish substantial equivalence because all digital x-ray receptor panels have had previous FDA clearance." This means no new clinical data (and thus no associated ground truth, expert reads, or AI performance metrics) was generated for this specific 510(k) submission.

Summary of what is present in the document that somewhat relates to the request, but not in the context of AI/ML acceptance criteria:

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

   • Acceptance Criteria (Implicit): Substantial equivalence to the predicate device in terms of indications for use, configuration, generator specifications, panel interfaces, and meeting US performance standards (21 CFR 1020.30 and 21 CFR 1020.31). Also, compliance with various IEC standards (60601-1, 60601-1-2, 60601-1-3, 60601-1-6, 60601-2-28, 60601-2-54).
   • Reported Device Performance: The "Substantial Equivalence Chart" (page 5) compares the Rover to the predicate, showing "SAME" or "Equivalent" for most characteristics. "Bench testing indicate that the new devices are as safe and effective as the predicate devices. Proper system operation is fully verified upon installation. We verified that the modified combination of components worked properly and produced diagnostic quality images as good as our predicate generator/panel combination." (page 6)

2. Sample sized used for the test set and the data provenance: Not applicable in the context of AI/ML performance testing. Testing was system-level functional and safety verification.

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 was not established as part of this submission. The software components were previously cleared.

4. Adjudication method for the test set: Not applicable.

5. If a multi reader multi case (MRMC) comparative effectiveness study was done: No, an MRMC study was not done.

6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done: No, as this is a mobile x-ray system, not a standalone AI algorithm. The imaging software used is a pre-cleared component.

7. The type of ground truth used: Not applicable for AI/ML performance. The "ground truth" for the device's acceptable performance was its compliance with safety standards and its ability to produce diagnostic quality images comparable to the predicate, as verified through bench testing.

8. The sample size for the training set: Not applicable. This device is not an AI/ML algorithm that requires a training set.

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

In conclusion, the document describes the clearance of a medical device (a mobile X-ray system) based on substantial equivalence to a predicate, not the performance claims of a novel AI/ML-based diagnostic or analytical tool. Therefore, it does not provide the specific information requested about acceptance criteria and studies for AI/ML device performance.

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