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The ANY VIEW DR SERIES (Anyviw-500DR, Anyview-240DR) is radiation medical equipment only used by professional radiologists. This product is designed to provide fluoroscopic and spot film images of the patient during diagnostic and interventional procedures.

This system can be applied in emergency room, operation room, cast room or etc. of hospital.

The ANYVIEW DR SERIES (Anyviw-500DR, Anyview-320DR, Anyview-240DR) are mobile x-ray fluoroscopic imaging systems used by radiation experts. ANYVIEW DR SERIES are a digital fluoroscopic imaging systems with Flat Panel Detector (FPD) used in diagnostic and interventional procedures. ANYVIEW DR SERIES are composed of C-arm, x-ray generating equipment (x-ray controller, high voltage generator, x-ray tube, motor-type collimator), FPD, and workstation (console computer and monitor).

ANYVIEW imaging software is a Digital Imaging System (DIS) designed for C-arm, ANYVIEW FPD Fluoroscopic Mobile X-ray System. ANYVIEW imaging software provides useful functions to manage X-ray images obtained from ANYVIEW DR SERIES FPD Fluoroscopic Mobile X-ray System.

ANYVIEW imaging software provides various image tools. One of the most noticeable features is that the C-arm imaqes taken during an exam are stored in the database for further review. I Image data is integrated with the patient information in DICOM(OPTION) compatible format which allows compatibility with existing DICOM and PACS system.

The ECOTRON Co., Ltd. ANYVIEW DR SERIES FPD Fluoroscopic Mobile C-Arm (ANYVIEW-240DR, ANYVIEW-320DR, ANYVIEW-500DR) is a radiation medical equipment designed to provide fluoroscopic and spot film images for diagnostic and interventional procedures. The device was found to be substantially equivalent to its predicate device, Anyview-500R Fluoroscopic Mobile X-ray System (K160279), based on non-clinical performance data.

1. Table of Acceptance Criteria and Reported Device Performance:

The provided document does not explicitly state formal "acceptance criteria" in a go/no-go format for performance metrics. Instead, it presents a comparison of technical characteristics and performance data of the subject device against its predicate and reference devices, demonstrating equivalent or improved performance without raising new safety or effectiveness concerns.

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

• Test Set: The study primarily relies on bench testing and comparative performance data of components (specifically the Flat Panel Detector, FPD) for non-clinical evaluation. There is no mention of a traditional "test set" of patient images.
• Data Provenance: The data provenance for the performance characteristics (MTF, DQE, Spatial Resolution) of the detectors is from the respective manufacturers (VIEWORKS for Subject Device, TOSHIBA for Predicate Device, and Thales for Reference Device). This is non-clinical, technical performance data. The document does not specify country of origin for this component-level data, nor whether it is retrospective or prospective, though it is typically collected during product development/testing by the manufacturer.

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. The study is a 510(k) submission for substantial equivalence based on technical and performance characteristics of medical hardware, not on clinical image interpretation or diagnostic accuracy by experts. No ground truth was established by human experts for a test set of images.

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

Not applicable, as no test set requiring expert adjudication was used.

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 was done. The device described is an X-ray imaging system, not an AI-powered image analysis tool.

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

Not applicable. The device is an imaging system, not an algorithm, and its performance evaluation does not involve standalone algorithm assessment.

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

Not applicable. The equivalence is established through engineering and technical performance comparisons of the device and its key components against predicate and reference devices, not against clinical ground truth. Performance metrics like MTF, DQE, and spatial resolution are derived from physical measurements and scientific testing of the imaging system and its components.

8. The sample size for the training set:

Not applicable. This is a medical device for imaging acquisition, not a machine learning or AI algorithm, so there is no concept of a "training set."

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

Not applicable.

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The Skan-C, a Mobile Surgical C-Arm X-Ray System, is intended to provide Fluoroscopic and Radiographic images of the patient during Diagnostic, Surgical and Interventional procedures.

Examples of Clinical Applications may include Orthopaedic. GI Procedure like Endoscopy and Cholenography. Neurology, Urology Procedures, Vascular, Critical Care and Emergency Room Procedures.

Skan-C is not recommended for Cardiac Applications.

Skan-C Surgical C-Arm is indication in real time and/or recording of surgical region of interest and anatomy, using X-ray imaging technique.

SKAN-C, is a mobile X-Ray C-Arm fluoroscopic device to assist in quiding medical intervention surgical procedures. The device can also be used for radiographic applications. The device is designed in such a way that it can be moved around and can be positioned for the required anatomical/clinical/procedural position.

SKAN-C, a Mobile Surgical C-Arm consists of two units, namely, Mobile Image Intensified C-Arm unit with generator, and a Work-Station for Image display, store and manipulation. C-Arm unit with generator is capable of movements which are essential for patient positioning, like horizontal travel, orbital movement, wig-wag movement and C rotation. The X-ray generator, X-Ray control system and collimator controls are housed in the C-Arm unit.

The provided document is a 510(k) premarket notification for a medical device (Skan C Mobile C-Arm X-Ray system). It outlines the device's indications for use, technological characteristics, and non-clinical/clinical tests performed to demonstrate substantial equivalence to a predicate device, rather than providing specific acceptance criteria and detailed study results typical of a performance evaluation directly tied to an AI algorithm.

Based on the provided text, the device is an X-ray system, not an AI-powered diagnostic device. Therefore, the questions related to AI-specific metrics, ground truth establishment for AI training/testing, and multi-reader multi-case studies for AI assistance are not directly applicable to this document.

However, I can extract information related to the performance evaluation of the X-ray system itself, which serves as its "acceptance criteria" and "study" for regulatory purposes.

Here's an interpretation of the requested information based on the provided document, focusing on the device as an X-ray system:

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

The document doesn't present a direct "acceptance criteria" table in the way one might see for diagnostic performance metrics (e.g., sensitivity, specificity). Instead, it demonstrates compliance with recognized safety and performance standards and compares its technological characteristics to a predicate device. The "acceptance criteria" can be inferred as meeting these standards and showing comparable technical specifications.

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

The document does not specify a numerical sample size for "test sets" in the context of diagnostic image analysis, as it's an X-ray system, not an AI algorithm being tested for diagnostic accuracy on a dataset.

• Non-Clinical Tests: These involve testing the physical device against engineering and safety standards. There isn't a "sample size" of images or patients in the typical sense. It refers to testing the device's components and overall system functionality (e.g., radiation output measurements, EMC tests).
• Clinical Tests (Usability and Image Quality):
  • Usability: "Usability aspects of the device were tested by the users and independent participants." No specific number provided.
  • Image Quality: "Independent views of Radiologists were obtained on the imaging performances and the acquired images were of adequate quality for the indicated use." No specific number of images or patients mentioned.
• Data Provenance: Not explicitly stated for any "data." The company is based in India (Skanray Technologies Private Limited, Mysore, India). It's common for such tests to be conducted internally or by accredited labs in the manufacturer's region or contracted locations.

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)

• Number of Experts: "Independent views of Radiologists were obtained on the imaging performances." The number of radiologists is not specified, only that "Radiologists" (plural) were involved.
• Qualifications: Stated as "Radiologists." No specific experience level (e.g., "10 years of experience") or subspecialty is detailed in the document.

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

The document states "Independent views of Radiologists were obtained." This implies individual assessments. There is no mention of an adjudication method (like 2+1 or 3+1 consensus) being used for combining expert opinions or resolving discrepancies.

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

There was no MRMC comparative effectiveness study done comparing human readers with AI assistance versus without AI assistance. This device is an X-ray imaging system, not an AI diagnostic tool. Its performance evaluation focuses on the safety, technical specifications, and image quality of the X-ray system itself.

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

This question is not applicable. The device is a C-Arm X-Ray system, not an AI algorithm. Therefore, there is no "standalone algorithm" performance to report.

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

For the "clinical tests" part of the evaluation:

• Usability: The "ground truth" was user feedback and observational assessment by "users and independent participants."
• Image Quality: The "ground truth" was "Independent views of Radiologists" regarding image adequacy. This is a form of expert opinion/consensus (though the consensus method isn't detailed). There's no mention of pathology or outcomes data for this specific evaluation in the provided summary.

8. The sample size for the training set

This question is not applicable. The device is a C-Arm X-Ray system, not an AI algorithm that requires a "training set" in the machine learning sense. The X-ray system is developed and validated through engineering standards and clinical evaluations demonstrating its functionality and safety, not through machine learning training.

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

This question is not applicable, as there is no "training set" for an AI algorithm in the context of this device.

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The XEPHILIO MC-1100 mobile fluoroscopy system is designed to provide fluoroscopic and spot-film radiographic images of the patient during diagnostic, surgical and interventional procedures. Examples of clinical application may include cholangiography, endoscopy, urologic, orthopedic, neurologic, vascular, cardiac, critical care and emergency room procedures. The system may be used for other imaging applications at the physician's discretion.

The XEPHILIO MC-1100 mobile fluoroscopy system consists of two mobile units: a Mainframe (C-Arm) and a Workstation. The Mainframe (C-Arm) is comprised of a high voltage generator, x-ray control, and a "C" shaped apparatus, which supports an X-ray tube and a flat panel detector [Canon CSX-10]. The Mainframe is designed to perform linear and rotational motions that allow the user to position the x-ray imaging components at various angles and distances with respect to the patient. The Mainframe can be used to acquire both still and moving images. The Workstation is a mobile platform that supports image display monitors and image processing. Interfaces are provided for optional peripherals such as recording and printing devices.

The provided document, K121303, a 510(k) summary for the XEPHILIO MC-1100 mobile fluoroscopy system, primarily focuses on demonstrating substantial equivalence to predicate devices through comparisons of technological characteristics, non-clinical test data, and compliance with safety standards. It does not contain detailed information about specific acceptance criteria, a study proving those criteria, or the specific performance metrics typically associated with AI/algorithm-based medical devices.

Therefore, many of the requested sections about acceptance criteria, detailed study design, ground truth establishment, expert involvement, and AI performance metrics cannot be directly extracted from this document.

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

1. Table of Acceptance Criteria and Reported Device Performance:

Based on the provided text, specific quantitative acceptance criteria and their corresponding reported device performance values in a comparative study are not detailed. The document generally states that "Tests were performed on the XEPHILIO MC-1100 which demonstrated that the device is safe and effective, performs comparably to the predicate device(s), and is substantially equivalent to the predicate device(s)." This indicates qualitative acceptance of comparable performance rather than specific numerical thresholds.

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

The document mentions "non-clinical image comparisons involving flat panel display images taken with the new device and the predicate device(s)." However, it does not specify the sample size (number of images or cases) used in these comparisons for the test set, nor does it provide any information on the data provenance (e.g., country of origin, retrospective or prospective).

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

The document does not mention using experts to establish ground truth for image comparisons. The "non-clinical image comparisons" likely refer to technical image quality assessments rather than clinical interpretation.

4. Adjudication Method for the Test Set:

No information about an adjudication method is provided, as no expert review process is described for the test set.

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

The document does not indicate that an MRMC comparative effectiveness study was conducted to evaluate human reader improvement with or without AI assistance. The device is a mobile C-Arm system, and the evaluation focuses on its performance against predicate hardware, not on AI-assisted diagnostic effectiveness.

6. Standalone (Algorithm Only) Performance Study:

The document does not describe a standalone (algorithm only) performance study. The device is a hardware system, and the evaluations are about its overall safety and effectiveness as a medical imaging system.

7. Type of Ground Truth Used:

For the "non-clinical image comparisons," the "ground truth" would likely be based on technical image quality metrics and specifications, compared against the predicate devices. It is not based on expert consensus, pathology, or outcomes data in a clinical diagnostic sense, as this is a hardware device submission.

8. Sample Size for the Training Set:

The document does not mention or imply the existence of a "training set" in the context of an AI/algorithm. The device is a hardware imaging system, and its development and testing are described in terms of engineering validation and verification, not machine learning model training.

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

As there is no mention of a "training set" or an AI/algorithm being developed, there is no information on how ground truth for a training set would have been established.

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• The device is a Surgical Mobile C-arm Imaging X-ray System and allows a technician to examine a patient by fluoroscopy or radiography.
• This device is operated and used by Physicians and X-ray technologist.
• The object of this device is total patient populations.

The device is a Surgical Mobile C-arm Imaging X-ray System.

This is a 510(k) premarket notification for a Shimadzu Corporation Surgical Mobile C-arm Imaging X-ray System, model WHA-200. This document is an FDA communication regarding substantial equivalence to a predicate device. It does not contain information about acceptance criteria or a study proving the device meets acceptance criteria. The document only confirms that the device is substantially equivalent to legally marketed predicate devices.

Therefore, I cannot provide the requested information from the provided text.

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The Pathfinder Mobile C-Arm is intended to be used for routine radiography and follow-up x-rays after studies such as (e.g. gastrointestinal studies, chest, bone, hip panings, and pacemaker implants).

Not Found

The provided text is a 510(k) clearance letter from the FDA for the MOONRAY Mobil C-Arm, indicating that the device has been found substantially equivalent to a legally marketed predicate device. This document does not contain any information about acceptance criteria, device performance studies, sample sizes, expert qualifications, or ground truth establishment.

Therefore, I cannot fulfill your request to describe the acceptance criteria and the study that proves the device meets them based on the provided text. The letter only confirms the regulatory clearance of the device.

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The MINI 6800 Digital Mobile C-arm is designed to provide the physician with general fluoroscopic visualization of the patient including but not limited to surgical orthopedic and extremity imaging. The device is not intended for whole-body pediatric imaging.

The OEC mobile workstation, which supports image display monitors, image processing and recording devices, is combined with a miniature C-arm to create the MINI 6800 Digital Mobile C-arm.

Interfaces are provided for optional peripheral devices such as thermal or laser printers and VCRs. Video outputs are compatible with RS-170 format for North American markets, CCIR format for international markets, and DICOM 3.0.

The MINI 6800 has the following physical characteristics:

• . All components are contained in one mobile workstation.
• . An articulating arm is attached to the workstation and extends out from the main cabinet to position the x-ray imaging components.
• . All mechanical positioning of the workstation and articulating arm is manual (nonmotorized).
• . The system is powered by a non-detachable power cord.
• Power ratings between 100-240 VAC, 4-6 Amps at 50/60 Hz. .
• . Internal system power is insulated from input power by an isolation transformer.
• Fluoroscopic operation: .
• -- 40 to 80 kVp
• -- 20 to 160 µA (0.020 to 0.160 mA)
• Automatic Exposure Rate Control

Major components of the system include:

• Dual video monitors .
• Input isolation transformer .
• Digital image processing and x-ray control .
• Monoblock X-ray tube and high-voltage power supply .
• lmage intensifier ●

The provided text does not contain information about specific acceptance criteria, device performance metrics, or study details such as sample sizes, data provenance, expert qualifications, or adjudication methods for the MINI 6800 Digital Mobile C-arm.

The document is a 510(k) Summary and a Substantial Equivalence Letter for the device. It focuses on:

• Device Description and Intended Use: General fluoroscopic visualization, surgical orthopedic, and extremity imaging, not for whole-body pediatric imaging.
• Substantial Equivalence: Comparing the MINI 6800 to other marketed mobile C-arms, stating they share similar functions and components (x-ray generator, image intensifier, video display, digital image processing).
• User Characteristics: Healthcare professionals trained in medical x-ray equipment.
• Technical Specifications: kVp, µA ranges, automatic exposure rate control, major system components (monitors, image intensifier, etc.), and physical characteristics.
• Standards Compliance: A list of product safety and electrical standards the device is designed in accordance with (e.g., 21 CFR 1020.30-32, ANSI/NFPA 70 & 99, UL 2601, various IEC standards).

Therefore, I cannot fill out the requested table or answer the specific questions about acceptance criteria, study design, or performance metrics as this information is not present in the provided text. The document asserts conformity to various safety and performance standards ("designed in accordance with product safety requirements"), but it does not detail specific acceptance criteria for image quality, diagnostic accuracy, or any other functional performance and then report study results against those criteria. It is a regulatory submission for substantial equivalence, not a scientific publication detailing a performance study.

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The Pathfinder Mobile C-Arm is intended to be used for routine radiography and follow-up x-rays after studies such as (e.g. gastrointestinal studies, chest, bone, hip pnnings, and pacemaker implants).

Not Found

This 510(k) summary does not contain the detailed information necessary to complete the requested table and answer all the questions. The provided document is a notification of substantial equivalence for a medical device (Pathfinder Mobile C-Arm) and focuses on regulatory approval rather than a detailed study report.

Here's what can be inferred and what is missing based on the input:

1. Table of Acceptance Criteria and Reported Device Performance:

This information is not available in the provided document. The 510(k) clearance letter confirms that the device is "substantially equivalent" to predicate devices, but it does not detail specific performance characteristics or acceptance criteria used in a clinical or technical study.

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

• Sample size for test set: Not available in the document.
• Data provenance (country of origin, retrospective/prospective): Not available in the document. It's common for 510(k) submissions to reference studies, but the details of those studies are not typically included in the clearance letter itself.

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

Not available in the document. The clearance letter does not describe any human expert review process for a test set.

4. Adjudication method for the test set:

Not applicable/Not available. Since details of a test set and ground truth establishment are not provided, an adjudication method cannot be determined.

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 a Mobile C-Arm, which is an imaging hardware device, not an AI or software-based diagnostic algorithm that would typically involve human readers interpreting images with or without AI assistance. Therefore, an MRMC comparative effectiveness study in the context of AI assistance is highly unlikely for this type of device.

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

Not applicable. This device is a hardware imaging system, not a standalone algorithm.

7. The type of ground truth used:

Not available/Not applicable. As the document does not describe a study involving a "ground truth" for diagnostic accuracy, this information cannot be extracted. The substantial equivalence determination for this type of device typically relies on demonstrating that the new device performs as intended and is as safe and effective as a legally marketed predicate device, often through engineering, bench, and sometimes limited clinical testing for safety and basic image quality, rather than a diagnostic accuracy study with a rigorous ground truth.

8. The sample size for the training set:

Not applicable. As this is a hardware device and not an AI algorithm, there would be no "training set" in the context of machine learning.

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

Not applicable. See point 8.

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