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The Wireless and Wired Yushan X-Ray Flat Panel Detector is intended to capture for display radiographic images of human anatomy. It is intended for use in general projection radiographic applications wherever conventional film/screen or CR systems may be used. The Yushan X-Ray Flat Panel Detector is not intended for mammography, fluoroscopy, tomography, and angiography applications. The use of this product is not recommended for pregnant women and the risk of radioactivity must be evaluated by a physician.

The Subject Device Yushan X-Ray Flat Panel Detector is static digital x-ray detector, model V14C PLUS, F14C PLUS, V17C PLUS are portable (wireless/ wired) detectors, while V17Ce PLUS is a non-portable (wired) detector. The Subject Device is equivalent to it's predicate device K243171, K201528, K210988, and K220510.

The Subject Device is designed to be used in any environment that would typically use a radiographic cassette for examinations. Detectors can be placed in a wall bucky for upright exams, a table bucky for recumbent exams, or removed from the bucky for non-grid or free cassette exams. The Subject Device has memory exposure mode, and extended image readout feature. Additionally, rounded-edge design for easy handling, image compression algorithm for faster image transfer, LED design for easy detector identification, extra protection against ingress of water.The Detector is currently indicated for general projection radiographic applications and the scintillator material is cesium iodide (CsI).

The Subject Device can automatically collect x-ray images from an x-ray source. It collects x-rays and digitizes the images for their transfer and display to a computer. The x-ray generator (an integral part of a fully-functional diagnostic system) is not part of the device. The sensor includes a flat panel for x-ray acquisition and digitization and a computer (including proprietary processing software) for processing, annotating and storing x-ray images.

The Subject Device is working by using DROC (Digital Radiography Operating Console), Xresta or DR console, which are unchanged from the predicate device, cleared under K201528 for DROC and K243171 for Xresta and DR console. The DROC or Xresta is a software running on a Windows PC/Laptop as a user interface for radiologist to perform a general radiography exam. The function includes:

1. Detector status update
2. Xray exposure workflow
3. Image viewer and measurement
4. Post image process and DICOM file I/O
5. Image database: DROC or Xresta supports the necessary DICOM Services to allow a smooth integration into the clinical network

The DR Console is a software/app-based device, which is a software itself. When this app is operating the OTS can be considered as the iOS system (iOS 16 or above), the safety and effectiveness of this OTS has been assessed and evaluated through the software testing (compatibility) action and also the usability test (summative evaluation). All the functions operate normally and successfully under this OTS framework. The function includes:

1. Imaging procedure review
2. Worklist settings
3. Detector connection settings
4. Calibration
5. Image processing

The software level of concern for the Yushan X-Ray Flat Panel Detector with DROC, Xresta, or DR Console has been determined to be basic based on the "Guidance for the Content of Premarket Submissions for Software Contained in Medical Devices"; and the cybersecurity risks of the Yushan X-Ray Flat Panel Detector with DROC, Xresta, or DR Console have also been addressed to assure that no new or increased cybersecurity risks were introduced as a part of device risk analysis. These risks are defined as sequence of events leading to a hazardous situation, and the controls for these risks were treated and implemented as proposed in the risk analysis (e.g., requirements, verification).

Acceptance Criteria and Study for Yushan X-Ray Flat Panel Detector (K250211)

This documentation describes the acceptance criteria and the study conducted for the Yushan X-Ray Flat Panel Detector (models V14C PLUS, F14C PLUS, V17C PLUS, V17Ce PLUS). The device has received 510(k) clearance (K250211) based on substantial equivalence to predicate devices (K243171, K201528, K210988, K220510).

The primary change in the subject device compared to its predicates is an increase in the CsI scintillator thickness from 400µm (in some predicate CsI models) to 600µm. This change impacts image quality metrics but, according to the manufacturer, does not introduce new safety or effectiveness concerns.

1. Table of Acceptance Criteria and Reported Device Performance

The acceptance criteria for this device are implicitly tied to demonstrating that the changes in scintillator thickness do not negatively impact safety or effectiveness, and ideally, improve image quality. The primary performance metrics affected by the scintillator change are DQE, MTF, and Sensitivity.

Summary of Device Performance vs. Acceptance:
The subject device demonstrates improved DQE, superior noise performance, and smoother images compared to the predicate device (specifically, CsI models), while maintaining comparable MTF and meeting all other safety and performance standards. The slight reduction in MTF compared to the highest performing predicate CsI model (0.69 vs 0.64 at 1 lp/mm) is likely considered an acceptable trade-off given the improvements in DQE and noise, and it is still significantly higher than GOS models.

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

The document does not explicitly state the numerical sample size for the test set used for the performance evaluation of the image quality metrics (DQE, MTF, Sensitivity, noise, smoothness). These metrics are typically derived from physical measurements on a controlled test setup rather than a clinical image dataset.

Data Provenance: Not explicitly stated regarding country of origin or retrospective/prospective nature. However, the evaluation results for image quality metrics, noise, and smoothness are generated internally by the manufacturer during design verification and validation activities.

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

Not applicable. The ground truth for DQE, MTF, and Sensitivity measurements is established through standardized physical phantom measurements (e.g., using RQA5 beam quality) rather than expert consensus on clinical images. These are quantifiable engineering parameters.

4. Adjudication Method for the Test Set

Not applicable. The evaluation of DQE, MTF, and Sensitivity is based on objective instrumental measurements, not on reader interpretations or consensus methods.

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

No, a multi-reader multi-case (MRMC) comparative effectiveness study was not explicitly mentioned or performed as part of this 510(k) submission. The submission focuses on demonstrating substantial equivalence based on technical specifications and physical performance measurements rather than a clinical trial assessing reader performance.

6. Standalone Performance Study

Yes, a standalone performance evaluation was conducted for the device. The reported DQE, MTF, and Sensitivity values, as well as the assessments of noise performance and image smoothness, are measures of the algorithm's (and the underlying detector hardware's) intrinsic performance without human-in-the-loop assistance. The comparison of these metrics between the subject device and the predicate device forms the basis of the standalone performance study.

7. Type of Ground Truth Used

The ground truth used for the performance evaluations (DQE, MTF, Sensitivity, noise, smoothness) is based on objective physical measurements and standardized phantom evaluations. These are quantitative technical specifications derived under controlled laboratory conditions, not expert consensus on pathology, clinical outcomes, or interpretations of patient images.

8. Sample Size for the Training Set

Not applicable. This device is an X-ray flat panel detector, a hardware component that captures images. While it includes embedded software (firmware, image processing algorithms), the document does not indicate that these algorithms rely on a "training set" in the context of machine learning. The image processing algorithms are likely deterministic or parameter-tuned, not learned from a large dataset like an AI model for diagnosis.

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

Not applicable, as there is no indication of a machine learning "training set" as described in the context of AI models. The ground truth for the development and validation of the detector's physical performance characteristics is established through established metrology and engineering testing protocols.

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Allengers Wireless/ Wired X-Ray Flat Panel Detectors used with AWS (Acquisition Workstation Software) Synergy DR FDX/Synergy DR is used to acquire/Process/Display/Store/Export radiographic images of all body parts using Radiographic techniques. It is intended for use in general radiographic applications wherever a conventional film/screener CR system is used.

Allengers Wireless/Wired X-ray Flat Panel Detectors are not intended for mammography applications.

The Wireless/ Wired X-Ray Flat Panel Detectors are designed to be used in any environment that would typically use a radiographic cassette for examinations. Detectors can be placed in a wall bucky for upright exams, a table bucky for recumbent exams, or removed from the bucky for non-grid or free cassette exams. These medical devices have memory exposure mode, and extended image readout feature. Additionally, rounded-edge design for easy handling, image compression algorithm for faster image transfer, LED design for easy detector identification, extra protection against ingress of water. This Device is currently indicated for general projection radiographic applications and the scintillator material is using cesium iodide (CsI). The Wireless/ Wired X-Ray Flat Panel Detectors sensor can automatically collect x-ray from an x-ray source. It collects the x-ray and converts it into digital image and transfers it to Desktop computer / Laptop/ Tablet for image display. The x-ray generator (an integral part of a complete x-ray system), is not part of the submission. The sensor includes a flat panel for x-ray acquisition and digitization and a computer (including proprietary processing software) for processing, annotating and storing x-ray images, the personal computer is not part of this submission.

Wireless/ Wired X-Ray Flat Panel Detectors used with Accessory: "AWS (Acquisition Workstation Software) Synergy DR FDX/ Synergy DR", runs on a Windows based Desktop computer/ Laptop/ Tablet as a user interface for radiologist to perform a general radiography exam. The function includes:

1. User Login
2. Display Connectivity status of hardware devices like detector
3. Patient entry (Manual, Emergency and Worklist)
4. Exam entry
5. Image processing
6. Search patient Data
7. Print DICOM Image
8. Exit

This document describes the 510(k) clearance for Allengers Wireless/Wired X-Ray Flat Panel Detectors (K243734). The core of the submission revolves around demonstrating substantial equivalence to a predicate device (K223009) and several reference devices (K201528, K210988, K220510). The key modification in the subject device compared to the predicate is an increased scintillator thickness from 400µm to 600µm, which consequently impacts the Modulating Transfer Function (MTF) and Detective Quantum Efficiency (DQE) of the device.

Based on the provided text, the 510(k) relies on non-clinical performance data (bench testing and adherence to voluntary standards) to demonstrate substantial equivalence, rather than extensive clinical studies involving human subjects or AI-assisted human reading.

Here's a breakdown of the requested information based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

The acceptance criteria are implicitly defined by the comparison to the predicate device's performance, particularly for image quality metrics (MTF and DQE). The goal is to demonstrate that despite changes, the device maintains diagnostic image quality and does not raise new safety or effectiveness concerns.

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

The document explicitly states that the submission relies on "Non-clinical Performance Data" and "Bench testing". There is no mention of a clinical test set involving human subjects or patient imaging data with a specified sample size. The data provenance would be laboratory bench testing results. The country of origin of the data is not explicitly stated beyond the company being in India, but it's performance data, not patient data. The testing is described as functional testing to evaluate the impact of different scintillator thicknesses.

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

This information is not applicable as the clearance is based on non-clinical, bench testing data (physical performance characteristics like MTF and DQE) rather than clinical image interpretation or diagnostic performance that would require human expert ground truth.

4. Adjudication Method for the Test Set

Not applicable, as there is no mention of a human-read test set or ground truth adjudication process.

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

No. The document does not mention an MRMC study or any study involving human readers, with or without AI assistance. The device is an X-ray detector, not an AI software.

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

Not applicable in the context of an AI algorithm, as this device is an X-ray detector and associated acquisition software. However, the "standalone" performance of the detector itself (MTF, DQE, sensitivity) was assessed through bench testing and measurements, which can be considered its "standalone" performance.

7. The Type of Ground Truth Used

The "ground truth" for the performance claims (MTF, DQE, sensitivity) is based on physical phantom measurements and engineering specifications obtained through controlled bench testing following recognized industry standards (e.g., IEC 62220-1-1). It is not based on expert consensus, pathology, or outcomes data from patient studies.

8. The Sample Size for the Training Set

Not applicable. This submission is for an X-ray flat panel detector, not an AI/ML model that would require a "training set" of data.

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

Not applicable. As stated above, this device does not involve an AI/ML model with a training set.

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The Wireless (V14C, V14G, F14C, F14G, V17C, V17G)/Wired (V14C, V14G, F14C, F14G, V17C, V17G, V17Ge, V17Ce) Yushan X-Ray Flat Panel Detector is intended to capture for display radiographic images of human anatomy. It is intended for use in general projection radiographic applications wherever conventional film/screen or CR systems may be used. The Yushan X-Ray Flat Panel Detector is not intended for mammography, fluoroscopy, tomography, and angiography applications. The use of this product is not recommended for pregnant women and the risk of radioactivity must be evaluated by a physician.

The subject device Yushan X-Ray Flat Panel Detector, model V14C, V14G, V17C, V17G, F14C, F14G are portable(wireless/wired) detectors, while V17Ce, V17Ge are a non-portable(wired) detector. The Yushan X-Ray Flat Panel Detector is designed to be used in any environment that would typically use a radiographic cassette for examinations. Detectors can be placed in a wall bucky for upright exams, a table bucky for recumbent exams, or removed from the bucky for non-grid or free cassette exams. Additionally, rounded-edge design for easy handling, image compression algorithm for faster image transfer, LED design for easy detector identification, extra protection against ingress of water.

Yushan series is working by using Xresta and DR console.

The Xresta is a software running on a Windows PC as an user interface for radiologist to perform a general radiography exam. The function includes:

• 1. Detector status update
• 2. Xray exposure workflow
• 3. Image viewer and measurement.
• 4. Post image process and DICOM file I/O
• 5. Image database: DROC support the necessary DICOM Services to allow a smooth integration into the clinical network

The DR Console is a software/app-based device, which is a software itself. When this app is operating the OTS can be considered as the iOS system (iOS 16 or above), the safety and effectiveness of this OTS has been assessed and evaluated through the software testing (compatibility) action and also the usability test (summative evaluation). All the functions operate normally and successfully under this OTS framework.

The function includes:

• 1. Imaging procedure review
• 2. Worklist settings
• 3. Detector connection settings
• 4. Calibration
• 5. Image processing

The provided document is a 510(k) summary for the Yushan X-Ray Flat Panel Detector. It outlines the device's technical characteristics and compares it to predicate devices to establish substantial equivalence. However, it does not describe a clinical study that proves the device meets specific acceptance criteria in terms of diagnostic performance (e.g., sensitivity, specificity for a particular condition).

Instead, the document focuses on non-clinical performance data to demonstrate substantial equivalence, primarily by showing that the device adheres to recognized voluntary standards and exhibits comparable physical and image quality characteristics to previously cleared devices.

Here's an analysis based on the provided text, addressing your points where possible:

1. Table of Acceptance Criteria and Reported Device Performance

The document does not explicitly present a table of acceptance criteria for diagnostic performance (e.g., a specific sensitivity or specificity target for a clinical task) and the device's performance against those criteria. It focuses on technical specifications and compliance with standards.

The closest to "acceptance criteria" are the technical specifications listed for the subject device and the predicate devices, implying that meeting or being comparable to these specifications is considered acceptable for substantial equivalence.

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

• Test Set Sample Size: Not applicable in the context of a clinical performance study. The document states: "No clinical study has been performed."
• Data Provenance: Not applicable. The evaluation relies on non-clinical (laboratory/technical) testing.

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

Not applicable, as no clinical study with expert ground truth was performed.

4. Adjudication method for the test set

Not applicable, as no clinical study with a test set requiring adjudication was performed.

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. This device is an X-ray flat panel detector, not an AI software intended to assist human readers.

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

Not applicable. This is a hardware device (X-ray detector) with associated software for image processing and display, not an AI algorithm. Its performance is inherent in its image acquisition capabilities. The "standalone" performance here refers to the detector's physical performance metrics (DQE, MTF, resolution).

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

For the image quality evaluation mentioned: The ground truth implicitly refers to the ideal image quality parameters as defined by industry standards (e.g., IEC standards for DQE, MTF) and the performance of the legally marketed predicate devices. It is not a clinical ground truth for diagnostic accuracy.

8. The sample size for the training set

Not applicable. This device is not an AI/machine learning model that requires a distinct training set in the typical sense. Its "training" would be the engineering and manufacturing processes to ensure it meets its design specifications.

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

Not applicable, as there is no training set in the context of an AI model for this device. The "ground truth" for the device's development would be its design requirements and engineering specifications validated through non-clinical testing.

Summary of the Study that Proves the Device Meets Acceptance Criteria:

The "study" referenced in the document is a compilation of non-clinical performance tests and adherence to voluntary standards.

• Non-clinical Performance Data: The device conforms to voluntary standards such as AAMI/ANSI ES60601-1, IEC 60601-1, IEC 60601-1-2, IEC 62304, IEC 60601-1-6, ANSI AAMI IEC 62366-1, and ANSI/AAMI HE75.
• FDA Guidance adherence: The "FDA's Guidance for the Submission of 510(k)s for Solid State X-ray Imaging Devices" (September 1, 2016) was followed to describe detector characteristics. Guidance documents for software (June 14, 2023) and cybersecurity (September 27, 2023) were also followed.
• Specific Tests Conducted:
  • Risk analysis, verification, and validation activities.
  • Load-bearing characteristics and protection against ingress of water (passed).
  • EMC emission testing (IEC60601-1-2) – results satisfactory.
  • Biocompatibility testing (ISO 10993 series) for materials in contact with patients.
  • Image Quality Evaluation: Confirmed that the image quality of the Yushan X-Ray Flat Panel Detector is substantially equivalent to that of the predicate device. This is the key "performance" study, demonstrating that the new device's images are comparable to those produced by already-cleared devices, implying acceptable clinical usability for its stated indications.

Conclusion stated in the document: Based on these non-clinical studies and comparisons, the manufacturer concluded that the device is "as safe and effective" as the legally marketed predicate devices and does not raise "different questions of safety and effectiveness."

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The Wireless(V14 CLARITY, V14 HC, V17 CLARITY, V17 HC, F14 CLARITY, F14 HC)/Wired(V14 CLARITY, V14 HC, V17 CLARITY, V17 HC, F14 CLARITY, F14 HC, W17 Clarity, W17 HC) PRORAD X-Ray Flat Panel Detector with DROC is intended to capture for display radiographic images of human anatomy. It is intended for use in general projection radiographic applications wherever conventional film/screen or CR systems may be used. The PRORAD X-Ray Flat Panel Detector with DROC is not intended for mammography, tomography, and angiography applications. The use of this product is not recommended for pregnant women and the risk of radioactivity must be evaluated by a physician.

PRORAD X-Ray Flat Panel Detector with DROC is the similar product to the predicate, Yushan X-Ray Flat Panel Detector with DROC, K201528, K210988, K220510. There are 8 models in this submission, V14 Clarity, V14 HC, V17 Clarity, V17 HC, W17 HC, F14 Clarity, F14 HC, W17 Clarity are a portable(wireless)/nonportable(wired) digital detector. The PRORAD X-Ray Flat Panel Detector with DROC is designed to be used in any environment that would typically use a radiographic cassette for examinations. Detectors can be placed in a wall bucky for upright exams, a table bucky for recumbent exams, or removed from the bucky for non-grid or free cassette exams. These medical devices have memory exposure mode, and extended image readout feature. Additionally, rounded-edge design for easy handling, image compression algorithm for faster image transfer, LED design for easy detector identification, extra protection against ingress of water.

The PRORAD X-Ray Flat Panel Detector with DROC sensor can automatically collects x-ray images from an x-ray source. It collects x-rays and digitizes the images for their transfer and display to a computer. The x-ray generator (an integral part of a complete x-ray system), is not part of the submission. The sensor includes a flat panel for x-ray acquisition and digitization and a computer (including proprietary processing software) for processing, annotating and storing x-ray images. The personal computer is not part of this submission.

PRORAD series is working by using DROC. This is a software running on a Windows PC as an user interface for radiologist to perform a general radiography exam. The function include:

1. Detector status update
2. Xray exposure workflow
3. Image viewer and measurement.
4. Post image process and DICOM file I/O
5. Image database: DROC support the necessary DICOM Services to allow a smooth integration into the clinical network

This submission for the PRORAD X-Ray Flat Panel Detector with DROC (K240771) indicates that no new performance data was generated to demonstrate substantial equivalence. Instead, the device is deemed substantially equivalent to its predicate devices (Yushan X-Ray Flat Panel Detector with DROC, K201528, K210988, K220510) due to minimal changes in product name, appearance, and labeling, implying that the performance of the device is identical to the predicates. Therefore, the acceptance criteria and study details would be those established for the predicate devices.

However, based solely on the provided document, which states "no clinical study has been performed" and that "the performance data is the same and need no extra validation," it explicitly does not include a study directly proving the device meets new acceptance criteria. It relies on the substantial equivalence argument, meaning the predicate devices' performance data is referenced implicitly.

Since the document explicitly states "No clinical study has been performed" and "Therefore the performance data is the same and need no extra validation", there isn't a direct study described in this document that proves the device meets (new) acceptance criteria. Instead, the device is deemed substantially equivalent to the predicate devices and thus relies on the predicate devices' performance data. The document does not provide details of any specific acceptance criteria or performance study results for the PRORAD device itself.

However, based on the non-clinical performance data section, it states that "the image quality evaluation confirmed that the image quality of the PRORAD X-Ray Flat Panel Detector with DROC is substantially equivalent to that of the predicate device." While this isn't a detailed study, it implies a comparison.

Here's an attempt to answer the questions based on the limited information regarding this specific device's performance, highlighting the reliance on predicate device information:

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

Based on the provided document, no specific acceptance criteria for the PRORAD X-Ray Flat Panel Detector with DROC itself are listed, nor is new device performance data reported. The document states that the device's technical characteristics, design, functional, and performance characteristics are similar to the predicate devices. The assumption is that the new device meets the same performance characteristics as the predicate. The comparison table of technical characteristics (from page 8-14 of the original document) contains performance-related metrics that are presented as identical or nearly identical between the subject device and the predicates.

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 states, "No clinical study has been performed." For non-clinical tests (e.g., electrical safety, EMC, mechanical, biocompatibility), sample sizes are not specified. The "image quality evaluation" also does not mention a sample size or data provenance. The assessment relies on the inherent similarity of the new device to the previously cleared predicate devices.

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 new clinical study was performed for this 510(k) submission. The document relies on "image quality evaluation" that confirmed substantial equivalence, but details on experts or ground truth establishment for this comparison are not provided.

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

Not applicable, as no new clinical study involving adjudication was performed for this 510(k) submission.

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 digital X-ray flat panel detector, not an AI-assisted diagnostic tool.

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

Not applicable. This device is an imaging hardware component, not an algorithm for standalone diagnostic performance.

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

Not applicable, as no new clinical study requiring ground truth was performed for this 510(k) submission. The "image quality evaluation" for substantial equivalence would likely rely on quantitative technical metrics rather than clinical ground truth for diagnostic accuracy.

8. The sample size for the training set

Not applicable, as no machine learning algorithm development (which would require a training set) is described for this device in the document.

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

Not applicable, as no machine learning algorithm development is described.

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Venu1748V and DRX-LC, as a major imaging component, are supplied to the manufacturers of medical diagnostic X-ray photography systems, and used in conjunction with the medical diagnostic X-ray photography system to image the object to be checked. They are capable of outputting the acquired static images to a processing device after acquisition.

Venu1748V and DRX-LC digital flat panel detector (Hereinafter referred to as Venu1748V and DRX-LC) are digital large-sized X-ray flat panel detector (FPD) with wireless function based on amorphous silicon (a-Si) thin film transistor (TFT) technology. Two models Venu1748V and DRX-LC are totally same except for the model name, trade mark, artwork of the protection film. They using cesium iodide (CsI) scintillator, and employ a 3064×8696 active pixel matrix with a pixel size of 139 u m, providing high-quality radiographic images. Supporting high-speed wireless communication, the equipment can be powered by internal rechargeable battery packs or/and external power charger, making it more flexible and easy to integrate and operate. iRay SDK(include iDetector) is intend to supply API interface for DR system manufacturers. DR system manufacturer control the detector by SDK interface. SDK is not intended to be used directly by other users beside DR system manufacturers. iDetector is a tool software based on iRay FPD(Flat Panel Detector) and SDK(Software Development Kit). It can be used for detector configuration, image acquisition, and calibration. So that users can evaluate the performance of iRay detectors at the first time. Also, iDetector can be used as a demonstration program to learn the process controlling and functionality of iRay Detectors and do assessment at user application developing time. This software is moderate level of concern. iDetector does not support image processing after collection.

This document describes the premarket notification (510(k)) for the iRay Imaging Technology (Haining) Limited Digital Flat Panel Detector (models Venu1748V and DRX-LC).

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

The document does not explicitly state "acceptance criteria" but rather presents a comparison of technological characteristics between the proposed device and a predicate device (K220536). The proposed device meets or improves upon the characteristics of the predicate device.

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 focuses on non-clinical performance testing and comparative analysis against a predicate device. There is no mention of a "test set" in the context of clinical data or human-interpreted image sets for evaluating device diagnostic performance. The studies are non-clinical, evaluating technical specifications of the device itself.

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 reported studies are non-clinical and focus on characterizing device performance parameters (e.g., MTF, DQE, uniformity) rather than diagnostic accuracy based on expert interpretation of images. Therefore, no experts were used to establish ground truth for a diagnostic test set.

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

Not applicable. As no clinical or human-interpreted test set was used, no adjudication method was employed.

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 submission is for a Digital Flat Panel Detector, which is an imaging component. It is not an AI-powered diagnostic device, and therefore, no MRMC study or evaluation of human reader improvement with AI assistance was performed.

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

Not applicable. The device is a Digital Flat Panel Detector, a hardware component for X-ray imaging. Performance is evaluated based on its technical specifications (e.g., MTF, DQE, SNR, uniformity) through non-clinical studies. There is no "algorithm only" performance assessment in the context of diagnostic interpretation.

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

The "ground truth" for the non-clinical studies relies on established scientific and engineering principles for measuring physical parameters of X-ray detectors (e.g., using phantoms, standardized test setups, and calibrated equipment to measure MTF, DQE, SNR, etc.). It does not involve expert consensus, pathology, or outcomes data in a clinical sense.

8. The sample size for the training set

Not applicable. This device is a hardware component and does not utilize a "training set" in the context of machine learning or AI algorithms.

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

Not applicable. As there is no training set for AI, no ground truth needed to be established for it.

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The detector is indicated for digital imaging solution designed for providing general radiographic diagnosis of human anatomy. It is intended to replace radiographic film/screen systems in all general-purpose diagnostic procedures. This product is not intended for mammography applications.

The detector functions by intercepting X-ray photons. Then the scintillator emits visible spectrum photons that illuminate an array of photo detectors (a-Si) that create electrical signals. The electrical signals are then digitally converted to display an image on the monitor.

The detector should be connected to a computer and X-ray generator for transfer of diagnostic images. The functions of the EverestView 4343X are supported by software and the software is of Moderate level of concern. The main function of software is image acquisition and transfer and it doesn't have functions of image post-processing. The detectors can be used for dynamic imaging (fluoroscopy) that is same as Predicate Device.

The provided text does not contain information about acceptance criteria and a study proving the device meets those criteria. The document is a 510(k) summary for an X-ray Flat Panel Detector (EverestView 4343X) and focuses on demonstrating substantial equivalence to a predicate device (CareView 1800RF) based on technological characteristics and safety testing.

It does include a comparison table of technical specifications between the proposed and predicate devices, which could be interpreted as comparative performance metrics, but these are not explicitly presented as acceptance criteria for a study proving clinical effectiveness.

Therefore, I cannot provide a table of acceptance criteria and reported device performance or information regarding specific studies (sample size, data provenance, expert ground truth, adjudication methods, MRMC studies, standalone performance, training set details) as this information is not present in the provided text.

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Allengers Wireless / Wired X-Ray Flat Panel Detectors used with AWS (Acquisition Workstation Software) Synergy DR FDX/ Synergy DR is used to acquire/ Process/ Display/ Store/ Export radiographic images of all body parts using Radiographic techniques. It is intended for use in general radiographic applications wherever a conventional film/screen or CR system is used

Allengers Wireless/ Wired X-Ray Flat Panel Detector is not intended for mammography applications.

Wireless/ Wired X-Ray Flat Panel Detectors used with AWS (Acquisition Workstation Software) Synergy DR FDX/ Synergy DR is substantially equivalent product of its predicate device, Yushan X-Ray Flat Panel Detector with DROC, K201528, K210988, K220510. There are 8 models in this submission G4336RWC, G4336RWG, G4343RWC, G4343RWG, T4336RWC, T4336RWG are portable (wireless) and G4336RWC, G4336RWG, G4343RWC, G4343RWG, T4336RWC, T4336RWG, G4343RG, G4343RC (wired) Digital are non-portable detectors. The Wireless/ Wired X-Ray Flat Panel Detectors is designed to be used in any environment that would typically use a radiographic cassette for examinations. Detectors can be placed in a wall bucky for upright exams, a table bucky for recumbent exams, or removed from the bucky for nongrid or free cassette exams. These medical devices have memory exposure mode, and extended image readout feature. Additionally, rounded-edge design for easy handling, image compression algorithm for faster image transfer, LED design for easy detector identification, extra protection against ingress of water.

This Device is currently indicated for general projection radiographic applications and the scintillator material is using cesium iodide (Csl) or gadolinium oxy sulfide (GOS).

The Wireless/ Wired X-Ray Flat Panel Detectors sensor can automatically collect x-ray from an x-ray source. It collects the x-ray and converts it into digital image and transfers it to Desktop computer / Laptop/ Tablet for image display. The x-ray generator (an integral part of a complete x-ray system), is not part of the submission. The sensor includes a flat panel for x-ray acquisition and digitization and a computer (including proprietary processing software) for processing, annotating and storing x-ray images, the personal computer is not part of this submission.

Wireless/ Wired X-Ray Flat Panel Detectors used with AWS (Acquisition Workstation Software) Synergy DR FDX/ Synergy DR, runs on a Windows based Desktop computer/ Laptop/ Tablet as a user interface for radiologist to perform a general radiography exam. The function includes:

• 1. User Login
• 2. Display Connectivity status of hardware devices like detector
• 3. Patient entry (Manual, Emergency and Worklist)
• 4. Exam entry
• 5. Image processing
• 6. Search patient Data
• 7. Print DICOM Image
• 8. Exit

The provided text outlines the performance data for the "Wireless/Wired X-Ray Flat Panel Detectors used with AWS (Acquisition Workstation Software) Synergy DR FDX/ Synergy DR." However, it does not specifically present a table of acceptance criteria and reported device performance in a quantitative manner (e.g., sensitivity, specificity, accuracy). Instead, it primarily focuses on compliance with recognized standards and guidance documents, emphasizing non-clinical tests.

Here's an analysis of the information provided, addressing your specific points:

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

The document does not provide a quantitative table of acceptance criteria (e.g., specific metrics like DQE values, spatial resolution, or SNR targets) with corresponding reported device performance values. Instead, it states that the device "confirms to the voluntary standards" and that "the image quality evaluation confirmed that the image quality of the Wireless/ Wired X-Ray Flat Panel Detectors is substantially equivalent to that of the predicate device."

The acceptance criteria are implicitly tied to the successful demonstration of compliance with a long list of international and FDA-recognized consensus standards and guidance documents. The reported performance is that the device "Met all requirements" for each of these standards.

Implicit Acceptance Criteria and Reported Performance (based on document content):

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

The document primarily describes non-clinical performance data (bench testing). There is no mention of a specific "test set" with a sample size of patient data. The provenance of testing (e.g., country of origin, retrospective/prospective) for these non-clinical tests is not detailed, beyond being conducted by Allengers Medical Systems Limited and likely by accredited testing labs for compliance with international standards.

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)

This information is not applicable or not provided since no clinical study with a "test set" requiring expert ground truth establishment for diagnostic performance is described. The evaluation focuses on engineering and regulatory compliance.

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

This information is not applicable or not provided as there was no clinical test set requiring expert 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

No MRMC comparative effectiveness study was done or reported. The document explicitly states: "Clinical Performance Data: No clinical study has been performed. The substantial equivalence has been demonstrated by non-clinical studies." Furthermore, this device is an X-ray flat panel detector and associated acquisition software, not an AI-powered diagnostic algorithm designed to assist human readers.

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

This question is not directly applicable in the context of an X-ray detector. The device itself is a component for acquiring images. Its "standalone performance" is assessed through engineering metrics and compliance with imaging standards (e.g., DQE, MTF, noise characteristics), which are part of the non-clinical testing. It's not an "algorithm" in the sense of a standalone diagnostic AI.

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

For the non-clinical tests, the "ground truth" is typically defined by the technical specifications of the standards and test methodologies themselves. For example:

• For IEC 62220-1-1 (DQE), the ground truth is the physical properties of the detector under specific X-ray conditions, measured according to the standard's protocol.
• For biocompatibility (ISO 10993), the ground truth is the absence of adverse biological reactions as determined by standardized in vitro and in vivo tests.
• For safety (IEC 60601-1), the ground truth is meeting the safety limits and design requirements outlined in the standard.

There is no mention of clinical ground truth (expert consensus, pathology, outcomes data) as no clinical studies were performed.

8. The sample size for the training set

Not applicable. This device is a hardware component (X-ray detector) and its associated acquisition software. It is not described as involving a machine learning algorithm that requires a "training set" of data for diagnostic purposes. The software mentioned ([AWS] Synergy DR FDX/ Synergy DR) is for image acquisition, processing, display, and storage, not for AI-driven detection or diagnosis.

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

Not applicable for the reasons stated in point 8.

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The CareView 1800RF detector is indicated for digital imaging solution designed for providing general radiographic diagnosis of human anatomy. It is intended to replace radiographic film/screen systems in all general-purpose diagnostic procedures. This product is not intended for mammography applications.

The CareView 1800RF detector is a class of radiography X-ray flat panel detector that has an imaging area of 434 mm × 434 mm. The detector communicates by a wired connection (Giga-bit Ethernet communication mode).

The detector functions by intercepting X-ray photons. Then the scintillator emits visible spectrum photons that illuminate an array of photo detectors (a-Si) that create electrical signals. The electrical signals are then digitally converted to display an image on the monitor.

The detector should be connected to a computer and X-ray generator to digitize X-ray images and transfer radiography diagnostics. The x-ray generator, an essential part of a full x-ray system, is not part of the subject medical device.

The CareView 1800RF detector can be used for dynamic imaging (fluoroscopy).

The provided document is a 510(k) premarket notification for the CareView 1800RF X-ray Flat Panel Detector. It aims to demonstrate substantial equivalence to a predicate device, CareView 1800Le (K193173), and a reference device, 1717FCC (K210985).

The document states that clinical images are not necessary for the current submission, and that successful results of bench testing should be sufficient to show substantial equivalence for the subject device. This implies that the acceptance criteria are primarily based on non-clinical performance benchmarks and that no clinical study involving human subjects or expert readers was conducted for this submission to prove device performance against acceptance criteria in a clinical context.

Therefore, many of the requested details about acceptance criteria derived from clinical studies, expert involvement, and ground truth establishment cannot be extracted from this document because such studies were explicitly stated as not necessary for this 510(k) submission.

However, the document does provide information regarding the technical specifications of the device and its predicate/reference devices, which implicitly serve as performance benchmarks for substantial equivalence based on bench testing.

Here's a breakdown of the information that can be extracted:

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

Since no clinical study with explicit acceptance criteria for diagnostic performance is provided, the closest relevant information is the comparison of technical specifications between the proposed device and its predicate/reference devices. The "acceptance criteria" here are essentially "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):

Not applicable. The submission explicitly states "Clinical images are not necessary for the current submission. Successful results of bench testing should be sufficient...". Therefore, no clinical test set was used. The substantial equivalence is based on bench testing for electrical safety, EMC, and comparison of technical specifications.

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. No clinical test set or human evaluation study was conducted.

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

Not applicable. No clinical test set.

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. The device is a flat panel detector, not an AI-powered diagnostic tool. Furthermore, no clinical studies were performed.

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

Not applicable. The device is a flat panel detector, not an algorithm, and no clinical studies were done.

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

Not applicable. No clinical studies were done, so no ground truth for diagnostic accuracy was established. The "ground truth" for the technical comparisons would be the specifications and performance characteristics of the predicate and reference devices, as determined by their respective manufacturers and validated through their own testing protocols.

8. The sample size for the training set:

Not applicable. This is a hardware device (X-ray Flat Panel Detector), not an AI algorithm requiring a training set.

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

Not applicable. No training set was used.

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Venul 717X is indicated for digital imaging solutions designed to provide general radiographic diagnosis for human anatomy including both adult and pediatric patients. It is intended to replace film/screen systems in general-purpose diagnostic procedures.

Venu1717X is a cassette-size tethered X-ray flat panel detector based on amorphous silicon thin-film transistor technology. It is designed to provide the high quality radiographic image which contains an active matrix of 3070×3070 with 139um pixel pitch. The scintillator of Venu1717X is CsI(Caesium Iodide). The technology of CsI direct growth reduces the exposure dose and improves the image quality. Since Venu1717X supports multiple trigger modes, it can satisfy both of the general DR system and retrofit DR system.

iRay SDK(include iDetector) is intend to supply API interface for DR system manufacturers.DR system manufacturer control the detector by SDK interface. SDK is not intended to be used directly by other users beside DR system manufacturers. The iRay SDK is unchanged from the predicate device.

The information provided indicates that the iRay Technology Taicang Ltd. Flat Panel Detector (Venu1717X) is a digital imaging solution for general radiographic diagnosis. While the provided text describes the device's technical specifications and non-clinical studies to establish substantial equivalence to a predicate device (Mars1717V-VSI, K201043), it does not contain details about specific acceptance criteria for diagnostic accuracy metrics (like sensitivity or specificity) for a clinical study.

Instead, the provided text focuses on demonstrating substantial equivalence primarily through technical performance characteristics and a "concurrence study" of clinical images.

Here's an attempt to answer your request based only on the provided text, highlighting what is available and what is missing:

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

The document does not explicitly state "acceptance criteria" in terms of diagnostic performance metrics for a clinical study (e.g., sensitivity, specificity, or AUC with target thresholds). It focuses on demonstrating equivalence through technical performance.

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

• Sample Size for Test Set: 30 clinical images.
• Data Provenance: Not specified (e.g., country of origin, retrospective or prospective). The document only states "Clinical images were provided".

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)

The document mentions a "concurrence study" but does not specify the number of experts, their qualifications, or how ground truth was established for the 30 clinical images. The statement "There was no significant difference between the images" implies a qualitative comparison by human readers, but details are missing.

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

The document does not specify any adjudication method for the "concurrence study" of the 30 clinical images.

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, a multi-reader multi-case (MRMC) comparative effectiveness study was not explicitly described. The study mentioned is a "concurrence study of 30 clinical images" comparing the proposed device to a predicate device, which is different from an MRMC study designed to assess reader improvement with AI assistance. The device itself is a Flat Panel Detector, which is hardware for image acquisition, not inherently an AI-driven diagnostic assistance tool.

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

This section is not applicable as the device is a Flat Panel Detector, a hardware component for imaging, not an AI algorithm performing diagnostic tasks in a standalone manner. The software mentioned (iRay SDK, iDetector) are for controlling the detector and integration, not for standalone diagnostic interpretation.

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

For the "concurrence study" of 30 clinical images, the type of "ground truth" and how it was established is not detailed. The study aimed to show "no significant difference" between images of the proposed and predicate device, rather than assessing diagnostic accuracy against an independent ground truth.

8. The sample size for the training set

This information is not applicable as the description refers to a medical imaging device (Flat Panel Detector) and its associated control software, not an AI model that would typically have a "training set" in the context of machine learning.

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

This information is not applicable for the same reasons as #8.

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Luna 1012X wireless digital flat panel detector is indicated for digital imaging solutions designed to provide general radiographic diagnosis for human anatomy including both adult and pediatric patients. It is intended to replace film/screen systems in all general-purpose diagnostic procedures. The device is not intended for mammography or dental applications.

Luna1012X Wireless Digital Flat Panel Detector (Hereinafter referred to as Luna1012X) is the kind of wireless digital flat panel detector. It supports the single frame mode, with the key component of TFT/PD image sensor flat panel of active area: 31.52cm×25.02cm. The sensor plate of Luna1012X is direct-deposited with CsI scintillator to achieve the conversion from X-ray to visible photon. The visible photons are transformed to electron signals by diode capacitor array within TFT panel, which are composed and processed by connecting to scanning and readout electronics, consequently to form a panel image by transmitting to PC through the user interface. The major function of the Luna1012X is to convert the X-ray to digital image, with the application of high resolution X-ray imaging. Both kinds of detectors are the key component of DR system, enable to complete the digitalization of the medical X-ray imaging with the DR system software. iRay SDK(include iDetector) is intended to supply API interface for DR system manufacturers. DR system manufacturer control the detector by SDK interface. SDK is not intend to be used directly by other users beside DR system manufacturers.

This document is a 510(k) Summary of Safety and Effectiveness for the iRay Technology Taicang Ltd. Wireless Digital Flat Panel Detector (Luna 1012X). It states that the device is substantially equivalent to a predicate device and provides information on its intended use, technological characteristics, and non-clinical testing.

However, the provided text does not contain information about acceptance criteria or a study proving that the device meets those criteria, specifically regarding AI/algorithm performance. The document is a regulatory submission focused on proving substantial equivalence to a predicate device based primarily on hardware specifications, material safety, electrical safety, and general performance parameters relevant to an X-ray detector. It is not an AI/algorithm performance study.

Therefore, I cannot extract the detailed information requested in the prompt (acceptance criteria for an AI algorithm, sample sizes, expert qualifications, adjudication methods, MRMC studies, standalone performance, ground truth types, or training set details) from the provided text.

The document discusses the following:

• Device Name: Wireless Digital Flat Panel Detector (Luna 1012X)
• Intended Use: Digital imaging solutions for general radiographic diagnosis of human anatomy (adult and pediatric), replacing film/screen systems. Not for mammography or dental applications.
• Predicate Device: iRay Technology Co., Ltd. Mars1013X Wireless Digital Flat Panel Detector (K220668)
• Testing: Electrical safety and EMC testing (IEC/ES 60601-1, IEC 60601-2-54, IEC 60601-1-2), Biological Evaluation (ISO 10993-1).
• Performance Parameters Mentioned (for substantial equivalence comparison, not acceptance criteria for an AI): Spatial Resolution (Min. 4.3lp/mm), Modulation Transfer Function (MTF) (Min. 0.60 at 1 lp/mm), Detective Quantum Efficiency (DQE) (Min. 0.43 at 1 lp/mm). These are standard technical specifications for an X-ray detector, not for an AI.
• Software Mentioned: iRay SDK (including iDetector) as an API interface for DR system manufacturers, not an AI software for image interpretation.

Without information about an AI or algorithm in the provided text, I cannot fulfill the request.

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