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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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The flat panel detector when used with a radiographic imaging system is intended to generate radiographic images of human anatomy wherever a conventional screen-film, digital radiography (DR) or computed radiography (CR) detector is used for general purposes.

When the dual energy subtraction is enabled, it is intended to assist the physician through the visualization of anomalies by reducing the visibility of underlying or overlying anatomical structures.

This device is not intended for use in mammography applications.

The Reveal 35C Flat Panel Detector is similar to the FDA cleared Yushan X-ray Flat Panel Detector. The detectors consist of amorphous silicon flat panel image sensors with cesium iodide scintillators. The light is captured by an amorphous silicon photodetector and the resulting signal is transferred via amorphous silicon thin film transistor (TFT) switches to external readout electronics to obtain X-ray images. The Reveal 35C Flat Panel Detector is a portable digital detector that can be integrated with a PC workstation and an X-ray source to acquire digital X-Ray images for general radiography. The detector supports wireless and wired data communication and can be used wherever a conventional screen-film, digital radiography, or computed radiography detector is used for general purposes.

The Reveal 35C Flat Panel Detector synchronize their image capture cycle with the X-Ray exposure in either of the two modes:

• 1. Wired Mode
• Wireless Mode 2.

The subject device, Reveal 35C Flat Panel Detector includes an optional Dual-Energy subtraction function. When the Dual-Energy Subtraction function is enabled, it will provide additional dual energy subtracted X-ray images. The images are intended to assist the physician through the visualization of anomalies by reducing the visibility of underlying or overlying anatomical structures.

Here's a summary of the acceptance criteria and study information for the KA Imaging Reveal 35C Flat Panel Detector, based on the provided text:

Acceptance Criteria and Reported Device Performance

The provided text does not contain a specific table of acceptance criteria or quantitative performance metrics for the Reveal 35C Flat Panel Detector. Instead, it focuses on demonstrating substantial equivalence to predicate devices through technical characteristics, design features, operating principles, functional and performance characteristics, and intended uses.

The text states that:

• Non-clinical bench testing has determined that the device hardware and software requirements conform to its specification. (A general statement of performance.)
• The image quality validation confirmed that the image quality of KA Imaging Reveal 35C Flat Panel Detector is substantially equivalent to that of the predicate device. (A comparative statement.)

Therefore, a table cannot be constructed with specific numerical acceptance criteria and reported device performance directly from this document. The "acceptance criteria" here is implicitly that the device performs equivalently to the listed predicate devices in its general function and image quality.

Study Information:

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

   • The document does not specify the sample size used for any test set or the data provenance (e.g., country of origin, retrospective/prospective). It mentions "non-clinical bench testing" and "image quality validation" but no details on patient data or specific test sets for clinical performance.

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

   • This information is not provided in the document.

3. Adjudication Method for the Test Set:

   • This information is not provided in the document.

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

   • An MRMC comparative effectiveness study is not mentioned in the document. There is no information about human readers improving with or without AI assistance, as the focus is on the device's technical equivalence rather than a clinical reader study of the dual-energy subtraction feature.

5. Standalone (Algorithm Only Without Human-in-the-Loop Performance) Study:

   • The document implies that the device's dual-energy subtraction function produces images (soft-tissue and bone) that are "equivalent to those of reference predicate devices (K122454 and K013481)." This suggests a standalone comparison of the image output itself, but details of a formal standalone performance study are not explicitly described. The text states the images are "intended to assist the physician," but doesn't quantify this assistance through a standalone algorithm performance metric.

6. Type of Ground Truth Used:

   • The document does not explicitly state the type of ground truth used for any evaluations. Given the focus on image quality equivalence and the absence of clinical outcome studies, it's likely that technical image quality metrics and visual comparison to predicate devices' outputs formed the basis of "validation," but specific ground truth types (e.g., pathology, clinical outcomes) are not mentioned.

7. Sample Size for the Training Set:

   • The document does not provide any information regarding the sample size for a training set. This is not uncommon for 510(k) submissions that focus on device equivalence rather than novel AI/ML algorithm performance where extensive training data details are typically required.

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

   • Since no training set is mentioned, this information is not provided.

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The flat panel detector CSX-20 is designed to provide fluoroscopic and spot radiographic images of human anatomy during diagnostic, surgical and interventional procedures. Examples of clinical application may include angiography, endoscopy, urologic, orthopedic, neurologic, vascular, cardiac, critical-care and emergency room procedures or other-imaging applications at the physician's discretion. The device is intended to replace the spot-film devices. The device is also intended to replace fluoroscopic images obtained through image intensifier technology. Not intended for mammography applications.

The Canon CSX-20 is a digital radiography flat panel detector that can take fluoroscopic and spot radiographic images of any part of the body. It directly converts the X-ray images captured by the sensor into high-resolution digital images. The instrument is a component of an x-ray system and as such cannot be used outside of such a system. This unit converts the X-rays into digital signals. Not intended for mammography applications.

The Canon CSX-20 is a digital radiography flat panel detector for fluoroscopic and spot radiographic images. The acceptance criteria and the study that proves the device meets the acceptance criteria are described below:

1. Table of Acceptance Criteria and Reported Device Performance

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

The document does not explicitly state a specific "test set" in terms of clinical images or patient data that would have a sample size. The testing appears to be primarily device performance testing and refers to "non-clinical/test data" and "performance testing and software validation." This suggests the test set would consist of technical measurements and functional evaluations of the device itself rather than patient data.

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

Given the nature of the testing described (performance testing, software validation, electrical safety, EMC), there is no mention of a "ground truth" derived from human expert evaluation of images. The ground truth for this type of device would generally be established by engineering and technical standards.

4. Adjudication Method for the Test Set

Not applicable, as the testing described focuses on technical and performance metrics of the device rather than clinical interpretation by multiple experts.

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

No MRMC study was performed or is mentioned. The submission focuses on demonstrating substantial equivalence to a predicate device through technical improvements and performance testing of the device itself. There is no comparison of human reader performance with or without AI assistance.

6. Standalone Performance

The information provided describes the performance of the device (CSX-20 Flat Panel Detector) itself, rather than an algorithm. The device "directly converts the X-ray images captured by the sensor into high-resolution digital images." Its performance metrics (A/D conversion, grayscale, frame rate) relate to its standalone function as an imaging component.

7. Type of Ground Truth Used

The ground truth used for this submission is based on:

• Engineering specifications and standards: For A/D conversion, S/N, grayscale gradations, and frame rate.
• Regulatory standards: For electrical safety, electromagnetic compatibility, and compliance with the US Performance Standard for radiographic equipment.
• Functional equivalence: Comparison to the predicate device (CSX-10) based on intended use, principles of operation, and functional design.

8. Sample Size for the Training Set

Not applicable. This device is a hardware component (digital X-ray detector). The concept of a "training set" typically applies to machine learning algorithms, which are not described as part of this device. The improvements are described as hardware modifications (A/D conversion).

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

Not applicable, as there is no "training set" or machine learning algorithm described. The improvements are based on hardware modifications and engineering design.

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The FDR D-EVO flat panel detector system 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 FDR D-EVO is not intended for mammography, fluoroscopy, tomography, and angiography applications.

FDR D-EVO flat panel detector is an indirect-conversion amorphous silicon (a-Si) portable flat panel detector (FPD) utilizing GOS (Gadolinium OxySulfide) as a schifillator. FDR D-EVO detector has Fuji's unique Irradiation Side Sampling system, delivering high image quality. D-EVO's 14x17" standard cassette size affords it the ability for use as a retrofit in any analog bucky and/or as an additional panel with fixed digital radiography systems, allowing a quick conversion to digital X-ray technology. The D-EVO features a detachable power source that enables easy positioning within the radiographic room within an upright bucky, table or as a free cassette. Data captured via operator console is sent electronically to the Fujifilm FDX Console to be displayed on the peraitor.

Here's a breakdown of the acceptance criteria and study details for the Fujifilm FDR D-EVO Flat Panel Detector, based on the provided text:

1. Acceptance Criteria and Reported Device Performance

2. Sample Size and Data Provenance

• Test Set Sample Size: 30 image pairs (comparing FDR D-EVO with Fujifilm Carbon XL-2).
• Data Provenance: Not explicitly stated, but the submission is from Fujifilm Medical Systems, USA, suggesting the study was likely conducted in the USA or with data relevant to the US market. The mention of "internal and international IEC testing requirements" suggests broader applicability, but the image quality reader study specifically compared against a cleared Fujifilm device, implying a focused internal validation. It is a retrospective study, as pre-existing images were used for comparison.

3. Number and Qualifications of Experts for Ground Truth

• Number of Experts: Three.
• Qualifications of Experts: Board-certified radiologists.

4. Adjudication Method

The document does not explicitly describe an adjudication method. It states that all images were deemed of diagnostic capability by the reviewers, and all three radiologists individually met the success criteria with 100% of their scores. This suggests individual assessment rather than a formal adjudication process between the experts to establish a single ground truth score per image.

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

While a reader study was performed with 3 board-certified radiologists, the document does not describe an MRMC comparative effectiveness study in terms of measuring reader improvement with AI vs. without AI assistance. The study directly compares the image quality of the FDR D-EVO system against a predicate device (Fujifilm Carbon XL-2) based on radiologists' scores. It doesn't involve AI assistance to human readers for a comparative effectiveness analysis.

6. Standalone (Algorithm Only) Performance Study

The document describes an "image quality reader study" where radiologists reviewed images. This is not a standalone (algorithm only) performance study. The performance is assessed by human interpretation of the images generated by the device.

7. Type of Ground Truth Used

The ground truth for the test set was established by expert consensus (or individual expert assessment, implicitly leading to a consensus that images were of diagnostic quality) of board-certified radiologists judging the "diagnostic capability" of the images and assigning scores.

8. Sample Size for the Training Set

The document does not provide information on the sample size used for the training set. The FDR D-EVO is a flat panel detector, not an AI algorithm in the context of this 510(k) submission. Therefore, it does not typically involve a "training set" in the same way an AI/ML algorithm would. The product description focuses on its physical and technical components and capabilities rather than learned predictive models.

9. How Ground Truth for Training Set Was Established

As mentioned above, the concept of a "training set" with established ground truth is not applicable to this device as described. The device is a hardware component for capturing radiographic images.

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