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Lux HD 35 Detector and Lux HD 43 Detector are indicated for digital imaging solutions designed to provide general radiographic diagnosis for human anatomy including both adult and pediatric patients. They are intended to replace film/screen systems in all general-purpose diagnostic procedures. Lux HD 35 Detector and Lux HD 43 Detector are not intended for mammography or dental applications.

Lux HD 35 Detector and Lux HD 43 Detector are digital flat panel detector. They support the single frame mode, with the key component of TFT/PD image sensor flat panel of active area: 35cm×43cm (Lux HD 35 Detector)/42.67cm × 42.67cm (Lux HD 43 Detector) .The differences between two models are overall change in the dimensions of the image receptor. The sensor plate of Lux HD 35 Detector and Lux HD 43 Detector 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 Lux HD 35 Detector and Lux HD 43 Detector 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. The Digital Radiographic Imaging Acquisition Software Platform - DR is part of the system, it is used to acquire, enhance, view image from Lux HD 35 Detector and Lux HD 43 Detector.

The provided text is a 510(k) summary for a medical device (Lux HD 35 Detector and Lux HD 43 Detector). It details the device's characteristics, intended use, and comparison to a predicate device to demonstrate substantial equivalence.

However, the provided document does not contain information about the acceptance criteria nor a study that proves the device meets specific performance criteria through a clinical evaluation involving human readers, ground truth establishment, or sample sizes related to AI/algorithm performance.

The "Non-clinical study" section primarily discusses:

• Electrical Safety and EMC testing: Adherence to IEC/ES 60601-1 and IEC 60601-1-2.
• Biological Evaluation: Evaluation of materials contacting operators/patients based on FDA guidance.
• Non-clinical Considerations: Stating substantial equivalence to the predicate device for non-clinical aspects mentioned in FDA guidance for solid-state X-ray imaging devices.
• Clinical Consideration: Stating that intended use, fundamental scientific technology, regulatory requirements, non-clinical performance, labeling, and quality-assurance programs are the same as the predicate device. It explicitly mentions: "There is no any negative change about clinical performance from predicate device."
• Wireless testing: Compliance with ANSI IEEE C63.27-2017.
• Cybersecurity testing: Compliance with section 524B(b)(2) of the Federal Food, Drug, and Cosmetics Act.

This submission clearly relies on demonstrating substantial equivalence to a predicate device (Carestream Health, Inc. Focus HD 43 Detector, K213529) through technical and safety comparisons, rather than presenting a performance study with acceptance criteria and results for an AI/algorithm.

Therefore, I cannot populate the table or answer the specific questions about "acceptance criteria and the study that proves the device meets the acceptance criteria" as they pertain to AI/algorithm performance, ground truth, expert adjudication, or MRMC studies. The document does not describe such a study.

The crucial information regarding acceptance criteria and performance data for an AI/algorithm, as requested in your prompt, is absent from the provided text. The document is a 510(k) a summary for a hardware device (X-ray detector) and focuses on the safety and performance equivalency to a predicate hardware device. It does not provide data on AI/algorithm performance against a clinical ground truth.

If this device were to include an AI component that required such a study for its clearance, that information would typically be detailed in a separate section of the 510(k) submission, outlining the AI's intended use, performance metrics, validation strategy, and results against a defined ground truth. This document does not contain that.

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This software is intended to generate digital radiographic images of the skull, spinal column, extremities, and other body parts in patients of all ages. Applications can be performed with the patient sitting, or lying in the prone or supine position and is intended for use in all routine radiography exams. The product is not intended for mammographic applications.

This software is not meant for mammography, fluoroscopy, or angiography.

The I-Q View is a software package to be used with FDA cleared solid-state imaging receptors. It functions as a diagnostic x-ray image acquisition platform and allows these images to be transferred to hard copy, softcopy, and archive devices via DICOM protocol. The flat panel detector is not part of this submission. In the I-Q View software, the Digital Radiography Operator Console (DROC) software allows the following functions:

• 1. Add new patients to the system; enter information about the patient and physician that will be associated with the digital radiographic images.
• 2. Edit existing patient information.
• 3. Emergency registration and edit Emergency settings.
• 4. Pick from a selection of procedures, which defines the series of images to be acquired.
• 5. Adiust technique settings before capturing the x-ray image.
• 6. Preview the image, accept or reject the image entering comments or rejection reasons to the image. Accepted images will be sent to the selected output destinations.
• 7. Save an incomplete procedure, for which the rest of the exposures will be made at a later time.
• 8. Close a procedure when all images have been captured.
• 9. Review History images, resend and reprint images.
• 10. Re-exam a completed patient.
• 11. Protect patient records from being deleted by the system.
• 12. Delete an examined Study with all images being captured.
• 13. Edit User accounts.
• 14. Check statistical information.
• 15. Image QC.
• 16. Image stitching.
• 17. Provides electronic transfer of medical image data between medical devices.

The provided document is a 510(k) summary for the I-Q View software. It focuses on demonstrating substantial equivalence to a predicate device through bench testing and comparison of technical characteristics. It explicitly states that clinical testing was not required or performed.

Therefore, I cannot provide details on clinical acceptance criteria or a study proving the device meets them, as such a study was not conducted for this submission. The document relies on bench testing and comparison to a predicate device to establish substantial equivalence.

Here's a breakdown of what can be extracted from the provided text regarding acceptance criteria and the "study" (bench testing) that supports the device:

1. Table of Acceptance Criteria and Reported Device Performance

Since no clinical acceptance criteria or performance metrics are provided, this table will reflect the general statements made about the device performing to specifications.

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

• Test Set Sample Size: Not applicable/not reported. The document describes bench testing, not a test set of patient data.
• Data Provenance: Not applicable. Bench testing generally involves internal testing environments rather than patient data from specific countries or retrospective/prospective studies.

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 test set was used, no experts were needed to establish ground truth for patient data. Bench testing typically relies on engineering specifications and verification.

4. Adjudication method for the test set

• Not applicable. No clinical test set or human interpretation was involved.

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, an MRMC comparative effectiveness study was not done. The document explicitly states: "Clinical Testing: The bench testing is significant enough to demonstrate that the I-Q View software is as good as the predicate software. All features and functionality have been tested and all specifications have been met. Therefore, it is our conclusion that clinical testing is not required to show substantial equivalence." The device is software for image acquisition, not an AI-assisted diagnostic tool.

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

• Yes, in a sense. The "study" described is bench testing of the software's functionality and its integration with solid-state detectors. This is an evaluation of the algorithm/software itself.

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

• For bench testing, the "ground truth" would be the engineering specifications and expected functional behavior of the software and its interaction with hardware components. It's about verifying that the software performs according to its design requirements.

8. The sample size for the training set

• Not applicable. The I-Q View is described as an image acquisition and processing software, not an AI/machine learning model that typically requires a training set of data.

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

• Not applicable, as there is no mention of a training set or AI/machine learning component.

Summary of the "Study" (Bench Testing) for K203703:

The "study" conducted for the I-Q View software was bench testing. This involved:

• Verification and validation of the software.
• Demonstrating the intended functions and relative performance of the software.
• Integration testing to verify that compatible solid-state detectors performed within specification as intended when used with the I-Q View software.

The conclusion drawn from this bench testing was that the software performs to specification and is "as safe and as functionally effective as the predicate software." This was deemed sufficient to demonstrate substantial equivalence, and clinical testing was explicitly stated as not required.

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The purpose of Acuity is to acquire, store, communicate, display and process medical X-ray images. These radiographic systems are intended for use by a qualified/trained physician or technician on both adult and pediatric subjects for taking diagnostic x-rays. Not for mammography, angiography, interventional, or fluoroscopy use.

RadmediX Acuity combines components into a complete stationary x-ray system, including software, tube stands, tube heads, collimators, generators, tables, and digital radiography panels.

Here's a breakdown of the acceptance criteria and the study information based on the provided text:

Acceptance Criteria and Device Performance:

The document describes the Acuity SDR Standard, Acuity SDR Plus, and Acuity FDR Standard as stationary x-ray systems. The core claim for substantial equivalence is that the device performs the same functions using the same technological methods as the predicate device (Visaris Vision®) to produce diagnostic x-ray images.

Study Information:

1. Sample Size and Data Provenance:

   • Test Set Sample Size: Not explicitly stated as a numerical sample size of clinical cases for a formal clinical trial. Instead, the non-clinical testing involved acquiring DICOM images from "all major body structures" using one AcuitySDR system configuration.
   • Data Provenance: The document does not specify the country of origin for the image acquisition or whether it was retrospective or prospective. It was a "field tested" system, implying real-world or simulated real-world scenarios.

2. Number of Experts and Qualifications (for Ground Truth):

   • Number of Experts: Not specified.
   • Qualifications of Experts: Not specified, beyond the statement that images were generated for "diagnostic x-rays" and assessed as "clinically acceptable." This implies evaluation by trained personnel (e.g., radiologic technologists, radiologists), but their specific qualifications or number are not detailed.

3. Adjudication Method:

   • No formal adjudication method is described. The assessment of image quality was based on whether images were "of high quality and contrast and clinically acceptable."

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

   • No MRMC comparative effectiveness study was performed or described. The submission focuses on demonstrating substantial equivalence through technical characteristics and non-clinical image quality assessment, not on human reader performance improvement with or without AI assistance.

5. Standalone Performance Study (Algorithm Only):

   • Yes, a standalone study (non-clinical testing) was performed. The algorithm (software) alongside the hardware components was tested to acquire and process images. The output (DICOM images) was then evaluated for inherent quality and acceptability. The assessment "all images were of high quality and contrast and clinically acceptable" refers to the output of the integrated system.

6. Type of Ground Truth Used:

   • Expert Consensus / Clinical Acceptability: The ground truth for image quality was established through subjective expert assessment of "high quality and contrast and clinically acceptable." It does not mention pathology or outcomes data.

7. Training Set Sample Size:

   • Not applicable. This device is an X-ray system, not an AI/ML algorithm that requires a training set in the typical sense for image interpretation or diagnosis. The "software" components mentioned (AccuVueMED, AccuVue) are image acquisition and processing software, which are generally deterministic and not "trained" on data in the way a diagnostic AI would be. They are "previously cleared software supplied with the system."

8. How Ground Truth for Training Set Was Established:

   • Not applicable, as there is no specific "training set" for an AI/ML algorithm in this submission. The software components were previously cleared by the FDA, implying their performance was established through other means at the time of their original clearance (K152172 and K141440).

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The DigiX FDX radiographic systems are used in hospitals, clinics and medical practices. DigiX FDX enables radiographic exposure of the whole body including: Skull, chest, abdomen, and extremities and may be used on pediatric, adult and bariatic patients. It can also be used for intravenous, small interventions (like biopsy, punctures, etc.) and emergency (trauma critical ill) applications. Exposure may be taken with the Patient's sitting, standing, or in the prone/ supine position.

The DigiX FDX System is not meant for mammography.

The DigiX FDX uses an integrated or portable or fixed or wi-fi digital detector for generating diagnostic images by converting X-Ray into electronics signals. DigiX FDX is also designed to be used with conventional film/screen or Computed Radiography (CR) Cassettes.

The DigiX FDX system is a diagnostic X-Ray system intended for general purpose radiographic imaging of the human body. It is not intended for mammographic imaqing.

The DigiX FDX system is comprised of a combination of devices that include a ceiling mounted X-Ray tube suspension, vertical Bucky stand, fixed or mobile patient Bucky table, X-Ray generator, X-Ray tube, beam limiting device, and a solid-state image receptor.

The DigiX FDX systems are not intended to be operated with any other cleared devices, or to be integrated with other software/hardware devices via direct or indirect connections.

Here's a breakdown of the acceptance criteria and study information based on the provided document:

1. Table of Acceptance Criteria and Reported Device Performance

The document focuses on demonstrating substantial equivalence to predicate devices rather than defining specific quantitative performance metrics as "acceptance criteria" for a new AI/software feature in the way clinical performance studies usually do. Instead, the "acceptance criteria" are implied to be that the modifications do not negatively impact safety or effectiveness, and that the device performs comparably to the predicate devices and meets relevant safety standards.

For the new features explicitly mentioned (Automatic Stitching and Dual Energy Subtraction), the document states they add functionality without affecting patient safety or effectiveness. For the other components, the criteria are often "Same" or "Similar functionality with same imaging results" or "doesn't affect the safety or effectiveness."

To represent this in a table, we'll extract performance comparisons from the "Functional and Specification Differences" table (Table 4) and the "Justification for Differences" (Table 5).

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

The document states:

• "Non-clinical testing included verification and validation testing, image evaluation, testing, and safety testing."
• "Performance testing included functional testing of all motions of the system(s) with respect to the design specifications. Image performance testing was conducted and results included in the submission."
• "Clinical testing is not applicable due to the fact that no new clinical applications were introduced to the system."

The document does NOT specify a sample size for any test set (clinical or otherwise) in terms of patient data or images used for validation of the radiographic system itself, nor does it mention data provenance (country of origin, retrospective/prospective). It primarily relies on hardware and software equivalence, and compliance with industry standards.

3. Number of Experts Used to Establish Ground Truth and Qualifications

Not applicable. The document explicitly states "Clinical testing is not applicable." Therefore, there was no clinical study conducted that would necessitate expert readers to establish ground truth for a test set. The evaluation focuses on technical performance and equivalence to predicate devices.

4. Adjudication Method for the Test Set

Not applicable. As no clinical test set requiring expert adjudication was performed, no adjudication method is mentioned.

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

No MRMC comparative effectiveness study was done. The document states "Clinical testing is not applicable due to the fact that no new clinical applications were introduced to the system." Therefore, there is no information on how much human readers improve with or without AI assistance. The new software features (Automatic Stitching, Dual Energy Subtraction) are presented as additional functionalities that don't affect safety or effectiveness, not as AI-assisted diagnostic tools requiring a comparative effectiveness study.

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

Not applicable. The device is a radiographic system, not an AI algorithm intended for standalone diagnostic performance. While it includes new software features (Automatic Stitching, Dual Energy Subtraction), these are integrated functionalities of the imaging system and not described as standalone diagnostic algorithms requiring independent performance evaluation without human interaction.

7. Type of Ground Truth Used

The "ground truth" for the evaluation is primarily based on:

• Compliance with technical specifications and design requirements: Functional testing, image performance testing.
• Adherence to safety and performance standards: IEC 60601 series, EN ISO 14971, IEC 62366-1, IEC 62304, 21 CFR 1020.30, 21 CFR 1020.31.
• Substantial equivalence to predicate devices: Demonstration that the new device has the same intended use, fundamental technological characteristics, and that any differences do not raise new questions of safety or effectiveness.
• Software documentation assessment: For the software components, including the new features, documentation was provided for a "Moderate Level of Concern" software as per FDA guidance.

No clinical ground truth (e.g., expert consensus, pathology, outcomes data) was used or required given the nature of this submission.

8. Sample Size for the Training Set

Not applicable. The document describes an X-ray imaging system, not an AI/ML device that requires a training set of data. The new software features (Automatic Stitching, Dual Energy Subtraction) are described as functionalities, not adaptive algorithms that learn from data.

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

Not applicable. As there was no training set, there was no ground truth to establish for it.

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Mars1417XF-GSI Wireless Digital Flat Panel 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 device is not intended for mammography or dental applications.

Mars1417XF-GSI Wireless Digital Flat Panel Detector is a 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: 35.04cm×42.54cm. The sensor plate of Mars1417XF-GSI Wireless Digital Flat Panel Detector is direct-deposited with Gd2O2S 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 Mars1417XF-GSI Wireless Digital Flat Panel Detector is to convert the X-ray to digital image, with the application of high resolution X-ray imaging. This detector is the key component of DR system, enables to complete the digitalization of the medical X-ray imaging with the DR system software.

The provided document is a 510(k) summary for a medical device called the "Wireless Digital Flat Panel Detector" (Model: Mars1417XF-GSI). This document primarily focuses on demonstrating substantial equivalence to a predicate device (Mars1417V-PSI, K161730) rather than presenting a detailed study with acceptance criteria for an AI/CAD algorithm.

The device itself is a digital X-ray detector, not an AI or CAD system that provides diagnostic assistance. Therefore, many of the requested criteria such as "effect size of how much human readers improve with AI vs without AI assistance" or "adjudication method" for establishing ground truth for an AI algorithm are not applicable to the information provided.

However, I can extract information related to the technical performance of the device, which can be seen as acceptance criteria for its physical characteristics and image quality, and how it was tested.

Here's a breakdown based on the information available:

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

The document doesn't explicitly state "acceptance criteria" in a typical clinical study sense for an AI device. Instead, it compares the technical characteristics of the proposed device to its predicate device to demonstrate substantial equivalence. These technical characteristics can be considered as performance metrics that, when similar or improved, demonstrate "acceptance" in the context of a 510(k) submission.

Note: The "acceptance criteria" for these are generally that the proposed device performs comparably or better, demonstrating that the changes do not negatively impact safety or effectiveness. For some parameters, like ADC Digitization, the proposed device shows an improvement (16 bit vs 14 bit).

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

The document describes non-clinical studies focused on the physical and performance attributes of the detector itself, not on analyzing patient data with an algorithm. Therefore, there isn't a "test set" of patient images in the context of an AI algorithm evaluation.

The non-clinical studies performed include:

• Electrical Safety and EMC testing (IEC/ES 60601-1, IEC 60601-1-2)
• Biological Evaluation (ISO 10993-1)
• Evaluation of detector characteristics: Detective quantum efficiency (DQE), Quantum limited performance, Modulation transfer function (MTF), Effects of aliasing, Sensitivity linearity, Lag, Change in detection sensitivity, Dose requirement and reciprocity changes, Stability of device characteristics with time, Uniformity of device characteristic, Noise power spectrum (NPS), Spatial resolution, Image Acquisition time, and Black level.

The "sample size" for these technical tests would refer to the number of devices or components tested, which is not specified but is typically a small number for device verification. The data provenance would be laboratory testing data, not patient data from specific countries.

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

Not applicable, as this is not an AI/CAD device being evaluated on clinical images by human experts. The "ground truth" for the technical performance metrics (e.g., DQE, MTF) is established through standardized physical measurements and calculations.

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

Not applicable, as there is 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:

Not applicable. The device is a digital X-ray detector, not an AI or CAD system designed to assist human readers.

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

Not applicable. The device itself is the "standalone" component in the sense that it converts X-rays to digital images without an AI algorithm for diagnostic interpretation. The document explicitly states that the device is a "key component of DR system, enables to complete the digitalization of the medical X-ray imaging with the DR system software."

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

For the evaluation of the detector's image quality and physical performance, the "ground truth" is established through physical measurements and calculations using standardized methods (e.g., for DQE, MTF, spatial resolution). These are objective technical metrics, not clinical ground truth like pathology or expert consensus.

8. The sample size for the training set:

Not applicable. This device is an X-ray detector, not a machine learning model that requires a training set. The "software" mentioned (iRay DR and iRay SDK) are for device control, image acquisition, and processing, not for AI-based interpretation.

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

Not applicable (see point 8).

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