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The PRORAD ATLAS ULTRAPORTABLE Digital X-ray system is intended to deliver high-quality, diagnostic radiographic images of the body extremities. It utilizes a portable X-ray unit, flat-panel detector and image acquisition software to produce clear digital images, enabling fast and accurate diagnosis. The portable X-ray unit is intended to be used only when stand/tripod mounted.

The PRORAD ATLAS ULTRAPORTABLE X-ray digital system is predominantly employed in various settings, including health-care centres, temporary and emergency health centres (established, especially in pandemic circumstances), outreach and field interventions (such as mobile clinics/vans, screening campaigns, and home care), and tele-radiology solutions in remote areas.

The primary users anticipated for the system include radiographers, radiological technologists, and medical professionals who are trained in safety, radiation protection, and image management.

The PRORAD ATLAS ULTRAPORTABLE PLUS Digital X-ray system is intended to deliver high-quality, diagnostic radiographic images of the body extremities. It utilizes a portable X-ray Unit, flat-panel detector and real-time image processing using software to produce clear digital images, enabling fast and accurate diagnosis. The portable X-ray unit is intended to be used only when stand/tripod mounted.

The PRORAD ATLAS ULTRAPORTABLE PLUS X-ray digital system is predominantly employed in various settings, including health-care centres, temporary and emergency health centres (established, especially in pandemic circumstances), outreach and field interventions (such as mobile clinics/vans, screening campaigns, and home care), and tele-radiology solutions in remote areas.

The primary users anticipated for the system include radiographers, radiological technologists, and medical professionals who are trained in safety, radiation protection, and image management.

The PRORAD ATLAS X-Ray system includes the ULTRAPORTABLE and ULTRAPORTABLE PLUS, which are portable diagnostic X-ray systems with fixed 70kV and 2mA tube current. These systems are intended to produce anatomical X-rays of the body extremities in both pediatric and adult patients. The PRORAD ATLAS X-Ray system was designed, developed, and manufactured by Prognosys Medical Systems Private Limited. The model numbers are listed below.

The PRORAD ATLAS X-ray system is a sophisticated, battery-powered X-ray generator offered in two versions: PRORAD ATLAS ULTRAPORTABLE and ULTRAPORTABLE PLUS. The main distinction between these models lies in their exposure time ranges and target anatomical areas. The ULTRAPORTABLE model provides exposure times ranging from 0.01 to 1.30 seconds, while the ULTRAPORTABLE PLUS model offers an extended exposure range of 0.01 to 2.5 seconds. Both models share identical internal components, software, algorithms, and operational features and are intended for imaging body extremities. The system includes a high-voltage tank with an X-ray tube mounted on an adjustable tripod stand, allowing users to adjust the height to the specific imaging area. Exposure parameters are configured through the X-ray generator's graphical user interface (GUI). After setting the parameters and positioning the patient on the detector, the X-ray is activated via an exposure switch. The detector captures the radiation, converts it into a digital signal, and transmits the data wirelessly to a computer equipped with compatible software. The images are processed and displayed on the computer for diagnostic review. The PRORAD ATLAS system is compatible with several 510(k)-cleared detectors and their associated software, listed below in Table 1. Prognosys includes one detector and its pre-configured software in the package, depending on availability. Fully battery-operated, the system does not support direct power connection but can seamlessly integrate with multiple detectors and compatible software as part of the package.

The provided FDA 510(k) clearance letter and supporting documentation for the PRORAD ATLAS ULTRAPORTABLE X-Ray Systems do not include acceptance criteria or a detailed study that proves the device meets specific performance criteria beyond general safety and effectiveness.

The document primarily focuses on demonstrating substantial equivalence to a predicate device (Remex KA6, K212144) rather than presenting a performance study with defined acceptance criteria. The "Summary of non-clinical testing" lists a series of international standards (IEC, ISO) and FDA guidance documents that were followed for design control, risk management, verification, and validation. The "Summary of clinical testing" mentions that clinical images were collected and reviewed by a qualified radiologist, confirming they are "clinically acceptable." However, specific quantitative acceptance criteria for image quality, diagnostic accuracy, or other performance metrics, along with the study design and results against those criteria, are not detailed in this document.

Therefore, I cannot provide a table of acceptance criteria and reported device performance, nor can I provide information about sample size, expert details, adjudication methods, MRMC studies, standalone performance, or training set specifics, as this information is not present in the provided text.

Based on the available text, here's what can be extracted:

• Overall Conclusion: The device is deemed "safe and effective when the device is used as labelled and is substantially equivalent to the predicate device."

Here's a breakdown of why the requested information cannot be fully provided based on the input:

1. A table of acceptance criteria and the reported device performance: This information is not explicitly stated in the document. The document confirms that "Validation of PRORAD ATLAS X-Ray System has demonstrated that the system enables optimal and quality imaging of anatomical structures" and that clinical images are "clinically acceptable," but no specific quantitative criteria or performance metrics are given.

2. Sample size used for the test set and the data provenance: The document states that "Clinical images of body extremities were collected from patients of varying ages, weights, and BMIs." However, the exact sample size and the provenance (e.g., country of origin, retrospective/prospective nature) of this clinical image test set are not specified.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts: The document mentions that images were "reviewed by a qualified radiologist." It does not specify the number of radiologists or their specific qualifications (e.g., years of experience, board certification).

4. Adjudication method (e.g. 2+1, 3+1, none) for the test set: No adjudication method for the clinical image review is described.

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: An MRMC study is not mentioned. The device described is an X-ray system, not an AI software to assist human readers.

6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done: This is not applicable as the device is an X-ray system, not an algorithm, and it's intended to be used by trained medical professionals.

7. The type of ground truth used: The ground truth for the clinical images appears to be "clinical acceptability" as determined by a "qualified radiologist." This aligns with "expert consensus" in a general sense, but no more objective ground truth (e.g., pathology, outcomes data) is mentioned for the image quality assessment.

8. The sample size for the training set: The document does not mention a training set, as it describes an X-ray hardware system, not an AI-driven software that requires a training set.

9. How the ground truth for the training set was established: Not applicable, as no training set is mentioned.

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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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Flat Panel Digital X-ray Detector 17HK701G-W is indicated for digital imaging solution designed for general radiographic system for human anatomy. It is intended to replace film or screen based radiographic systems in general purpose diagnostic procedures all and not to be used for mammography.

The 17HK701G-W is the solid state x-ray imager, which can generate radiographic images of any part of the body. These devices intercept x-ray photons and the scintillator (CSI:TI) emits visible spectrum photons that illuminate an array of photo-detectors that create an electrical signals. After the electrical signals are generated, it is converted to digital value, and the images are displayed on monitors. The digital value can be communicated to the operator console via wiring connection. The 17HK701G-W consists of the following components: Flat Panel Detector, Control Box, battery Charger, 2 packs of battery, power adapter for charger, Calibration Software, power cord and cables. The 17HK701G-W can be used for general X-ray system excluding fluoroscopic, angiographic, and mammographic applications. The subject device is supported by software. The software is of Moderate level of concern and is identical to the predicate software.

This document pertains to the 510(k) premarket notification for the LG Electronics Inc. 17HK701G-W Flat Panel Digital X-ray Detector. The information provided outlines the device's characteristics and its substantial equivalence to a predicate device, K182348 (14HK701G-W).

Acceptance Criteria and Device Performance:

The document primarily focuses on demonstrating substantial equivalence to a predicate device rather than presenting a detailed independent study with specific acceptance criteria and a "reported device performance" table in the manner typically seen for new AI/CADe systems with diagnostic claims.

Instead, the acceptance for this device hinges on its technological characteristics being substantially equivalent to the predicate device, specifically regarding safety and effectiveness. The comparison table below highlights key technological characteristics:

Table of Acceptance Criteria and Reported Device Performance

The Study That Proves the Device Meets the Acceptance Criteria:

The document describes a submission for substantial equivalence to an existing predicate device (K182348, 14HK701G-W). This means the "study" is a collection of tests and comparisons designed to demonstrate that the new device (17HK701G-W) is as safe and effective as the predicate device, despite minor differences.

Here's the breakdown of the information requested:

1. 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: "Clinical data has been provided according to FDA guidance document 'Guidance for the Submission of 510(k)s for Solid State X-ray Imaging Devices'. The data was not necessary to establish substantial equivalence based on the modifications to the device but provided further evidence in addition to the laboratory performance data to show that the device works as intended."
   • This implies that while clinical data was submitted, it wasn't the primary basis for the substantial equivalence determination due to the nature of the device (a digital X-ray detector, not an AI/CADe system with a diagnostic claim) and the similarity to the predicate. Therefore, detailed sample sizes, data provenance (country, retrospective/prospective), or ground truth methods for this clinical data are not explicitly provided in this summary. The focus was on non-clinical performance and technological equivalence.

2. 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):

   • As detailed above, the clinical data was not the primary driver for substantial equivalence, and therefore, details on expert ground truth establishment for a test set are not provided nor expected in this type of submission.

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

   • Not applicable/not provided as a formal reader study with adjudication for ground truth was not the primary method for demonstrating equivalence for this device.

4. 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. This device is a diagnostic imaging component (a flat panel detector), not an AI/CADe system designed to assist human readers or make a diagnostic claim.

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

   • This question is generally relevant for AI/CADe systems. For this device (a flat panel detector), its "standalone performance" is assessed by electrical safety, EMC, and imaging performance tests (DQE, MTF, resolution), which were conducted. The device (detector) itself does not perform an "algorithm" making a diagnostic assessment.

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

   • For the non-clinical performance tests (DQE, MTF, resolution), the "ground truth" is established by physical measurement standards (e.g., IEC 62220-1). For the clinical data, if it was descriptive (e.g., image quality assessment), the ground truth would likely be expert interpretation, but specific details are not provided.

7. The sample size for the training set:

   • This device is a hardware component with associated firmware/software for image acquisition and processing. It is not an AI/Machine Learning model that undergoes "training" on a dataset in the conventional sense. The software validation refers to standard software development lifecycle processes, not machine learning model training. Therefore, a "training set sample size" is not applicable in this context.

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

   • As above, a "training set" in the context of machine learning is not applicable for this device. The software validation follows established engineering principles, ensuring the software performs its intended functions correctly and reliably, rather than learning from labeled data.

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