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Intended for use by a qualified/trained physician or technician on both adult and pediatric subjects for taking diagnostic x-rays. Not for mammography.

AcuityDRe components into a complete digital x-ray system upgrade kit, including software and digital radiography panels. The customer selects one of the following digital x-ray receptor panels: AcuityDRe 1417w, AcuityDRe 1717w, AcuityDRe 1717t. The "w" indicates wireless wi-fi while the "t" indicates tethered. The indications for use remains unchanged: Intended for use by a qualified/trained physician or technician on both adult and pediatric subjects for taking diagnostic x-rays. Not for mammography. So the only difference between this submission and the predicate submission is the generator/tubestand combination. Each system consists of the following items: Customer supplies: Diagnostic x-ray generator (HF) Class I Code IZO. + Tubehead: Class I Code ITY + Tube Mount: Class I Code IYB + Attached Collimator, Manual (IZX) Class II 510(k) Exempt We supply: Digital X-Ray Receptor Panel 892.1680 Class II Code MQB. Digital X-ray Software 892.2050 Class II Code LLZ. The software offered for sale with this system has received previous 510(k) clearance in K201058.

The provided text describes the 510(k) premarket notification for the AcuityDRe device, which is an upgrade kit for digital x-ray systems. It primarily focuses on demonstrating substantial equivalence to a predicate device (Accuvue+, K201058), rather than detailing a clinical study with specific acceptance criteria and performance against those criteria in a typical sense of an AI/ML medical device.

The "study" cited here is non-clinical testing, specifically clinical image evaluation performed by a Board Certified Radiologist, and bench testing for physical performance characteristics. It's not a comparative effectiveness study involving human readers with and without AI assistance, nor a standalone AI performance study.

Here's an attempt to extract and present the information based on the provided text, acknowledging that many requested fields regarding AI/ML device studies are not applicable or detailed in this 510(k) summary for an x-ray hardware upgrade kit.

Device: AcuityDRe (Digital X-ray System Upgrade Kit)

Description of Testing (Non-Clinical and Image Evaluation)

The primary goal of the testing was to demonstrate substantial equivalence of the AcuityDRe system (comprising new digital x-ray receptor panels, software, and generator compatibility) to a legally marketed predicate device (Accuvue+, K201058). This was achieved through:

• Bench testing for physical performance characteristics of the new digital x-ray panels.
• Clinical image evaluation by a Board Certified Radiologist to assess image quality.
• Confirmation of compliance with relevant medical device standards and FDA guidance documents.

1. Table of Acceptance Criteria and Reported Device Performance

Given that this 510(k) is for an X-ray system upgrade kit rather than an AI/ML algorithm, the "acceptance criteria" are not framed as typical AI performance metrics (e.g., sensitivity, specificity, AUC). Instead, the key "acceptance criteria" are based on demonstrating substantial equivalence to the predicate device in terms of:

• Same Indications for Use
• Similar Technological Characteristics (especially panel performance: DQE, MTF, and panel sizes)
• Compliance with safety and performance standards.
• "Excellent quality" of clinical images as assessed by an expert.

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

• Test Set Sample Size: Not explicitly stated numerically for the clinical image evaluation. The text only mentions "Clinical image evaluation was performed on the proposed new panels."
• Data Provenance: Not specified. It's likely retrospective, as it's an evaluation of images generated by the new panels. Country of origin not mentioned; presumed to be images from typical clinical settings where such panels would be used.

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

• Number of Experts: One.
• Qualifications: A "Board Certified Radiologist." Further details like years of experience are not provided.

4. Adjudication Method for the Test Set

• Adjudication Method: Not applicable/None, as only a single expert was used for the qualitative assessment of image quality.

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

• MRMC Study: No. This type of study (AI vs. human-assisted AI) was not performed as the device is an x-ray system upgrade, not an AI diagnostic algorithm.
• Effect Size of Human Reader Improvement: Not applicable.

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

• Standalone Study: No, as the device itself is a component of an x-ray system, not a standalone AI algorithm. The image quality assessment was of the images produced by the hardware, which would then be interpreted by humans.

7. Type of Ground Truth Used

• Ground Truth Type: Expert Consensus / Qualitative Expert Assessment. The "ground truth" for the image quality was the subjective assessment by the Board Certified Radiologist that the images were "of excellent quality." This is distinct from, for example, pathology-confirmed diagnoses or patient outcomes data.

8. Sample Size for the Training Set

• Training Set Sample Size: Not applicable. This device is a hardware component (digital x-ray panels) and associated software, not an AI/ML algorithm that requires a "training set" in the context of machine learning model development. The software mentioned (AccuVue/AccuVue+) has received previous clearance for control of generators.

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

• Ground Truth Establishment for Training Set: Not applicable, as there is no "training set" in the context of an AI/ML algorithm for this device.

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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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Intended for use by a qualified/trained physician or technician on both adult and pediatric subjects for taking diagnostic x-rays. Not for mammography.

Accuvue+ components into a complete digital x-ray system upgrade kit, including software and digital radiography panels. The customer selects one (or more) of the following digital x-ray receptor panels: DRTech 4343A, (K192400); DRTech 4343W, (K193017); AcuityDR (K171137); AcuityDR 1417 (K162552 EVS 3643G) or AcuityDR 1717 (K162555 EVS 4343, EVS 4343G). We wish to offer for sale a subset of our recently cleared submission K200726. This in essence would be an upgrade kit. The indications for use remains unchanged: Intended for use by a qualified/trained physician or technician on both adult and pediatric subjects for taking diagnostic x-rays. Not for mammography. So the only difference between this submission and the predicate submission is the generator/tubestand combination.

This document is a 510(k) summary for a medical device called Accuvue+. It describes the device and its intended use, and argues for its substantial equivalence to a predicate device. However, it does not contain information about acceptance criteria or a study proving that the device meets specific performance criteria through a clinical or non-clinical study.

The document explicitly states:

• "6. Clinical testing. Not required for a determination of substantial equivalence."
• "5. Non clinical testing: Testing was performed in previous submssions according to the following standards:" (It then lists general safety and communication standards, not performance criteria for diagnostic accuracy or image quality.)

Therefore, I cannot extract the requested information regarding acceptance criteria and a study proving the device meets those criteria from the provided text. The submission focuses on demonstrating substantial equivalence based on technological characteristics and adherence to general safety standards, rather than a performance study with acceptance criteria.

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Intended for use by a qualified/trained physician or technician on both adult and pediatric subjects for taking diagnostic x-rays. Not for mammography.

RadmediX AcuityPDR combines components into a complete mobile x-ray system, including software, a generator/collimator combination, and digital radiography panels. Radmedix combines components from various manufacturers into a complete mobile x-ray system. The customer selects one (or more) of the following digital x-ray receptor panels: DRTech 4343A, (K192400); DRTech 4343W, (K193017); AcuityDR (K171137); AcuityDR 1417 (K162552 EVS 3643, EVS 3643G) or AcuityDR 1717 (K162555 EVS 4343, EVS 4343G). In addition, the customer selects one of three software packages: Accuvue+ (K130883), AccuVueMED, (K152172) or AccuVue (K141440). The generator can be battery operated. A single battery charge produces 200 Exposures at max KV and MAS settings. The battery charger is UL Listed and the internal lithium ion battery is overcharge and overcurrent protected. A typical acquisition computer would be a Lenovo P53S or a Dell Precision 3541.

This document describes a medical device, AcuityPDR, a mobile X-ray system, and its 510(k) submission for FDA clearance. The submission relies on demonstrating substantial equivalence to a predicate device (MinXray CMDR 2CW).

Here's an analysis of the requested information based on the provided text:

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

The document does not explicitly state acceptance criteria in the form of quantitative performance metrics for the AcuityPDR device itself. Instead, it focuses on demonstrating "substantial equivalence" to a predicate device. The acceptance criteria for this type of submission are typically that the device performs as safely and effectively as the predicate and has the same indications for use.

The device performance is reported implicitly through the comparison table on page 4, showing that the AcuityPDR either matches or has comparable/equivalent functionality to the predicate in various technical parameters.

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 explicitly states in section 6, "Clinical testing. Not required for a determination of substantial equivalence." This indicates that no clinical test set was used for this specific submission. The submission relies on non-clinical testing (bench testing, standards compliance) and comparison to the predicate device.

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)

Since no clinical test set was required or used for this submission, there is no mention of experts being used to establish ground truth for a test set.

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

As no clinical test set was used, no adjudication method is mentioned.

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 study was performed as clinical testing was "Not required for a determination of substantial equivalence." The device is a mobile X-ray system, not an AI-powered diagnostic tool, so the concept of human readers improving with AI assistance is not applicable in this context.

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

This is not an AI algorithm; it's a mobile X-ray system. Therefore, standalone algorithm performance is not applicable. The performance is assessed through compliance with electrical and safety standards for X-ray devices and comparison of technical specifications to a predicate.

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

For this type of 510(k) submission, "ground truth" is primarily established by:

• Compliance with recognized electrical, safety, and performance standards for X-ray generating equipment (e.g., IEC 60601 series, 21 CFR 1020).
• Demonstration that the device's technical specifications and intended use are substantially equivalent to a legally marketed predicate device.
• Component certifications: "All of the components subject to the CDRH performance standard are certified to comply with the standard by their respective manufacturers."

8. The sample size for the training set

The document does not describe a training set for an AI model, as this is not an AI device. The "training" for such a device would refer to its design and manufacturing processes adhering to established engineering principles and standards.

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

Not applicable, as no AI model or training set is described. The "ground truth" for the device's design and manufacturing relies on established medical device regulations, design controls, and industry standards for X-ray imaging equipment.

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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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ProRad Series Stationary Radiographic System is intended for use by a qualified, trained doctor or technician on both adult and paediatric subjects for taking diagnostic radiographic exposures of the skull, spinal column, chest, abdomen, extremities, and other body parts. Applications can be performed with the patient sitting, standing, or lying in the prone or supine position. Not for mammography.

The ProRad series Stationary Radiographic System is a diagnostic x-ray system intended for general purpose radiographic imaging of the human body. There are two types of configurations (2FC and 3NC) for ProRad; the difference is in the mounting of the X-ray tube. For X-ray tube mounting the configuration is either the floor mounted (2FC) or ceiling suspension (3NC) assembly.

The devices are a new combination of a previously cleared solid state digital x-ray acquisition panel and software with the diagnostic x-ray components (including Xray tube, high frequency X-ray generator, a tilting vertical bucky, X-ray table and collimator) required to make a complete system. The purchaser may select any of the digital panels and software based on the user's requirements. The other components are also available in different configurations to meet specific customer needs. The X-ray panel and imaging software have been previously cleared by the FDA, and most of the other components are used in previously cleared 510(k) devices.

Here's an analysis of the acceptance criteria and study information for the ProRad 2FC and ProRad 3NC Digital Stationary Radiographic Systems, based on the provided text:

Acceptance Criteria and Device Performance Table:

The document primarily focuses on demonstrating substantial equivalence to predicate devices rather than setting and reporting specific performance metrics with acceptance criteria in a comparative table for novel device features. The "Comparable Properties" table (pages 6-8) is used to show alignment with predicate devices.

1. Sample sized used for the test set and the data provenance:

• Sample Size: Not explicitly stated as a separate "test set" for a dedicated algorithm performance study. Instead, compliance is demonstrated through testing of the integrated system and reliance on previous FDA clearances for individual components.
• Data Provenance: Not applicable in the context of an algorithm performance test set. The clinical images reviewed by a radiologist were "acquired by the device," but the origin (e.g., country, prospective/retrospective status) is not specified.

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

• Number of Experts: One radiologist.
• Qualifications of Experts: Only "a radiologist" is mentioned. Specific qualifications (e.g., years of experience, board certification) are not detailed.

3. Adjudication method for the test set:

• Adjudication method: Not applicable. The radiologist's review was a single assessment, not a consensus or adjudication process among multiple readers.

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:

• MRMC Study: No. The document explicitly states: "Since the digital x-ray panels and software have previously received FDA clearance, a clinical study was not required as per the FDA guidance document." The clinical image review was supplementary.
• Effect size of human reader improvement with AI: Not applicable, as no MRMC study or AI assistance evaluation was conducted. The device is an imaging system, not an AI diagnostic tool.

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

• Standalone Performance Study: No. This device is a diagnostic X-ray system, which intrinsically requires a human (a qualified doctor or technician) in the loop for operation and interpretation. The performance of individual cleared components (digital panels, software) was relied upon.

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

• Type of Ground Truth: For the supplementary review, the "ground truth" was the radiologist's assessment that the images were "acceptable and allowed the radiologist to make an accurate diagnosis." This is a form of expert opinion on image quality and diagnostic utility, rather than an objective "truth" like pathology or outcomes.

7. The sample size for the training set:

• Sample Size for Training Set: Not applicable. This document does not describe a machine learning algorithm that requires a training set. The device is a conventional X-ray system composed of cleared components.

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

• Ground Truth for Training Set: Not applicable, as there is no machine learning algorithm described.

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The EVS 2430W and EVS 2430GW Digital X-ray detector is indicated for digital imaging solution designed for providing general radiographic diagnosis of human anatomy. This device is intended to replace film or screen based radiographic systems in all general purpose diagnostic procedures. This device is not intended for mammography applications.

The EVS 2430(G)W is a wired/wireless flat-panel type digital X-ray detector that captures projection radiographic images in digital format within seconds, eliminating the need for an entire x-ray film or an image plate as an image capture medium. EVS 2430(G)W differs from traditional X-ray systems in that, instead of exposing a film and chemically processing it to create a hard copy image, a device called a Detector is used to capture the image in electronic form.

The EVS 2430(G)W Detector is an indirect conversion device in the form of a square plate in which converts the incoming X-rays into visible light. This visible light is then collected by an optical sensor. which generates an electric charges representation of the spatial distribution of the incoming X-ray quanta.

The charges are converted to a modulated electrical signal thin film transistors. The amplified signal is converted to a voltage signal and is then converted from an analog to digital signal which can be transmitted to a viewed image print out, transmitted to remote viewing or stored as an electronic data file for later viewing.

This document describes the regulatory submission for the DRTECH Corporation's EVS 2430W and EVS 2430GW Digital X-ray detectors. The submission aims to demonstrate substantial equivalence to a predicate device (EVS 3643, EVS 3643G, K162552).

Here's an analysis of the provided information regarding acceptance criteria and the supporting study:

1. Table of Acceptance Criteria and Reported Device Performance:

The document primarily focuses on demonstrating substantial equivalence to a predicate device rather than defining explicit acceptance criteria with specific thresholds for each parameter. Instead, it compares the performance of the modified device (EVS 2430W, EVS 2430GW) to the predicate device (EVS 3643, EVS 3643G). The acceptance is implicitly based on the new devices showing "basically equal or higher" physical values for key X-ray device comparison metrics.

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

• Sample Size: Not explicitly stated as a number of cases or images. The document mentions "Clinical images were provided" and "study confirmed that the x-ray detectors EVS 2430(G)W provide images of equivalent diagnostic capability." The specific number of images or patients in the clinical image test set is not provided.
• Data Provenance: Not explicitly stated. The study was a "single-blinded concurrence study according to CDRH's Guidance for the Submission of 510(k)'s for Solid State X-ray Imaging Devices." This typically implies a controlled setting, but the origin (e.g., country, type of clinic) of the raw images or data used is not mentioned. It is a clinical test to demonstrate clinical effectiveness. Whether it's retrospective or prospective is also not stated clearly for this "clinical image evaluation."

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

• Number of Experts: Not specified. The document states it was a "single-blinded concurrence study." This implies that expert readers (e.g., radiologists) were involved in evaluating the images, but their number is not provided.
• Qualifications of Experts: Not specified. While the context (radiographic diagnosis) implies radiologists would be the experts, their specific qualifications (e.g., years of experience, subspecialty) are not mentioned.

4. Adjudication Method for the Test Set:

• Adjudication Method: Not explicitly stated. The term "concurrence study" implies that multiple readings were compared for agreement, but the specific adjudication method (e.g., 2+1, 3+1, majority vote, etc.) is not detailed. The "single-blinded" aspect means the readers were unaware which images came from the test device versus the predicate.

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

• Was an MRMC study done? A "single-blinded concurrence study" was conducted to confirm "equivalent diagnostic capability" to the predicate. This is a type of comparative effectiveness study. However, it's not explicitly framed as an "MRMC study" in the sense of a statistical design aimed at measuring observer performance gains. It's more of a qualitative assessment of equivalence.
• Effect size of human readers with AI vs. without AI assistance: Not applicable. This device is a digital X-ray detector, not an AI-assisted diagnostic tool. The comparison is between the new detector and a predicate detector, not about AI assistance for human readers.

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

• Was standalone performance done? Not applicable. The device is a hardware component (X-ray detector). Its "performance" is measured by physical metrics (DQE, MTF, Resolution) and its ability to produce diagnostically equivalent images for human interpretation, not by an autonomous algorithm making diagnoses.

7. The Type of Ground Truth Used:

• Type of Ground Truth: The document states that the "clinical image evaluation proved the clinical effectiveness of the subject device" and that the study confirmed "equivalent diagnostic capability." This strongly suggests that the ground truth was established by expert consensus (i.e., expert readers determining the diagnostic quality and clinical equivalence of images produced by the test device compared to the predicate). It does not mention pathology, outcomes data, or other definitive ground truths.

8. The Sample Size for the Training Set:

• Sample Size for Training Set: Not applicable. This document is for a hardware device (X-ray detector), not a machine learning algorithm that requires a "training set." The tests described are for objective performance metrics and clinical image evaluation for equivalence.

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

• How Ground Truth for Training Set was Established: Not applicable, as this is not an AI/ML device requiring a training set.

In summary, the document details a comparison of a new X-ray detector to a predicate device, focusing on technical specifications and demonstrating "equivalent diagnostic capability" through a clinical image evaluation. While it confirms the device meets acceptance criteria (primarily by demonstrating equivalence to a previously cleared device), many specific details about the clinical study's methodology, particularly regarding reader numbers, qualifications, and exact sample sizes, are not provided in this specific excerpt.

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