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Intended for use by a qualified/trained medical professionals, who have full understanding of the safety information and emergency procedures as well of capabilities and function of the device provides radiographic, multiradiographic and fluoroscopic imaging and is used for guidance and visualization during diagnostic radiographic, surgery, and interventional procedures. The device is to be used in healthcare facilities both inside of hospital, in a variety of procedures of the skull, spinal column, extremities, and at the discretion of the medical professional the device may be used for other imaging applications on all pediatric patients (birth to 21 years) within the limits of the device. Applications can be performed with the patient sitting, standing, or lying in the prone or supine position. The system is not intended for mammography applications

The Virtual C DRF-NEO system is a mobile imaging system that acquire, process and display both static radiographic images and dynamic radiographic images such as photo-spot and fluoroscopy. Dynamic image acquisition is performed without the limitation of a mechanical linkage between the x-ray source and the x-ray detector. The mechanical linkage typical in existing dynamic imaging systems is either a c-arm or u-arm that ensures the alignment of the imaging components during image acquisition. The Virtual C DRF-NEO System features a novel collimator with built-in x-ray source to detector alignment software (Machine-Vision Collimator (MVC), combine they provide the technology for a "virtual c-arm" system. The novel MVC utilized four independent shutter to automatically position the radiation beam, so the area of exposure always remains within the confines of the active area of the detector. In addition, the angle and inclination of x-ray source is displayed to the operator. A visual display provides real time video images of the patient and a shaded area within the video images represent the location and size of the radiation beam with respect to the patient.

The provided text describes a 510(k) premarket notification for the "Virtual C DRF-NEO Digital Imaging System." This document primarily focuses on demonstrating substantial equivalence to a predicate device through comparison of technical specifications and summaries of non-clinical testing. It explicitly states that "No clinical data is necessary to evaluate safety or effectiveness for purposes of determining substantial equivalence of the proposed modification."

Therefore, based on the provided text, there is no study that proves the device meets specific acceptance criteria related to its performance in a clinical or standalone setting. The submission relies on establishing substantial equivalence to a predicate device, and thus, does not include information on acceptance criteria based on clinical performance metrics or studies involving human readers or ground truth.

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

1. Table of Acceptance Criteria and Reported Device Performance

Not applicable. The document does not define specific clinical acceptance criteria for the "Virtual C DRF-NEO Digital Imaging System" or present a study comparing its performance against such criteria. The submission focuses on demonstrating substantial equivalence to a predicate device by comparing technical specifications.

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

Not applicable. No clinical test set was used, as "No clinical data is necessary to evaluate safety or effectiveness." The assessment was based on non-clinical bench testing and comparison to technical specifications of a predicate device.

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

Not applicable. No clinical test set was used, and therefore, no experts were involved in establishing ground truth for clinical performance.

4. Adjudication Method for the Test Set

Not applicable. No clinical test set was used.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done, and Effect Size of Human Readers Improvement with AI vs. Without AI Assistance

Not applicable. No MRMC comparative effectiveness study was conducted. The device is a digital imaging system, not an AI-assisted diagnostic tool that would typically involve a multi-reader study to evaluate improvement with AI.

6. If a Standalone (i.e., algorithm only without human-in-the-loop performance) Was Done

Not applicable. No standalone performance study was conducted. The assessment was based on demonstrating substantial equivalence to a predicate device.

7. The Type of Ground Truth Used

Not applicable. For the purpose of this 510(k) submission, the ground truth was effectively the technical specifications and validated performance of the predicate device and the digital panel component (DRTECH EVS 2430W with K171137 clearance), against which the proposed device's characteristics were compared for substantial equivalence. No clinical ground truth (e.g., pathology, outcomes data) was used for evaluating the new device's performance in a clinical context.

8. The Sample Size for the Training Set

Not applicable. No training set was involved, as this submission is not for an AI/ML algorithm requiring a training phase.

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

Not applicable. No training set was used.

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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 for the purpose of acquiring X-ray images of the desired parts of patient's anatomy (including head, cervical spine, chest, abdomen, lumbar spine, pelvis and extremities).This device is not intended for mammography.

This SR-2300/SR-2300S Portable DR Imaging System is a portable digital device developed, designed and manufactured by SIUI. The device consists of the following major components: a portable X-ray unit, a flat panel detector, and workstation. The difference between SR-2300 and SR-2300S is the operation interface of the portable X-ray Unit only, while the flat panel detector and the workstation are the same for SR-2300 and SR-2300S. The SR-2300S has the display interface with button-operation digital tube, while the SR-2300 has the display interface with touch screen operation.

The provided text describes a 510(k) premarket notification for the SR-2300/SR-2300S Portable DR Imaging System. This document focuses on demonstrating substantial equivalence to a predicate device, rather than proving the device meets specific acceptance criteria through a clinical performance study for an AI/ML-enabled medical device.

Therefore, many of the requested details about acceptance criteria, study design (sample size, ground truth, experts, adjudication, MRMC), and training set information for an AI/ML device are not present in this document. This submission is for a traditional medical device (portable X-ray system), not an AI/ML diagnostic software.

Here's a breakdown of the available information based on your request, highlighting what is not applicable (N/A) or not provided in this type of submission:

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

• Acceptance Criteria: N/A. This submission focuses on demonstrating substantial equivalence to a predicate device through technical specifications and compliance with established performance standards for X-ray systems, rather than meeting specific performance metrics for a diagnostic or AI algorithm.
• Reported Device Performance: The document provides a comparison of technical characteristics between the subject device and the predicate device (Table 1 on page 6-7). This table serves to show that the new device's specifications meet or exceed those of the predicate, or that any differences do not impact safety or efficacy. For example:
  • X-ray Generator Peak Power: Predicate: 2kW; Subject: 3.2kW (implied from "Tube voltage 40-125kV, Tube current 10-100mA")
  • Tube Voltage Adjustable Range: Predicate: 40-100kV; Subject: 40-125kV
  • mAs Range: Predicate: 0.4mAs-50mAs; Subject: 0.4mAs-200mAs
  • DQE: Predicate: 25% at 1.0 lp/mm, 5% at 3.0 lp/mm; Subject: 36% at 1.0 lp/mm, 13% at 3.0 lp/mm
  • MTF: Predicate: 35% at 2.0 lp/mm; Subject: 38% at 2.0 lp/mm

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

• Sample Size for Test Set: N/A. No clinical test set data is reported as "Clinical testing is not necessary for the SR-2300/SR-2300S Portable DR Imaging System in order to demonstrate substantial equivalence to the predicate device." (Page 10, Section 5.9).
• Data Provenance: N/A for clinical performance. The document states "Clinical images were provided; these images were not necessary to establish substantial equivalence based on the modifications to the predicate device, but they provide further evidence in addition to bench testing data to show that the complete system works as intended." (Page 10, Section 5.7). No details on the number or origin of these "clinical images" are given.

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

• N/A. No clinical test set with expert-established ground truth was required or performed for this 510(k) submission.

4. Adjudication method for the test set

• N/A. No clinical test set.

5. If a multi reader multi case (MRMC) comparative effectiveness study was done, If so, what was the effect size of how much human readers improve with AI vs without AI assistance

• N/A. This is not an AI/ML-enabled device, and no MRMC study was conducted.

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

• N/A. This is a hardware device (portable X-ray system), not a standalone algorithm.

7. The type of ground truth used

• N/A. No clinical performance study requiring ground truth was conducted. The "ground truth" for this type of device is typically established through engineering specifications, phantom testing, and comparisons to established performance standards for X-ray imaging, as well as the predicate device's characteristics.

8. The sample size for the training set

• N/A. This is a traditional medical device, not an AI/ML device that requires a training set.

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

• N/A. No training set for an AI/ML algorithm.

Summary of what the document does provide regarding device performance proof:

The document primarily relies on non-clinical testing and compliance with established standards to demonstrate substantial equivalence and ensure safety and effectiveness.

• Non-clinical Testing Summary (pages 8-10, Section 5.7):
  • The device complies with and was tested in accordance with numerous international standards for medical electrical equipment, X-ray equipment, electromagnetic compatibility, radiation protection, risk management, biological evaluation, software life cycle processes, usability, and symbols.
  • Examples of standards: IEC 60601-1, IEC 60601-1-2, IEC 60601-2-54, IEC 60601-1-3, ISO 14971, ISO 10993 (various parts), IEC 62304, IEC 60601-1-6, IEC 62366-1, ISO 15223-1, ISO 13485.
  • Proof: "The test results showed compliance with the above standards. Validation was performed for overall operation by taking and reviewing test images. Clinical images were provided; these images were not necessary to establish substantial equivalence based on the modifications to the predicate device, but they provide further evidence in addition to bench testing data to show that the complete system works as intended. The non-clinical tests demonstrate that the device is as safe, as effective, and performs as well as the predicate." (Page 10, Section 5.7).
• Comparison to Predicate Device (Table 1, pages 6-7): Shows that the subject device's technical specifications are comparable or superior to the predicate, and any differences do not raise new safety or effectiveness concerns.

In conclusion, this 510(k) submission is for a conventional X-ray system, and therefore, the detailed criteria for an AI/ML device performance study are not applicable or provided. The "proof" of meeting acceptance criteria for this device rests on demonstrating adherence to recognized performance standards for X-ray equipment and showing substantial equivalence to a legally marketed predicate device through technical comparisons and non-clinical bench testing.

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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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