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The RFA-1717DI detector is indicated for digital imaging solution designed for general radiographic system for human anatomy. It is intended to replace film or screen based radiographic system in all general-purpose diagnostic procedures. It is not to be used for mammography.

The RFA-1717DI detector is a digital X-ray flat panel detector which has 43cm x 43cm imaging area and communicates with a wired communication feature. Giga-bit Ethernet communication method through connection of tether cable. The RFA-1717 detector is available in two types of scintillator: Csl: Tl type for RFA-1717DIC model and Gadox:Tb type for RFA-1717DIG model. The device accepts x-ray photons and the scintillator and emits visible spectrum photons that illuminate an array of photo (IGZO)-detector that creates electrical signals. After the electrical signals are generated, it is converted to digital values, and the images will be displayed on the monitor. This device should be integrated with an operating PC and an X-Ray generator. It can digitalize x-ray images and transfer them for radiography diagnostics. Advanced digital image processing allows considerably efficient diagnosis and imaging data management on network.

This document describes a 510(k) submission for the RFA-1717DI digital flat panel X-ray detector, seeking substantial equivalence to existing predicate devices. As such, the acceptance criteria and study described are for a non-clinical performance evaluation comparing the new device to established predicates, rather than a clinical study evaluating the impact of an AI algorithm on human reader performance.

Here's the breakdown of the acceptance criteria and the study that proves the device meets them, based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

The acceptance criteria are implicitly defined by demonstrating comparability to the predicate devices. The "performance" here refers to the technical specifications and measurements of the device itself, rather than diagnostic accuracy.

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

• Test Set Sample Size: The document does not specify a numerical sample size for "test sets" in the traditional sense of a clinical trial with patient cases. The evaluation is primarily based on bench testing and technical performance measurements of the device itself (e.g., MTF, DQE, NPS).
• Data Provenance: The testing was conducted by ASTEL Inc. (the manufacturer) and a 3rd party testing lab A (for electrical safety and EMC). The country of origin for the data (and manufacturing) is explicitly stated as Korea (26-79, Gajeongbuk-ro, Yuseong-gu, Daejeon, 34113, Korea). This was a prospective evaluation of the new device's performance against established standards, not a retrospective analysis of patient data.

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

• Number of Experts: Not applicable. For this type of device (a digital X-ray detector, not an AI diagnostic algorithm), "ground truth" is established via physical measurements and adherence to engineering standards, not clinical expert consensus on image interpretation.
• Qualifications of Experts: The experts would be qualified engineers and physicists specializing in medical imaging device testing and regulatory compliance. The document mentions "3rd party testing lab A," implying accredited professionals conducted the safety and performance tests.

4. Adjudication Method for the Test Set

• Adjudication Method: Not applicable. There is no human interpretation or diagnostic "ground truth" adjudicated for this type of device submission. The device's performance is objectively measured against physical and engineering standards.

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

• Was it done? No. This type of study (MRMC) is typically performed for AI-powered diagnostic aids, where the impact of the AI on human reader performance is being evaluated. This submission is for a basic imaging acquisition device, not an AI diagnostic tool.
• Effect Size: Not applicable.

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

• Was it done? Yes, in essence. The "non-clinical tests" and "performance testing/data" section describe the standalone performance of the RFA-1717DI detector. This includes measurements of MTF, DQE, and NPS, which are intrinsic performance metrics of the device itself, independent of human interpretation or any AI algorithm. The device's ability to produce images comparable to the predicate devices is the core of this "standalone" assessment.

7. Type of Ground Truth Used

• Type of Ground Truth: The ground truth for this device is based on objective physical and engineering standards and measurements. This includes:
  • International Standards: IEC 62220-1 (for DQE, MTF, NPS performance), IEC 60601-1 (electrical safety), IEC 60601-1-2 (electromagnetic compatibility).
  • Predicate Device Specifications: The performance values of the legally marketed predicate devices (LLX240AB01 and LTX240AA01) serve as the benchmark for "substantial equivalence."
  • Risk Management Standards: ISO 14971.

8. Sample Size for the Training Set

• Sample Size: Not applicable. This is a 510(k) for a hardware device (X-ray detector). It does not involve AI algorithms that require a "training set" of data.

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

• How Established: Not applicable, as there is no training set for this type of device.

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ViZion Ultra is intended for digital image capture use in general radiographic examinations, wherever conventional screen-film systems may be used, excluding fluoroscopy, angiography and mammography. ViZion Ultra allows imaging of the skull, chest, shoulders, spine, abdomen, pelvis, and extremities.

The ViZion Ultra system represents the straightforward integration of a new digital x-ray receptor panel (K110849) and our previously cleared software. This is a MODIFICATION of our clearances K112661 and K123644 wherein we have changed the supplier of the panel. Therefore a special 510(k) has been submitted. The ViZion Ultra is compatible with Sedecal SHF generators. Some manufacturers rebrand the Sedecal SHF generators, and these generators are compatible as well.

ViZion Ultra is a Digital Radiography system, featuring an integrated flat panel digital detector (FPD) ViZion Ultra is designed to perform digital radiographic examinations as a replacement for conventional film. This integrated platform provides the benefits of PACS with the advartages of digital radiography for a filmless environment and improves cost effectiveness. The major funciple of operation of the Viztek PACS and the new receptor panel were not changed. Our main predicate is ViZion + DR, K123644, wherein we combined our OPAL-RAD software with two new digital panels.

Here's an analysis of the acceptance criteria and the study proving the device meets them, based on the provided text:

Key Takeaway: This 510(k) submission is for a modification to an existing digital X-ray system, specifically changing the supplier of the digital receptor panel. Therefore, the "study" primarily focuses on demonstrating that the new panel performs as well as or better than the previously cleared predicate device, rather than establishing de novo efficacy. The acceptance criteria are implicitly met by achieving substantial equivalence to the predicate.

1. Table of Acceptance Criteria and Reported Device Performance

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

• Test Set Sample Size: The document does not specify a numerical sample size for the clinical images used in the evaluation. It only states that "Clinical images were acquired."
• Data Provenance: The document does not explicitly state the country of origin or whether the data was retrospective or prospective. Given the nature of a 510(k) for a minor modification (panel change), it's likely these were prospectively acquired images for comparison purposes, but this is not definitively stated.

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

• Number of Experts: "a board certified radiologist" (singular, implies one).
• Qualifications of Experts: "board certified radiologist." Specific years of experience are not mentioned.

4. Adjudication Method for the Test Set

• The document states that "a board certified radiologist... concluded the images from the new panel are as good as the images acquired with the predicate panel." This indicates a single-reader assessment rather than a multi-reader adjudication method (like 2+1 or 3+1). There was no additional adjudication process described.

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

• No, an MRMC comparative effectiveness study was not done. The evaluation described involves a single radiologist comparing images from the new panel to those from the predicate panel. The focus was on demonstrating non-inferiority (equal or better image quality) rather than quantifying an improvement in human reader performance with AI assistance. This device is a digital X-ray receptor, not an AI-powered diagnostic aid.

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

• This question is not applicable in the context of this device. The ViZion Ultra is a digital X-ray receptor panel and associated software for image acquisition. It is not an algorithm designed for interpretation or diagnosis that would typically undergo a standalone performance study in the way AI algorithms are evaluated. Its "performance" is inherently tied to the image it produces for a human reader. The technical parameters like MTF and DQE (mentioned as being supplied by the manufacturer) represent objective standalone measures of the detector's physical performance.

7. Type of Ground Truth Used

• The ground truth for the clinical images was implicitly established by expert consensus/opinion (from the single board-certified radiologist) that the images produced by the new panel were "as good as" those from the predicate panel. There is no mention of pathology, outcomes data, or other objective measures of disease presence as ground truth, as the study's purpose was image quality comparison for substantial equivalence, not diagnostic accuracy.

8. Sample Size for the Training Set

• Not applicable / Not provided. The device described is a hardware component (digital X-ray detector) and associated software for image acquisition and viewing. It is not an AI/Machine Learning algorithm that typically requires a distinct "training set" in the conventional sense for its function. The software is noted as "SAME as K112661," implying that its functionality is established.

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

• Not applicable. As stated above, this is not an AI/ML device that requires a training set with established ground truth for learning.

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ViZion + DR is intended for digital image capture use in general radiographic examinations, wherever conventional screen-film systems may be used, excluding fluoroscopy, angiography and mammography. ViZion + allows imaging of the skull, chest, shoulders, spine, abdomen, pelvis, and extremities.

The ViZion + DR system represents the straightforward integration of a new digital x-ray receptor panel and our previously cleared software. This is a MODIFICATION of our clearance K112661 wherein we have changed the supplier of the panel. ViZion + DR is a Digital Radiography system, featuring an integrated flat panel digital detector (FPD) ViZion + is designed to perform digital radiographic examinations as a replacement for conventional film. This integrated platform provides the benefits of PACS with the advantages of digital radiography for a filmless environment and improves cost effectiveness. The major functions and principle of operation of the Viztek PACS and the new receptor panel were not changed. Our main predicate is ViZion DR, K112661, wherein we combined our OPAL-RAD software with a new digital panel. We now also offer two sizes of panels: 14" x 17" and 17" x 17" panels.

Here's an analysis of the provided text regarding the acceptance criteria and study for the Viztek ViZion + DR device:

1. Table of Acceptance Criteria and Reported Device Performance

The provided 510(k) summary (K123644) does not outline specific numerical acceptance criteria for image quality metrics. Instead, it relies on a qualitative assessment for substantial equivalence.

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

• Sample Size for Test Set: Not explicitly stated. The document mentions "Clinical images were acquired," implying a set of images, but the exact number is not provided.
• Data Provenance: The document does not specify the country of origin of the data. It is implied to be retrospective or a limited prospective acquisition for the purpose of the 510(k) submission, as it involves comparison to existing predicate devices. The study is described as a clinical image evaluation, not a large-scale clinical trial.

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

• Number of Experts: "a board certified radiologist" (singular).
• Qualifications: "board certified radiologist." No further details on years of experience are provided.

4. Adjudication Method for the Test Set

• Adjudication Method: "a board certified radiologist who concluded the images from the new panel are as good as or better than the images acquired with the predicate panel." This indicates a single reader assessment, not a multi-reader adjudication method (like 2+1 or 3+1).

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done, and the Effect Size

• MRMC Study: No, an MRMC comparative effectiveness study was not done. The evaluation was performed by a single board-certified radiologist.
• Effect Size: Not applicable, as no MRMC study was conducted.

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

• This device is a digital flat panel X-ray detector system, not an AI algorithm. Therefore, a standalone performance study in the context of an AI algorithm is not applicable. The device itself is the imaging system, and its performance is assessed directly (both technically and clinically).

7. The Type of Ground Truth Used

• Type of Ground Truth: The ground truth for the clinical image evaluation was expert opinion/consensus, specifically the judgment of a single board-certified radiologist comparing the new panel's images to those from predicate panels. There is no mention of pathology or outcomes data for establishing ground truth in this submission.

8. The Sample Size for the Training Set

• This submission describes a hardware modification (a new digital panel combined with existing software) and its equivalence to predicate devices, not the development or training of a new AI algorithm. Therefore, there is no "training set" in the context of machine learning.

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

• Not applicable, as there is no training set for an AI algorithm.

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The ddR Versa™ Motion System is intended for use by a qualified/trained doctor or technician on 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)

ddRVersa™ Motion uses the same core technology as our ddRElement but is modified in that: The supplier for the digital x-ray receptor panel has changed. The system can now accommodate two digital x-ray receptor panels instead of just one. A Wi-Fi digital x-ray receptor panel may be chosen as the second panel. The tubestand is changed from a C-arm configuration to an overhead plus wall stand configuration. The table is changed from non-motorized to motorized. The generator supplier has been changed. The collimator has been changed.

The provided text describes a 510(k) summary for the ddRVersa™ Motion Digital Diagnostic X-Ray System (K123005), which is a modification of a predicate device. The information focuses on demonstrating substantial equivalence to the predicate device, rather than proving performance against specific acceptance criteria for a novel AI/software component.

Based on the provided text, here's a breakdown of the requested information:

1. Table of Acceptance Criteria and Reported Device Performance

No specific numerical acceptance criteria (e.g., sensitivity, specificity, accuracy thresholds) are mentioned in the provided text for device performance. The submission aims to show "substantial equivalence" to a predicate device, meaning it performs "as well as or better than" the legally marketed predicate.

Instead, the "acceptance criteria" are implied by the comparative nature of the 510(k) process, which focuses on demonstrating equivalence to the predicate device in terms of safety and effectiveness.

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

• Sample Size for Test Set: The document states that "Clinical images were obtained." However, it does not specify the sample size (i.e., number of images or patients) used for the clinical evaluation.
• Data Provenance: The document does not specify the country of origin of the data or whether it was retrospective or prospective.

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

• Number of Experts: The document states images were "evaluated by professional radiologists." It does not specify the exact number of radiologists involved.
• Qualifications of Experts: The experts are described as "professional radiologists." No further specific qualifications like years of experience or subspecialty are provided.

4. Adjudication Method for the Test Set

The document does not describe any specific adjudication method (e.g., 2+1, 3+1, none) for resolving discrepancies among expert readers or for establishing a definitive ground truth from the radiologists' evaluations. It simply states they "evaluated" the images.

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

• Was a MRMC study done? No. The document does not mention a MRMC comparative effectiveness study to assess how much human readers improve with AI vs without AI assistance. This device is an X-ray system, not an AI-powered diagnostic aide.

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

• Was a standalone study done? No. The device is a digital X-ray system that produces images for human interpretation, not an algorithm designed for standalone diagnostic performance.

7. Type of Ground Truth Used

The ground truth for the clinical evaluation was established by "professional radiologists" who found the images to be of "good diagnostic quality." This implies an expert consensus/opinion on image diagnosticity, rather than pathology, outcomes data, or a pre-defined reference standard.

8. Sample Size for the Training Set

The document does not refer to a "training set" as this device is a hardware system undergoing modifications and demonstrating substantial equivalence, not a machine learning model requiring a training phase.

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

Not applicable, as there is no mention of a training set for an algorithm in this submission.

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The Clear Vision DR7000F product is intended for use by a qualified/trained doctor or technician on both adult and pediatric 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.

The Clear Vision DR7000F system is intended to be used in medical clinics and hospitals for emergency, orthopedic, chiropractic, and other medical purposes. This device is not indicated for use in mammography.

The Clear Vision DR7000F system is a high-resolution digital imaging system designed for digital radiography. It is designed to replace conventional film radiography techniques. This system consists of a tube head/collimator assembly mounted on a U-Arm, along with a generator, generator control, and a detector, operating software.

The detector which is used proposed device is LTX240AA01-A (K090742) and LLX240AB01 (K102587) of Samsung Mobile Display Co., Ltd. These detectors are cleared by FDA 510(k).

The provided 510(k) summary for the Clear Vision DR7000F does not contain information about acceptance criteria or a study proving the device meets specific performance criteria related to AI or algorithm-only performance.

The document describes a digital radiography X-ray system, which is a hardware device, not an AI or algorithm-based diagnostic tool. The submission focuses on demonstrating substantial equivalence to predicate hardware devices and compliance with electrical, mechanical, environmental safety, and performance standards for X-ray systems.

Therefore, most of the requested information regarding AI/algorithm performance, ground truth establishment, expert review, and training/test set sizes is not applicable to this document.

Here's a breakdown of what can be extracted or inferred from the provided text, and where information is missing / not applicable:

1. Table of Acceptance Criteria and Reported Device Performance

Regarding specific questions related to AI/Algorithm performance:

2. Sample size used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)

• Not applicable / Not provided. This device is an X-ray imaging system, not an AI algorithm. The "clinical testing" mentioned for the X-ray detectors likely refers to performance evaluation under clinical conditions, not an algorithm's diagnostic accuracy on a test set of images.

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)

• Not applicable / Not provided. No specific "ground truth" establishment for an algorithm's performance is mentioned. Evaluation of an X-ray system focuses on image quality, radiation dose, safety, and functionality, which are assessed against technical specifications and clinical utility, rather than diagnostic "ground truth" for an AI model.

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

• Not applicable / Not provided.

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

• Not applicable / Not provided. This submission is for a medical imaging device, not an AI-assisted diagnostic tool. No MRMC study is mentioned.

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

• Not applicable / Not provided. No standalone algorithm performance is discussed.

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

• Not applicable / Not provided. For an X-ray device, "ground truth" generally relates to physical measurements (e.g., spatial resolution, contrast-to-noise ratio, MTF, DQE) and clinical image quality (diagnostic acceptability) rather than a pathology reference for an AI diagnosis.

8. The sample size for the training set

• Not applicable / Not provided. No AI training set is mentioned.

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

• Not applicable / Not provided. No AI training is mentioned.

Summary regarding the device:

The Clear Vision DR7000F is a digital radiography X-ray system. The study proving it meets acceptance criteria primarily involves engineering and performance testing against established international standards (EN/IEC 60601 series) for medical electrical equipment, as well as specific guidance for solid-state X-ray imaging devices. The acceptance criteria relate to electrical, mechanical, environmental safety, electromagnetic compatibility (EMC), and the technical performance and clinical utility of the X-ray detectors. The "study" mentioned is the compilation of these satisfactory test results conducted by the manufacturer, demonstrating compliance and substantial equivalence to existing cleared predicate hardware devices.

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ViZion DR is intended for digital image capture use in general radiographic examinations, wherever conventional screen-film systems may be used, excluding fluoroscopy, angiography and mammography. ViZion allows imaging of the skull, chest, shoulders, spine, abdomen, pelvis, and extremities.

The ViZion DR system represents the straightforward integration of two cleared devices: ViZion DR, K102123 and K102587, the Samsung Digital Flat Panel. ViZion DR is a Digital Radiography system, featuring an integrated flat panel digital detector (FPD) (K102587, Samsung Flat-Panel X-Ray Detector), made by Samsung Mobile Display Co., Ltd. (this is the 510(k) for the flat panel detector) and Viztek's proprietary OPAL-RAD PACS image viewing and acquire interface software technology, (K063337) which incorporates state of the art object-oriented software and connectivity. ViZion is designed to perform digital radiographic examinations as a replacement for conventional film. This integrated platform provides the benefits of PACS with the advantages of digital radiography for a film less. environment and improves cost effectiveness. The major functions and principle of operation of the Viztek PACS and Samsung FPD were not changed.

This is a 510(k) premarket notification for a new version of the Viztek ViZion DR, a Digital Radiography (DR) system. The submission focuses on replacing the digital flat panel detector (FPD) with a new model while maintaining the existing software and overall functionality. As such, the "acceptance criteria" and "study that proves the device meets the acceptance criteria" are framed in terms of demonstrating substantial equivalence to a predicate device, rather than proving a specific diagnostic accuracy against a clinical ground truth.

Here's an analysis based on the provided text:

1. Table of Acceptance Criteria and Reported Device Performance

The acceptance criteria are implicitly defined by the demonstration of substantial equivalence to the predicate device, specifically regarding safety and effectiveness, and the absence of new indications for use or technological differences that would raise new questions of safety or effectiveness. The reported performance is a comparison to the predicate.

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

The document states: "Clinical images collected demonstrate equal or better image quality as compared to our predicates." However, it does not specify the sample size for this clinical image inspection, nor does it provide details about the data provenance (e.g., country of origin, retrospective or prospective nature). Given the nature of a 510(k) for a component change (the FPD), this might have been a limited comparative study rather than a large-scale clinical trial.

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

The document mentions "clinical image inspection" but does not provide any information on the number of experts involved in this inspection or their qualifications.

4. Adjudication Method for the Test Set

The document does not specify any adjudication method. It only mentions "clinical image inspection."

5. If a Multi Reader Multi Case (MRMC) Comparative Effectiveness Study was done, If so, what was the effect size of how much human readers improve with AI vs without AI assistance

No, a Multi Reader Multi Case (MRMC) comparative effectiveness study was not done. This device is a digital X-ray detector system, not an AI-assisted diagnostic tool. The comparison is between the new detector and a previous detector system, with the focus on image quality and equivalence, not reader performance improvement with AI.

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

This refers to an algorithm's performance without human intervention. Since the device is a digital X-ray detector system (hardware), not a diagnostic algorithm, this concept does not apply. The "standalone performance" for this device would relate to its hardware specifications and image acquisition capabilities, which are covered by bench and laboratory testing.

7. The Type of Ground Truth Used (expert consensus, pathology, outcomes data, etc.)

The available information suggests that the "ground truth" for the comparison was based on "clinical image inspection" and possibly "bench, and test laboratory" results, comparing the image quality of the new device to that of the predicate device. This implies a subjective assessment of image quality by potentially experts, but the specifics are not detailed. It is unlikely to involve pathology or outcomes data for this type of 510(k) submission.

8. The Sample Size for the Training Set

This submission is for a digital X-ray detector system, not an AI algorithm that requires a training set. Therefore, there is no training set in the context of this device.

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

As there is no training set for this hardware device, this question is not applicable.

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ViZion DR is intended for digital image capture use in general radiographic examinations for adult and pediattic, wherever conventional screen-film systems may be used, excluding fluoroscopy, angiography and mammography. ViZion allows imaging of the skull, chest, shoulders, spine, abdomen, pelvis and extremities.

The ViZion DR system represents the straightforward intearation of two cleared devices. ViZion DR is a Digital Radiography system, featuring an integrated flat panel digital detector (FPD) K090742 (Samsung Flat-Panel X-Ray Detector), Samsung Mobile Display Co., Ltd. (this is the 510(k) for the flat panel detector) and Viztek's proprietary OPAL-RAD PACS image viewing and acquire interface software technology, (K063337) which incorporates state of the art object-oriented software and connectivity. ViZion is designed to perform digital radiographic examinations in replacement for conventional film. This integrated platform provides the benefits of PACS with the with the advantages of digital radiography for a filmless environment and improves cost effectiveness. The major functions and principle of operation of the Viztek PACS and Samsung FPD were not changed.

The provided text is a 510(k) summary for the Viztek ViZion DR, a digital flat panel X-Ray detector system. It describes the device, its intended use, and states that bench and test laboratory results indicate the new device is as safe and effective as predicate devices, with clinical images demonstrating equal or better image quality.

However, the document does not contain specific acceptance criteria, detailed study results, sample sizes for test or training sets, ground truth establishment methods, information about expert involvement, adjudication methods, or MRMC study details. The 510(k) summary is a high-level comparison to predicate devices, focusing on substantial equivalence rather than a detailed performance study with quantifiable acceptance criteria.

Therefore, most of the requested information cannot be extracted from the provided text.

Here is what can be inferred or stated based on the text provided:

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

2. Sample sized used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)

• Sample Size (Test Set): Not specified. The document only states "Clinical images collected."
• Data Provenance: Not specified. It's unclear if the data was retrospective or prospective, or from which country.

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)

• Not specified. The document does not mention the involvement of experts in establishing ground truth or evaluating the clinical images.

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

• Not specified. 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

• Not specified. The document does not mention an MRMC study or any AI assistance. The device is a digital X-ray detector system, not an AI-powered analysis tool.

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

• Not applicable/Not specified. The device is a digital X-ray detector system, not an algorithm, so the concept of "standalone" performance for an algorithm isn't directly relevant in the context of this document. The document focuses on the image quality produced by the detector.

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

• Not specified. The document only states "Clinical images collected demonstrate equal or better image quality." It does not elaborate on how "image quality" was objectively assessed or against what ground truth.

8. The sample size for the training set

• Not applicable/Not specified. This device is an imaging hardware system. It does not appear to involve machine learning models that would require a "training set" in the conventional sense. The "training" here would refer to engineering and calibration, not data-driven model training.

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

• Not applicable/Not specified. (See point 8).

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The Cuattro UnoMD, when used with a cleared digital image capture device, provides for the capture of digital images in place of conventional film radiographic examinations. The device is intended to be available for retrofit on existing or planned x-ray machines with a cleared digital image capture device. The device is intended for use by trained and qualified personnel in the acquisition and review of radiographic images. The product is not intended for mammography or fluoroscopy applications.

The Cuattro UnoMD software is a Windows based software application capable of acquiring x-ray images from commonly commercialized digital flat panels. The software can use traditional mouse and keyboard inputs as well as touch screen monitors as an alternative. The use of the software enables the user to use a separate traditional x-ray generator and capture x-ray images without film. The images are processed and then presented to the user on a touch screen computer monitor, within 12 seconds after the x-ray exposure. The software also has capabilities to send images to hospital medical PACS systems and digital media for archival.

The Cuattro UnoMD system, as described in the provided 510(k) summary (K093480), relies on demonstrating substantial equivalence to a legally marketed predicate device (DEL-IMS, K063188) rather than meeting specific performance acceptance criteria through a clinical study with a predefined set of metrics. The submission focuses on non-clinical performance data and bench testing.

Here's an analysis based on the provided text, addressing your questions:

1. Table of Acceptance Criteria and Reported Device Performance

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

The document explicitly states that "Bench testing has been performed on the device following Cuattro's design control processes, as well as the applicable FDA guidance documents, in particular the guidance on Content of Premarket Submissions for Software Contained in Medical Devices."

• Sample Size: The document does not specify a quantitative sample size for a "test set" in the context of clinical images or patient data. The evaluation was non-clinical performance data and bench testing.
• Data Provenance: Not applicable in the context of a clinical test set. The evaluation focuses on the software's functional characteristics and compliance with standards, not on an analysis of clinical image data from a specific origin.

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

Not applicable. This was a 510(k) submission based on non-clinical performance data and bench testing, not a clinical study involving expert interpretation of images or establishment of ground truth for a diagnostic task.

4. Adjudication method for the test set

Not applicable, as there was no test set of clinical images requiring adjudication by experts.

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 comparative effectiveness study was performed or mentioned in the provided text. The submission focuses on the software's functionality and its equivalence to a predicate device, not on its impact on human reader performance. This device is a PACS system component; it's not described as an AI-powered diagnostic aid meant to directly improve human reader accuracy.

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

This point is somewhat addressed. The device is a "software application capable of acquiring x-ray images from commonly commercialized digital flat panels" and "processes and then presents" images. It's an image acquisition and archiving system, not an "algorithm" in the sense of a standalone diagnostic or analytical tool. Its performance is standalone in its core function (acquiring, processing, and presenting images) without human intervention in the image processing itself, but it still requires a human for interpretation. The bench testing performed would evaluate this standalone functionality.

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

Not applicable in the conventional sense. The "ground truth" for this type of submission (focusing on substantial equivalence for an image acquisition/management system) would be:

• Compliance with DICOM 3.0 standards.
• Successful acquisition and display of images.
• Processing time within specifications (e.g., 12 seconds).
• Correct interfacing with cleared digital image capture devices.
• Adherence to software design control processes.

These are verifiable through technical specifications, testing protocols, and direct observation, rather than clinical ground truth like pathology.

8. The sample size for the training set

Not applicable. The Cuattro UnoMD is a software system for image acquisition and management, not an AI/ML diagnostic algorithm that requires a training set of images.

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

Not applicable, as there is no training set mentioned for this type of device.

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