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These Digital Radiographic Systems are 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 supine position. Not for mammographic use.

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I am sorry, but the provided text is a letter from the FDA regarding a 510(k) premarket notification for "Mobivision Digital Mobile X-Ray Systems (various models)". It confirms the device's substantial equivalence to predicate devices and outlines regulatory requirements.

However, this document does not contain any information about acceptance criteria, study details, device performance, sample sizes, ground truth establishment, expert qualifications, or adjudication methods.

Therefore, I cannot fulfill your request to describe the acceptance criteria and the study that proves the device meets them based on the given text.

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This radiographic system 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.

This diagnostic x-ray system consists of a tubehead/collimator assembly mounted on a ceiling suspension along with a generator, generator control, and an elevating x-ray table. Power ratings for the available generators are in the rage of 32 kw to 80 kW. Exposure voltage range varies from 40 - 125 KV or 40 - 150 kV with current of 300 -100 mA. Exposure time is 1 ms - 10 s.

This 510(k) submission describes a diagnostic X-ray system and does not involve Artificial Intelligence (AI) or machine learning. Therefore, many of the requested criteria, such as those related to AI performance, sample sizes for training/test sets in an AI context, expert ground truth establishment for AI, MRMC studies for AI, or standalone AI performance, are not applicable to this document.

The acceptance criteria and "device performance" described in this document relate to the substantial equivalence of the new device (RADVISION ET) to a predicate device (RADVISION E and RADVISION EU) based on safety and effectiveness.

Here's the information that can be extracted based on the provided text:

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

The acceptance criteria here are implicitly met if the new device is deemed "substantially equivalent" to the predicate device. The performance is compared based on functional and safety characteristics.

The "study that proves the device meets the acceptance criteria" is described as:

• "The results of bench and test laboratory indicates that the new device is as safe and effective as the predicate devices."
• "After analyzing bench and external laboratory testing to applicable standards, it is the conclusion of Almana Medical Imaging that the RADVISION ET Diagnostic X-Ray Systems are as safe and effective as the predicate device, have few technological differences, and has no new indications for use, thus rendering them substantially equivalent to the predicate devices."

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

This information is not provided in the document. The submission references "bench and test laboratory" studies without specifying sample sizes for physical testing.

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)

This is not applicable as this is a traditional medical device submission, not an AI/ML submission requiring expert ground truth for image interpretation.

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

This is not applicable as this is a traditional medical device submission.

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

This is not applicable as this is a traditional medical device submission, not involving AI assistance.

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

This is not applicable as this is a traditional medical device submission, not involving an algorithm.

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

The "ground truth" in this context refers to the safety and effectiveness of the predicate device, established through its existing legal marketing and compliance with standards. The new device is compared to this established benchmark through technical specifications and bench/laboratory testing. There's no specific "ground truth" for diagnostic accuracy in the way it's used for AI algorithms.

8. The sample size for the training set

This is not applicable as this is a traditional medical device submission, not an AI/ML submission.

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

This is not applicable as this is a traditional medical device submission, not an AI/ML submission.

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The RFVision 9.9D is a high resolution, digital imaging system designed for digital videography. It is intended to replace conventional film techniques in multipurpose or dedicated applications when general fluoroscopy, interventional fluoroscopy or angiography or cardiac imaging procedures are performed. The RFVision 9.9D allows the operator to view and enhance digital fluoroscopic images. High resolution digital spot images may be acquired at single or rapid acquisition rates. Images may be viewed and enhanced enabling the operator to bring out diagnostic details.

RFVision 9.9 D is an advanced remote controlled 90/90 Digital RF system with floating table top for radiographic and fluoroscopy procedures. Reliable, time tested generator. High quality image intensifier and digital imaging system improves diagnostic capabilities assures filmless operations and smooth integration in digital PACS environment.

The provided text is a 510(k) summary for the RFVision 9.9D Image Intensified Fluoroscopic System. It does not contain any information regarding specific acceptance criteria, device performance metrics, or study details (like sample sizes, ground truth establishment, or expert qualifications) for proving the device meets acceptance criteria.

The 510(k) summary focuses on demonstrating substantial equivalence to a predicate device (K061173, CPIVision Digital Imaging System). The "Safety and Effectiveness, comparison to predicate device" section broadly states: "The results of bench, test laboratory and clinical testing indicates that the new device is as safe and effective as the predicate devices." However, it does not elaborate on what those tests were, what specific criteria they measured, or what the results were.

The "Substantial Equivalence Chart" primarily lists characteristics that are "SAME" as the predicate device, such as intended use, performance standard (21 CFR 1020.30), generator, and electrical safety. This indicates that the device is deemed effective and safe because it is similar to an already approved device, not because it underwent independent testing against new, specific performance criteria.

Therefore, I cannot provide the requested table or answer most of the specific questions as the necessary information is not present in the provided document.

Here's what I can extract or infer based on the document's content, with many fields remaining blank due to lack of information:

1. Table of Acceptance Criteria and Reported Device Performance

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

• Sample Size: Not specified.
• Data Provenance: Not specified, only generally referred to as "bench, test laboratory and clinical testing." No mention of country of origin or whether data was retrospective or prospective.

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

• Not specified. The document indicates "user testing data" but provides no details on how "ground truth" (if applicable in this context) was established or by whom.

4. Adjudication method for the test set

• Not specified.

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 information is provided. This device is an image-intensified fluoroscopic system, not explicitly an AI-assisted device in the context of this 510(k) summary.

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

• Not applicable/Not specified. This is a hardware imaging system, not an algorithm being evaluated without human interaction.

7. The type of ground truth used

• Not specified. Given the nature of a fluoroscopic system, "ground truth" would likely relate to image quality assessment, radiation dose, system reliability, and functional performance rather than diagnostic accuracy like in an AI algorithm. No details are given.

8. The sample size for the training set

• Not applicable/Not specified. There is no mention of a "training set" as this is a hardware device submission, not an AI/ML algorithm submission that would typically involve training data.

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

• Not applicable/Not specified. As above, no training set is mentioned.

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These are Radiographic X-Ray Systems with a flat panel detector(s), which allow the acquisition of x-ray exposures without the use of conventional film/screen systems. The systems allow radiographic exposures of the whole body including skull, spinal column, chest, abdomen, extremities, excluding mammography. Radiographic exposures may be taken with the patient in the sitting, standing, or lying positions.

DR Vision Neo is flat panel state-of-art digital imaging suite, driving significant improvements in productivity and quality. The streamlined efficiency of the DR Vision Neo enhance the entire radiographic operation, improving workflow by delivering diagnostic images instantly, and allowing users to move x-ray images clectronically to remote workstations, image archives, and printers. The DR Vision Neo is a Trixell Pixium 4600 flat panel detector based direct digital radiography system with ceiling suspended design. These are essentially standard diagnostic x-ray systems which have added to them a digital x-ray acquisition panel. The DR Vision Neo has a single panel while the DR Vision Duo has two panels, one in a wall stand and one in the patient table. Features are:

• Flat panel detector: Amorphous silicon panel with cesium iodide scintillator offers exceptional DQE 17" x 17" size eliminates the need to rotate the panel for transverse views l 43 x 143 micron pixels in matrix of 3121 x 3121 x 14 bits. * High-precision 9 million pixel resolution. * 14-bit digital data conversion with 16.000 grayscales. * Motorized detector panel movements with auto-tracking with ceiling suspended tube stand. Battery operated mobile elevating table for radiological applications with 4-way floating table top allows easy and quick patient positioning. * IHE/HL-7/DICOM Standard

The provided text is a 510(k) summary for the DR Vision Neo and DR Vision Duo Diagnostic X-Ray Systems, which focuses on establishing substantial equivalence to a predicate device. It does not contain detailed information about a clinical study with acceptance criteria and specific device performance metrics in the way a traditional performance study would.

Instead, the submission for these diagnostic X-ray systems relies on demonstrating substantial equivalence to a legally marketed predicate device (K061054 Siemens Axiom Aristos FX Plus Digital Radiography System). This is a common pathway for medical devices that are similar in technology and intended use to existing devices.

The "acceptance criteria" in this context are not performance thresholds for a disease detection task, but rather the criteria for demonstrating substantial equivalence to the predicate device. The "study" proving this is a comparison of technical characteristics and safety/effectiveness claims.

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

1. Table of Acceptance Criteria and Reported Device Performance

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

• The document mentions "bench, test laboratory and clinical testing," but does not specify a sample size for any test set or clinical study.
• Data Provenance: Not specified. There is no information on the country of origin of the data or if it was retrospective or prospective.

3. Number of Experts and Qualifications for Ground Truth

• The document does not specify the number of experts used to establish ground truth or their qualifications. The evaluation for substantial equivalence relies on comparing technical specifications and general safety/effectiveness, rather than expert-derived ground truth for diagnostic accuracy in a specific condition.

4. Adjudication Method

• Not applicable/Not specified. Since a specific clinical study with detailed diagnostic outcomes is not described, there is no mention of an adjudication method.

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

• No. The document does not describe a MRMC comparative effectiveness study or any effect size of human readers improving with AI vs. without AI assistance. This device is a diagnostic X-ray system, not an AI-powered diagnostic aid.

6. Standalone (Algorithm Only) Performance Study

• No. The document does not describe a standalone performance study. This is an X-ray imaging system, not an algorithm being evaluated for standalone performance.

7. Type of Ground Truth Used

• Not applicable in the context of diagnostic accuracy. The "ground truth" here is effectively the accepted technical specifications and safety profile of the predicate device, which the new device is compared against for substantial equivalence. There's no mention of pathology, outcomes data, or expert consensus being used as ground truth for diagnostic performance of the device in a clinical setting in this summary.

8. Sample Size for the Training Set

• Not applicable/Not specified. This document does not describe an AI or machine learning model that would require a "training set." It is related to the hardware and basic functionality of a digital X-ray system.

9. How Ground Truth for the Training Set Was Established

• Not applicable/Not specified. As there is no training set mentioned, there is no information on how its ground truth would be established.

Conclusion from the document:

The regulatory submission for the DR Vision Neo and DR Vision Duo Diagnostic X-Ray Systems establishes substantial equivalence by demonstrating that they have the same intended use, comply with the same performance and safety standards, have comparable power ranges, and utilize the identical digital panel as the legally marketed Siemens Axiom Aristos FX Plus Digital Radiography System. The conclusion is that the new devices are as safe and effective as the predicate, with few technological differences and no new indications for use.

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These Radiographic Systems are 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 Rad Vision E is a standard configuration fixed column diagnostic radiographic system. The column can move right or left on a track and the tube head can move up and down. Rad Vision eu is a universal swivel arm X-ray system. It is easy to operate and permits a swift radiographic procedure, a feature which applies to all conventional exposure techniques on all parts of the body. The system is composed of a floor-to-wall column and a turnable arm. On the arm is the tubehead with a collimator mounted to it. All components required for a complete system are supplied. With the patient table, the patient can be moved into any required position without the need for repositioning. Therefore it offers the same advantages as a bucky radiography table. Owing to the large vertical movement of the swivel arm patients in the standing position can be examined from head to feet.

The provided text is a 510(k) summary for the Rad Vision E and Rad Vision eu Diagnostic X-Ray Systems, along with the FDA's clearance letter. This document focuses on the substantial equivalence of the device to a predicate device, rather than a detailed study proving performance against specific acceptance criteria for a novel AI/software medical device.

Therefore, many of the requested elements (like a table of acceptance criteria and proven performance, sample sizes for test/training sets, expert qualifications, adjudication methods, MRMC study results, or detailed ground truth information) are not typically found in this type of 510(k) submission for a conventional X-ray system. These aspects are more central to the regulatory submission for AI/ML-driven or image-analysis software devices that require validation of diagnostic accuracy.

The document indicates "The results of bench, test laboratory and clinical testing indicates that the new device is as safe and effective as the predicate devices," but does not elaborate on the specifics of these tests or acceptance criteria beyond general safety and effectiveness in comparison to the predicate.

Here's an attempt to answer the questions based only on the provided text, acknowledging that much of the requested information is not present:

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

The document does not explicitly state specific quantitative acceptance criteria or detailed reported device performance metrics in the format of a table you'd expect for an AI/software device. The overarching "acceptance criterion" inferred is that the new device is as safe and effective as the predicate device.

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

Not explicitly stated in the provided text. The document refers to "clinical testing" but does not provide details on sample size, data provenance, or study design (retrospective/prospective).

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 or not explicitly stated. For a conventional X-ray system submission, the "ground truth" relates more to the physical performance and image quality of the X-ray machine itself, typically evaluated through engineering tests, phantom studies, and possibly clinical trials (without needing a separate expert panel to establish "ground truth" for diagnostic images in the way an AI algorithm would).

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

Not applicable or not explicitly stated.

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 such MRMC comparative effectiveness study is mentioned. This type of study is relevant for AI-assisted diagnostic tools, not typically for the clearance of a conventional X-ray hardware system.

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

This question is not applicable as the device is a diagnostic X-ray system (hardware), not an algorithm or software-only device.

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

For a conventional X-ray system, "ground truth" in terms of diagnostic images is less about establishing a definitive diagnosis on images and more about ensuring the system produces diagnostically acceptable images, measured by objective image quality metrics, dose, and clinical utility. The document does not specify the method for establishing ground truth for any "clinical testing" mentioned.

8. The sample size for the training set

Not applicable. The device is a hardware X-ray system; it does not involve a "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 this type of device.

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