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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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This digital 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, extremittes, and other body parts. Applications can be performed with the patient sitting, standing, or lying in the prone or supine position. Excludes fluoroscopy, angiography, and mammography.

This digital diagnostic x-ray system consists of a tubehead/collimator assembly mounted on a ceiling suspension OR a U-Arm, along with a generator, generator control, and an elevating x-ray table. Power ratings for the available generators are in the rage of 50 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. Models: Model DR3000 (U-arm single detector) and Model DR4000 (dual detector-ceiling suspension and table and upright bucky) and DR1000 single detector (standing bucky and ceiling suspension. The digital panel is the Pixium 3543pR (K090625) and the digital subsystem is the SmartRad, CMT Medical Technologies, K003438.

Here's an analysis of the provided text regarding the acceptance criteria and study for the Viztek WL Series Digital Diagnostic Digital X-Ray Systems (K091752):

It is important to note that the provided text is a 510(k) summary, which focuses on demonstrating substantial equivalence to a predicate device rather than presenting extensive clinical study data for new acceptance criteria. Therefore, much of the requested information regarding detailed study design, sample sizes, expert qualifications, and specific performance metrics tested against pre-defined acceptance criteria is not explicitly present in this type of document.

The "acceptance criteria" in this context are primarily tied to demonstrating the Viztek WL Series is as "safe and effective" and "substantially equivalent" to its predicate devices (K082604, K090625, and K003438). This usually involves showing that performance characteristics (like pixel size, software, safety standards) are comparable, and there are no new risks or concerns.

1. Table of Acceptance Criteria and Reported Device Performance

Given the nature of a 510(k) summary for substantial equivalence, the "acceptance criteria" are implied by the characteristics of the predicate device. The "reported device performance" is essentially that it meets or is comparable to these characteristics.

Summary of "Acceptance": The device's performance is deemed "as safe and effective" and "substantially equivalent" to the predicate devices due to having "few technological differences" and "no new indications for use." The primary "acceptance" relies on meeting the same regulatory standards and having comparable technical specifications.

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

The document states, "The results of bench and test laboratory indicates that the new device is as safe and effective as the predicate devices," and "After analyzing bench and external laboratory testing to applicable standards..."

• Sample Size for Test Set: Not specified. The document does not detail specific sample sizes for any testing (e.g., number of images, number of patients).
• Data Provenance: The testing is referred to as "bench and external laboratory testing." The country of origin for this data is not specified, nor is whether it was retrospective or prospective. It's typical for bench testing to not involve patient data in the same way clinical trials do.

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

Not applicable/Not specified. This type of 510(k) summary for an X-ray system, especially one establishing substantial equivalence based on technical specifications and safety standards, does not typically involve expert review for "ground truth" derived from patient images in the way AI/CAD devices do. The "ground truth" here is compliance with technical specifications and safety standards.

4. Adjudication Method for the Test Set

Not applicable/Not specified. As no expert review for "ground truth" is mentioned, no adjudication method would be detailed.

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

No. A multi-reader multi-case (MRMC) comparative effectiveness study is not mentioned in this 510(k) summary. This document is for a digital X-ray system, not an AI or CAD system designed to aid human readers. Therefore, there's no discussion of human reader improvement with or without AI assistance.

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

Not applicable/Not specified. This is a digital X-ray system, not an AI algorithm. Its performance is measured by its ability to acquire diagnostic images according to established standards and technical specifications, not as a standalone diagnostic algorithm.

7. Type of Ground Truth Used

The "ground truth" for this submission is based on:

• Bench and external laboratory testing to applicable standards: This implies objective measurements of physical characteristics, adherence to electrical safety standards (IEC-60601), and performance standards (21 CFR 1020.30).
• Comparison of technical specifications: Matching or demonstrating acceptable differences in parameters like pixel size and other hardware components against the predicate device.

It does not rely on expert consensus, pathology, or outcomes data, as it's not evaluating diagnostic accuracy of a finding but rather the system's ability to produce images safely and effectively.

8. Sample Size for the Training Set

Not applicable/Not specified. This document does not describe the development of an AI algorithm with a training set. The software component mentioned (SmartRad) is an existing, previously cleared device (K003438), which would have had its own validation.

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

Not applicable/Not specified. As there is no AI algorithm being developed by Viztek for this submission that would require a dedicated training set, this information is not available in the document.

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This digital 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, extremitics, and other body parts. Applications can be performed with the patient sitting, standing, or lying in the prone or supine position.

This digital diagnostic x-ray system consists of a tubehead/collimator assembly mounted on a ceiling suspension OR a U-Arm, along with a generator, generator control, and an elevating x-ray table. Power ratings for the available generators are in the rage of 50 kw to 80 kW. Exposure voltage range varies from 40 - 125 K V or 40 - 150 k V with current of 300 - 100 mA. Exposure time is 1 ms - 10 s. Models: Model DR3000 (U-arm single detector) and Model DR4000 (dual detector-ceiling suspension and table and upright bucky) and DR1000 single detector (standing bucky and cciling suspension. The digital subsystem is the SmartRad (CMT Medical Technologies) K003438.

This 510(k) summary (K082604) for the Viztek DR Series Digital Diagnostic Digital X-Ray Systems (Multiple Models) is a submission for substantial equivalence to predicate devices, not a study presenting novel performance data. Therefore, many of the requested elements regarding acceptance criteria and study details are not directly applicable.

Here's an analysis based on the provided document:

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

The document does not specify quantitative acceptance criteria in terms of clinical performance metrics (e.g., sensitivity, specificity, accuracy) because it is a submission for substantial equivalence, not a direct performance study against a predefined clinical benchmark. The acceptance criteria essentially revolve around demonstrating equivalence to legally marketed predicate devices in terms of technical characteristics and intended use.

Note on "Digital Panel" and "Number of panels": While these are present in the comparison chart, they highlight differences rather than direct "acceptance criteria" for performance. The Viztek device uses a Pixium 4600 panel and can have one or two panels, while the predicate uses a CANON CXDI 50G and has one. The implication is that these differences do not raise new questions of safety or effectiveness.

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

Not applicable. This is a 510(k) submission for substantial equivalence, not a clinical study involving a test set of patient data for performance evaluation. The "tests" mentioned are bench and laboratory tests, not clinical evaluations with a patient sample.

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

Not applicable. No clinical ground truth establishment or expert review of a test set of images is described for this type of submission.

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

Not applicable. No clinical test set requiring adjudication is described.

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

Not applicable. This device is a digital X-ray system, not an AI-powered diagnostic tool, and no MRMC study is described. The submission focuses on the hardware and its equivalence.

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

Not applicable. This device is a digital X-ray system, and there is no "algorithm only" performance reported in this context. The device's primary function is to acquire X-ray images.

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

Not applicable in the clinical sense. The "ground truth" for this submission is adherence to recognized performance standards (e.g., 21 CFR 1020.30, IEC-60601 for electrical safety), and the technical specifications and intended use of the legally marketed predicate devices.

8. The sample size for the training set

Not applicable. This device does not involve a "training set" in the context of machine learning or AI.

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

Not applicable. No training set is involved.

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Opal-RAD™ is a software device that receives digital images and data from various sources (i.e. CT scanners, MR scanners, ultrasound systems, R/F Units, computed & direct radiographic devices, secondary capture devices, scanners, imaging gateways or other imaging sources). Images and data can be stored, communicated, processed and displayed within the system and or across computer networks at distributed locations.

Lossy compressed mammographic images are not intended for diagnostic review. Mammographic images should only be viewed with a monitor approved by FDA for viewing mammographic images. For primary diagnosis, post process DICOM "for presentation" images must be used.

Opal - RAD is a software suite of web based PACS applications that was developed specifically to handle the DICOM protocol, for both transmitting and viewing DICOM images and data elements. The applications were developed so that access to the PACS can occur from any Microsoft Windows computer with internet capabilities, and offer an interface that users find to be quite intuitive after some initial learning. The Opal-RAD applications deal with all manner of DICOM images and modalities, including MR, CT, CR, US, and many others. These images can be viewed, manipulated, annotated, transmitted to other facilities, printed, animated and stored using the Opal-RAD suite.

The device is designed to be deployed over conventional networking infrastructure available in most healthcare organizations, and utilizes commercially available computer platforms (Intel Pentium-based) and operating systems (Microsoft Windows 2000, Windows NT, and Windows 98). The system does not produce any original medical images.

This document is a 510(k) summary for the Opal-RAD™ Picture Archiving Communications System (PACS). It primarily focuses on demonstrating substantial equivalence to a predicate device and does not contain detailed information about specific performance acceptance criteria or a dedicated clinical study proving device performance against those criteria.

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

Here's a breakdown of what can and cannot be provided based on the input:

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

• Cannot be provided. The document does not describe specific acceptance criteria (e.g., sensitivity, specificity, accuracy thresholds) or present a table of device performance metrics against such criteria. It states that the device was "manufactured in accordance with the voluntary standards listed in the enclosed voluntary standard survey" and that a "hazard analysis" was performed with a "Level of Concern for potential hazards has been classified as minor." However, these are not quantitative performance metrics against acceptance criteria typically found in clinical studies for diagnostic or AI-enabled devices.

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

• Cannot be provided. The document does not describe a test set, sample size, or data provenance for any performance evaluation.

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)

• Cannot be provided. No information about ground truth establishment or experts is present, as no specific performance study is detailed.

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

• Cannot be provided. No adjudication method is mentioned as there's no described test set or ground truth establishment process.

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

• Cannot be provided. This document is for a PACS system, which primarily handles the display, storage, and communication of medical images. It is not an AI-assisted diagnostic tool, and therefore, no MRMC study or AI-related effect size information would be present. The document explicitly states: "The system does not produce any original medical images." and "A physician, providing ample opportunity for competent human intervention interprets images and information being displayed and printed."

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

• Cannot be provided. As a PACS system, it's designed to facilitate human interpretation, not to provide standalone algorithmic diagnoses.

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

• Cannot be provided. No ground truth is mentioned in relation to specific device performance.

8. The sample size for the training set

• Cannot be provided. There is no mention of a training set, as this is a PACS system and not an AI/machine learning diagnostic device.

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

• Cannot be provided. No training set or ground truth establishment for a training set is described.

Summary of Device and 510(k) Scope:

The Opal-RAD™ is a Picture Archiving Communications System (PACS) software suite. Its primary function is to:

• Receive digital images and data from various sources (CT, MR, US, CR, DR, etc.).
• Store, communicate, process, and display these images and data within the system or across networks.

This 510(k) submission demonstrates substantial equivalence to an existing legally marketed predicate device (RATTAN IMACS K032176). The core argument for substantial equivalence for a PACS system often revolves around comparing its technical characteristics (e.g., DICOM compatibility, display capabilities, networking, security, data integrity) and its intended use with the predicate, rather than presenting a clinical study with detailed performance metrics like sensitivity or specificity.

The document highlights:

• Its capability to handle DICOM protocol.
• Web-based access from Microsoft Windows computers.
• Compatibility with common networking infrastructure and hardware (Intel Pentium-based).
• Crucially, it emphasizes that it does not produce any original medical images and that "A physician, providing ample opportunity for competent human intervention interprets images and information being displayed and printed."
• It also includes specific warnings regarding mammographic images (lossy compressed not for diagnostic review, use FDA-approved monitors, "for presentation" images for primary diagnosis).

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