Search Results

The Planar Dome® EX line Model Dome® E2c is intended to be used in displaying and viewing digital medical images for review, analysis, and diagnosis by trained medical practitioners.

This device must not be used in primary image diagnosis in mammography.

The Planar Dome® EX line Model Dome® E2c is a flat panel hi-resolution AMLCD monitor system for displaying medical images. The system consists of an AMLCD monitor and a high-resolution graphic control board that connects to a PACS workstation for image display. The controller board is installed into the PACS workstation computer or other computer system used to display PACS medical images. CXtra is user-friendly software, the purpose of which is to optimize the display for DICOM-compliant viewing.

The provided text is a 510(k) Summary for the Planar Dome® EX line Model Dome® E2c, which is a medical image display system. The document focuses on demonstrating substantial equivalence to a predicate device and does not contain information about acceptance criteria or a study proving the device meets those criteria in the way a clinical performance study would.

Instead, the summary states that the device "has been tested to various Standards and was validated for its intended use" and that "Performance attributes are similar" to the predicate device. It also mentions that the device will "Follow DICOM PS3.14 'Grayscale Standard Display Function'". This implies that the acceptance criteria are related to compliance with these standards and similar performance to the predicate, rather than clinical efficacy metrics typical of AI/CAD devices.

Given the information, I will interpret "acceptance criteria" as the performance attributes and standards the device aims to meet.

Here's the breakdown of the information that can be extracted or reasonably inferred from the provided document, addressing your points:

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

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 does not describe a clinical study with a "test set" in the context of comparing algorithm performance on medical images. The device is a display monitor. The "testing" mentioned refers to compliance with standards and validation of the display system's functionality. Therefore, information on sample size or data provenance is not applicable and not provided.

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. As a display device, there is no "ground truth" to be established by experts for image interpretation in this context. The device's performance is likely evaluated against technical specifications and display standards.

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

Not applicable, as there is no test set for clinical interpretation.

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 done. This device is a display monitor and does not incorporate AI or provide assistance to human readers in the way an AI/CAD system would. Its primary function is to render images accurately.

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

Not applicable. This is a display device, not an algorithm.

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

Not applicable. The "ground truth" for a display device would relate to its technical specifications and adherence to display standards (e.g., DICOM grayscale display function), which are measured objectively using calibrated equipment.

8. The sample size for the training set

Not applicable. This device is a hardware display system, not a machine learning model.

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

Not applicable. This device is a hardware display system, not a machine learning model.

Ask a specific question about this device

The Planar DOME DIGITAL FLAT-PANEL DISPLAY SYSTEM™, Model E4c™ is intended to be used in displaying and viewing medical images for review and analysis by trained medical practitioners.

This device must not be used in primary image diagnosis in mammography.

The Planar DOME EX™ DIGITAL FLAT-PANEL DISPLAY SYSTEM™, Models E4c Color™ is a flat panel hi-resolution LCD monitor systems for displaying medical images. The system consists of a LCD monitor and a high-resolution graphic control board that connects to a PACS workstation for image display. The controller board is installed into the PACS workstation computer or other computer system used to display PACS medical images. Cxtra is user-friendly software that purpose is to optimize the display for DICOM-compliant viewing.

The provided text describes a 510(k) summary for the Planar DOME EX SERIES DIGITAL FLAT-PANEL DISPLAY SYSTEM™, Model E4c Color™. This document is a pre-market notification for a medical device seeking clearance from the FDA, asserting that the new device is substantially equivalent to existing legally marketed predicate devices.

The information provided focuses on the regulatory aspects and comparison to predicate devices, rather than a detailed study design proving performance against specific acceptance criteria for an AI model. This device is a display system, not an AI diagnostic algorithm. Therefore, many of the requested items related to AI study design (like sample size for test/training sets, ground truth establishment, expert adjudication, MRMC studies) are not applicable to this type of medical device submission.

Here's an attempt to answer the questions based only on the provided text, highlighting where the information is not present or not applicable:

1. Table of Acceptance Criteria and Reported Device Performance

The document does not explicitly state "acceptance criteria" in a quantitative, measurable way for an AI algorithm's performance. Instead, it asserts substantial equivalence based on similarities to predicate devices in terms of:

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

• Sample size for the test set: Not applicable and not mentioned. This is a display system, not an AI algorithm evaluated on a dataset of cases.
• Data provenance (e.g., country of origin of the data, retrospective or prospective): Not applicable and not mentioned.

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

• Not applicable and not mentioned. The device is a display, and there is no mention of a ground truth established by experts for performance evaluation in the context of diagnostic accuracy.

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

• Not applicable and not 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. This study is not relevant to an MRMC comparative effectiveness study, as the device is a display system and not an AI assistant.

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

• No. This is not an AI algorithm, so a standalone performance evaluation of an algorithm is not applicable.

7. The type of ground truth used

• Not applicable. The "ground truth" for this device would be its technical specifications (e.g., luminance, resolution, color accuracy) meeting industry standards (like DICOM PS3.14) and functional requirements for displaying medical images, not diagnostic outcomes or pathology reports. The document states it "has been tested to various Standards and was validated for its intended use."

8. The sample size for the training set

• Not applicable and not mentioned. There is no AI model or training set involved.

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

• Not applicable and not mentioned. There is no AI model or training set involved.

Ask a specific question about this device

The DOME CX™ DIGITAL FLAT-PANEL DISPLAY SYSTEM™, Models C3 Color™ and C3 Gray™ are intended to be used in displaying and viewing medical images for review and analysis by trained medical practitioners.

The DOME CX™ DIGITAL FLAT-PANEL DISPLAY SYSTEM™, Models C3 Color™ and C3 Gray™ are flat panel hi-resolution LCD monitor systems for displaying medical images. The system consists of a LCD monitor and a highresolution graphic control board that connects to a PACS workstation for image display. The controller board is installed into the PACS workstation computer or other computer system used to display PACS medical images.

The provided document describes a 510(k) submission for a medical image display system. It is a pre-market notification to establish substantial equivalence to a predicate device, not a study evaluating device performance against acceptance criteria in the way a clinical trial for an AI diagnostic device would.

Therefore, most of the requested information regarding acceptance criteria, study details, sample sizes, expert ground truth, adjudication, and MRMC studies is not applicable to this type of regulatory submission for a display system. The document focuses on regulatory compliance and substantial equivalence rather than a clinical performance study.

Here's an attempt to extract relevant information and explain why other points are not present:

1. Table of Acceptance Criteria and Reported Device Performance

This information is not explicitly stated in the document as performance metrics with numerical targets. For a display system like this, "acceptance criteria" likely refer to meeting performance specifications similar to the predicate device and relevant industry standards (e.g., DICOM, display luminance, contrast, resolution, uniformity). The document only states that the device contains "adequate information and data to enable FDA - CDRH to determine substantial equivalence to the predicate device" and that the device will be manufactured "in accordance with voluntary and safety standards."

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

Not Applicable. This document describes a display system, not an AI or diagnostic device that would undergo testing with a "test set" of medical images for diagnostic performance evaluation. The "data" being referenced would be the technical specifications and performance data of the display itself, demonstrating its capabilities as a medical image display.

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

Not Applicable. As above, there is no "test set" in the context of clinical ground truth for a display system.

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

Not Applicable.

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 display system, not an AI or diagnostic tool. Therefore, no MRMC study, AI assistance comparison, or effect size is relevant or mentioned.

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

Not Applicable. This is a hardware display system, not an algorithm.

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

Not Applicable. For a display system, the "ground truth" would relate to its technical specifications (e.g., color accuracy, luminance uniformity, resolution, contrast ratios) as measured by calibrated instruments, rather than diagnostic "ground truth" on medical pathology. This information is not detailed in the summary.

8. The sample size for the training set

Not Applicable. This is a hardware display system, not a machine learning model that requires a training set.

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

Not Applicable.

Ask a specific question about this device

The DOME CX™ DIGITAL FLAT-PANEL DISPLAY SYSTEM™, Models C2™ and C5i™ are intended to be used in displaying and viewing medical images for review and analysis by trained medical practitioners.

The DOME CX™ DIGITAL FLAT-PANEL DISPLAY SYSTEM™, Models C2™ and C5i™ are flat panel hi-resolution LCD monitor systems for displaying gray scale medical images for diagnostic and referral use. The Displays use active matrix liquid-crystal display (AMLCD) panels that generate lower electro-magnetic emissions and heat, and consume less power than traditional CRT displays. The Displays use a common internal interface controller that connects directly to a common graphics display controller via a Digital Visual Interface (DVI) 1.0 interface. The Displays' Thin Film Transistors (TFTs) control transmissive liquid-crystal elements and use intedrated Cold Cathode Fluorescent Tube (CCFT) backlight systems. The DOME C2 displays two megapixels of data in a portrait or landscape orientation of 1200 x 1600 8-bit pixels. The DOME C5i displays five megapixels of data in a portrait or landscape orientation of 2048 x 2560 8-bit pixels. Both display systems include the LCD display panel, integrated drive electronics, integrated backlight, and an external power supply. Optional connections to USB can provide enhanced system functionality, including kevboard, mouse, and calibration technology.

Here's an analysis based on the provided text, outlining the acceptance criteria and the study details:

Device: DOME CX™ DIGITAL FLAT-PANEL DISPLAY SYSTEM™, Models C2™ and C5i™

1. Table of Acceptance Criteria and Reported Device Performance

The provided document (K032202) is a 510(k) summary for a medical device. For image display systems, the acceptance criteria are generally related to demonstrating substantial equivalence to a legally marketed predicate device rather than specific performance metrics like sensitivity or specificity for disease detection. The performance is assessed against the predicate's established technical characteristics and intended use.

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

The document does not describe a specific test set of medical images or data. The evaluation is focused on the device's technical specifications and how they compare to a predicate device, as this is a 510(k) for a display system, not an algorithm that interprets images. The "performance" being evaluated is the display's ability to render images according to its specifications, which are deemed equivalent to the predicate. Therefore, there's no mention of:

• Sample size for a test set
• Data provenance (country of origin, retrospective/prospective)

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

Not applicable. As noted above, there isn't a test set of images requiring expert ground truth for this type of device (a display system). The assessment is on the display's characteristics themselves.

4. Adjudication Method for the Test Set

Not applicable, as there is no test set or clinical endpoint requiring adjudication.

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

Not applicable. This is a display system, not an AI algorithm designed to assist human readers in image interpretation. Therefore, an MRMC study demonstrating human reader improvement with AI assistance would not be relevant for this device.

6. If a Standalone (Algorithm Only Without Human-in-the-Loop Performance) Was Done

Not applicable. This device is a display system, not an algorithm. Its function is to present images for human interpretation, not to provide standalone diagnostic output.

7. The Type of Ground Truth Used

Not applicable. There is no diagnostic "ground truth" established as part of this submission, as the device's function is image display, not image analysis or diagnosis. The "ground truth" in this context would implicitly be the technical specifications (e.g., pixel count, luminance, contrast) of the predicate device.

8. The Sample Size for the Training Set

Not applicable. This device is a display system; it does not employ machine learning or require a training set of data.

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

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

Ask a specific question about this device