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The device is intended to display medical images from endoscopic camera systems and other compatible medical image systems.

The device is a medical grade monitor for real-time use during endoscopic procedures and is suitable for use in hospital operating rooms, surgical centers, doctor's offices, clinics and similar medical environments.

The MDEC-2523 is a medical grade monitor with a 23,8 inch color TFT-LDC screen size and is intended to display medical images from endoscopic or laparoscopic camera systems and other compatible medical image systems.

The MDEC-2523 consists of an LCD panel and LED backlight integrated into a plastic housing body with an internal metal chassis structure, that also integrates the electronics, sensors, and interconnections. The MDEC-2523 encloses user controls that are used to control and configure the device via the embedded on screen display (OSD) software. The device has a glass cover to protect the LCD panel and allow easy cleaning. The MDEC-2523 is powered by means of an internal power supply.

This monitor displays color video images that are output from medical imaging systems on the LCD (liquid crystal display) panel.

The provided document is a 510(k) premarket notification letter from the FDA regarding a medical LCD monitor (MDEC-2523). It primarily focuses on demonstrating substantial equivalence to a predicate device based on technical specifications and adherence to regulatory standards.

Crucially, this document does not include information about a study proving the device meets acceptance criteria related to a clinical performance claim, specifically one involving AI, human readers, or ground truth establishment based on expert consensus, pathology, or outcomes data.

The acceptance criteria and study described in the input prompt are typically associated with devices that make diagnostic or therapeutic claims based on analyzing medical images or data, often involving AI or software. This document is for an LCD monitor, which is a display device. Its performance is assessed through electrical safety, EMC, environmental, and software testing to ensure it correctly displays images without introducing new safety or effectiveness concerns, rather than evaluating its accuracy in diagnosing conditions or improving human reader performance.

Therefore, I cannot fulfill the request to describe the acceptance criteria and the study as outlined in the prompt's nine points because the provided text does not contain this type of study information.

The non-clinical performance testing conducted for this device, as stated in the document, includes:

• Electrical safety testing (ANSI AAMI E560601-1)
• EMC testing (IEC 60601-1-2 Edition 4.1)
• Environmental testing (Climate, shock and vibration, packed, thermal analysis)
• Software testing
• Usability/human factors

The document explicitly states: "Clinical study - Not Applicable." This further confirms that the type of study outlined in the prompt (involving expert consensus, mrmc, standalone performance etc.) was not performed or required for this device.

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The display is intended to be used in displaying and viewing digital images, including standard and multiframe digital mammography, for review, analysis and diagnosis by trained medical practitioners. It is especially designed for breast tomosynthesis (3D mammography) applications, breast MRI and breast US. It is especially designed for CT and ultrasound including vascular and gynaecological US.

The Coronis OneLook® (MDMC-32133) is a medical computer display designed for general radiology, mammography and tomosynthesis applications. The device can also be used for home reading in radiology.

The display is a high-resolution LCD monitor with improved technical chat are important for accurate medical image review: high luminance, good luminance ratio and luminance ratio and luminance stability. The medical display comes with special image-enhancing technologies to ensure consistent brightne of the display, noise-free images (=good luminance uniformity), ergonomic reading and automated compliance with DICOM and other medical image quality standards and guidelines. These technologies help the radiologist to make a swift and accurate diagnosis.

The displays can be used optionally with the downloadable QAWeb Enterprise software, listed under D332294 as a class 1 device with product code LHO. QAWeb Enterprise is a calibration software that is intended as a quality assurance software for the displays. QAWeb Enterprise software helps to keep the display DICOM compliant.

The display can be used optionally with the downloadable Intuitive Workflow Tools, cleared in K191845 as a class 2 device with product code PGY. The Intuitive Workflow Tools are accessories for image enhancement on diagnostic displays:

• SpotView: The Barco SpotView display feature allows focusing on a region of interest in an image by boosting the display's backlight such that the maximum luminance in provided inside the region of interest. SpotView also enables focused observation during reading by dimming images outside the region of increasing the contrast in this region. Also magnification and inversion of pixels are possible with SpotView.

-AAM – Application Appearance Manager: This workflow tool allows you to set the luminance as well as the color space for each application that is on the workstation. There are often multiple windows open on a screen, but not all of them need the hightness

These applications are addressed by the general term 'Intuitive Workflow Tools'. The integration of the Intuitive Workflow Tools with the displays have been de-risked, verified and validated to ensure that they do not affect the safety and effectiveness of the displays.

The display can be used with or without the Barco MXRT display controller boards. The display controller board is installed in a PACS workstation computer, connected to the display can optionally be used with a touchpad, which is a controlling device (e.g. like a mouse or trackball) that makes it easier to work with diagnostic images and to use the IWTs.

The provided text describes a 510(k) premarket notification for a medical display device, Coronis OneLook (MDMC-32133). The document primarily focuses on demonstrating substantial equivalence to a predicate device (MDMC-12133, Coronis Uniti) through bench testing of technical characteristics.

Here is an analysis 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 document outlines a series of performance bench tests conducted on the MDMC-32133 in comparison to the MDMC-12133, referencing the "Guidance for Industry and FDA Staff: Display Devices for Diagnostic Radiology" (2022). The text generally states that the MDMC-32133 has "similar characteristics" and "did not reveal new issues of safety and performance." However, it does not provide specific numerical acceptance criteria or detailed quantitative reported performance values for each characteristic. Instead, it concludes equivalence based on these test outcomes.

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

The document describes "performance bench tests" that were performed. This implies testing on physical units of the device. It does not specify the sample size (number of devices tested) for these bench tests. The data provenance is not explicitly mentioned as retrospective or prospective in the context of data used for testing, but rather refers to the device characteristics under comparison.

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

This information is not applicable as the study described is purely a technical bench test comparison of display performance, not a study involving human interpretation of medical images or establishment of ground truth for medical diagnoses.

4. Adjudication Method for the Test Set:

This information is not applicable for the reasons stated in point 3.

5. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study Was Done:

No, an MRMC comparative effectiveness study was not done. The document explicitly states: "No animal testing or clinical testing has been performed." The study focuses on technical characteristics of the display itself, not on the effectiveness of human readers using the display for diagnosis.

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

This is not applicable in the context of an algorithm's performance, as the device is a medical display, not an AI algorithm. The performance evaluation is for the display's technical specifications.

7. The Type of Ground Truth Used:

The "ground truth" for this type of test is typically established by industry standards and specifications for display performance, as well as the performance of the legally marketed predicate device (MDMC-12133). The tests are designed to measure intrinsic physical properties of the display hardware against these established benchmarks.

8. The Sample Size for the Training Set:

This information is not applicable as the device is a display hardware, not an AI model that requires a training set.

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

This information is not applicable for the reasons stated in point 8.

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The display is intended to be used for displaying and viewing digital images (excluding digital mammography) for review and analysis by trained medical practitioners.

The No Color 8MP (MDNC-8132) is a medical computer display designed for general radiology applications. The device can also be used for home reading in radiology. The display is a high-resolution LCD monitor with characteristics that are important for accurate medical image review: high luminance, good luminance uniformity, good luminance stability. The medical display comes with special image-enhancing technologies to ensure consistent brightness over the display, noise-free images (=good luminance uniformity), ergonomic reading and automated compliance with DICOM and other medical image quality standards and guidelines. These technologies help the radiologist to make a swift and accurate diagnosis. The displays can be used optionally with the downloadable QAWeb Enterprise software, listed under D332294 as a class 1 device with product code LHO. QAWeb Enterprise is a calibration software that is intended as a quality assurance software for the displays. QAWeb Enterprise software helps to keep the display DICOM compliant. The display can be used optionally with the downloadable Intuitive Workflow Tools, cleared in K191845 as a class 2 device with product code PGY. The Intuitive Workflow Tools are accessories for image enhancement on diagnostic displays: SpotView and AAM – Application Appearance Manager. The display can be used with or without the Barco MXRT display controller boards.

The provided document is a 510(k) summary for the Barco Nio Color 8MP (MDNC-8132) medical display. It details the device, its intended use, and a comparison to a predicate device (Nio Color 2MP, MDNC-2521) to demonstrate substantial equivalence.

Based on the information provided, here's an analysis of the acceptance criteria and the study proving the device meets them:

Crucially, this document does not describe a study involving AI or human reader performance. It describes a comparison of a medical display device to a predicate device based on technical specifications and bench testing. Therefore, many of the requested points related to AI, human readers, ground truth establishment, and training sets are not applicable to this submission.

Acceptance Criteria and Reported Device Performance

The acceptance criteria for this device appear to be based on demonstrating "similar characteristics" and "equivalence" to the predicate device MDNC-2521 across various physical and performance metrics for a medical display. The general acceptance is that the modifications "did not reveal new issues of safety and performance" compared to the cleared predicate device.

Table of Acceptance Criteria and Reported Device Performance

Study Details (as applicable to a medical display submission, not AI)

Given that this is a submission for a medical display device demonstrating substantial equivalence to a predicate, and not an AI/software as a medical device (SaMD), the following points are largely not applicable or interpreted within the context of hardware testing.

1. Sample size used for the test set and the data provenance:

   • The test set consists of the Barco Nio Color 8MP (MDNC-8132) device itself. The "sample size" is essentially n=1 (one type of device being tested). There isn't a "data set" in the traditional sense of medical images or patient data.
   • Data provenance: Not applicable in the sense of patient data. The tests were performed on the physical device as per "Guidance for Industry and FDA Staff: Display Devices for Diagnostic Radiology", issued in 2022.

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

   • Not applicable. There is no "ground truth" to be established by experts in the context of diagnostic performance on images, as this is a display device. The "truth" here is whether the display's physical and photometric characteristics meet predetermined engineering and regulatory standards (e.g., DICOM GSDF conformance) and are equivalent to the predicate. This is typically verified by specialized testing equipment and procedures.

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

   • Not applicable. Adjudication is relevant for expert agreement on medical findings, not for objective physical measurements of a display.

4. 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 was not done. This is a submission for a medical display, not an AI device. The document explicitly states: "No animal testing or clinical testing has been performed."

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

   • No, this was not done. This is a display device, not an algorithm.

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

   • Not applicable in the medical context. The "ground truth" for this device's performance are the established technical specifications and performance characteristics outlined in industry standards (like DICOM) and the FDA guidance for display devices. The comparison is made against the performance of a legally marketed predicate device.

7. The sample size for the training set:

   • Not applicable. This device does not use a "training set" as it is a hardware display, not an AI algorithm.

8. How the ground truth for the training set was established:

   • Not applicable. (See point 7).

Summary of the Study:

The study proving the device meets the acceptance criteria is a series of bench tests conducted on the Nio Color 8MP (MDNC-8132) display. These tests were performed according to the "Physical Laboratory Testing instructions in 'Guidance for Industry and FDA Staff: Display Devices for Diagnostic Radiology'" (2022). The purpose was to demonstrate that the MDNC-8132 possesses "similar characteristics" and is "equivalent" in performance to the predicate device, the Nio Color 2MP (MDNC-2521), across various critical display parameters such as spatial resolution, luminance, uniformity, reflection, and color tracking. The document concludes that these tests "did not reveal new issues of safety and performance" and confirmed its substantial equivalence to the predicate. Additionally, compliance with EMC and Safety standards, and passing environmental tests, contributed to the conclusion of substantial equivalence. No clinical or animal testing was performed, as the
demonstration of equivalence was based solely on technical and performance bench testing.

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The display is intended to be used for displaying and viewing digital images (excluding digital mammography) for review and analysis by trained medical practitioners.
The display is intended to be used for displaying and viewing digital images (excluding digital mammography) for review and analysis by trained medical practitioners. The display may be used in dental applications.

Both the Nio Color 2MP (MDNC-2521) and the Nio Color 3MP (MDNC-3521) are medical computer displays designed for general radiology imaging applications.
The MDNC-3521 model can also be used in dental applications.
The devices can also be used for home reading in radiology.

The MDNC-2521 is a derivative of the MDNC-2221.
The MDNC-3521 is a derivative of the MDNC-3421.

The modified displays are effectively identical to the respective predicate devices except for the following changes:
Updated LCD panel with same resolution and dimensions compared to the respective predicate devices New housing, display stand and internal mechanics, with similar functionality and design principle compared to the respective predicate devices Updated internal electronics boards, with similar functionality and design principle compared to the respective predicate devices Updated firmware, with similar functionality and design principle compared to the respective predicate devices New packaging, with similar functionality and design principle compared to the respective predicate devices or any other Barco diagnostic display Rephrasing of the intended usage environment, because of the evolution towards more home reading in radiology Small update in the intended user description The modified device has the following similarities compared to the unmodified device: The same intended use The same operating principle The same fundamental technology

The displays can be used optionally with QAWeb Enterprise software, listed under D332294 as a class 1 device with product code LHO. QAWeb Enterprise is a calibration software that is intended as a quality assurance software for the displays. QAWeb Enterprise software helps to keep the display DICOM compliant.

The display can be used optionally with Intuitive Workflow Tools, cleared in K191845 as a class 2 device with product code PGY. The Intuitive Workflow Tools are accessories for image enhancement on diagnostic displays: SpotView: The Barco SpotView display feature allows focusing on a region of interest in an image by boosting the display's backlight such that the maximum luminance in provided inside the region of interest. SpotView also enables focused observation during reading by dimming images outside the region of interest and increasing the contrast in this region. Also magnification and inversion of pixels are possible with SpotView. AAM - Application Appearance Manager: This workflow tool allows you to set the luminance as well as the color space for each application that is on the workstation. There are often multiple windows open on a screen, but not all of them need the high brightness of the diagnostic applications.

The provided text describes the acceptance criteria and a study demonstrating that the Barco Nio Color 2MP (MDNC-2521) and Nio Color 3MP (MDNC-3521) displays meet these criteria for substantial equivalence to their predicate devices. The study focuses on physical performance bench tests rather than clinical efficacy with AI or human readers.

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

1. Table of Acceptance Criteria and Reported Device Performance

The acceptance criteria are implied by the comparison to predicate devices and the performance bench tests. The Predicate Device columns represent the established performance, which the new devices aim to be substantially equivalent to. The Device for which listing is sought columns show the reported performance of the new devices.

Acceptance Criteria (Implied by Predicate Performance) and Reported Device Performance

Note: "Similar characteristics" is the conclusion of the bench tests as stated in the document.
[] The intrinsic response time of the new LCD panel on the modified device is further improved with Barco's RapidFrame technology, a proprietary medical overdrive algorithm which improves the temporal response of the system.*

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

The study described is a series of physical performance bench tests conducted on the device itself and in comparison to predicate devices, rather than a study involving medical images or patient data.

• Sample Size for Test Set: Not applicable in the context of clinical images/data. The tests were performed on the modified devices (MDNC-2521 and MDNC-3521).
• Data Provenance: Not applicable. The "data" here refers to the physical performance measurements of the display hardware.

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

• Number of Experts: Not applicable. The ground truth for display performance parameters (e.g., luminance, resolution) is established by metrology standards and measurement equipment, not by human expert opinion in this context.
• Qualifications of Experts: Not applicable.

4. Adjudication Method for the Test Set

• Adjudication Method: Not applicable. Physical performance measurements do not inherently require an adjudication method.

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

• MRMC Study: No, an MRMC comparative effectiveness study was not done. The submission focuses on device characteristics and substantial equivalence to existing predicate devices based on physical performance.
• Effect Size: Not applicable as no MRMC study was conducted.

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

• Standalone Performance: Not applicable. The device is a medical computer display, and its performance is assessed based on its physical properties and adherence to display standards, not as an AI algorithm. While it can be optionally used with software like QAWeb Enterprise for calibration and Intuitive Workflow Tools for image enhancement, the submission focuses on the display hardware itself.

7. The Type of Ground Truth Used

The ground truth for this device's evaluation is based on physical measurement standards and the performance specifications of the predicate devices. The "Guidance for Industry and FDA Staff: Display Devices for Diagnostic Radiology" (issued in 2022) serves as the reference for the physical laboratory testing instructions, which define the expected performance characteristics.

8. The Sample Size for the Training Set

• Sample Size for Training Set: Not applicable. This is a hardware device (display) and not an AI algorithm that requires a training set of data.

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

• Ground Truth for Training Set: Not applicable. As this is not an AI algorithm, there is no training set or ground truth established for training purposes.

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The Sterile Level 3 Surgical Gown is intended to be worn by operating room personnel during surgical procedure to protect both the surgical patient and the operating room personnel from transfer of microorganisms, body fluids, and particulate material. Per ANSI/AAMI PB70:2012 Liquid barrier performance and classification of protective apparel and drapes intended for use in health care facilities, the surgical gowns met the requirements for Level 3 classification.

The Sterile Level 3 Surgical Gown is a Class II medical device under the FDA product code of FYA, General & Plastic Surgery Panel, and Regulation 21 CFR 878.4040. The device description of the Sterile Level 3 Surgical Gown is in accordance with the Guidance on Premarket Notification [510(k)] Submissions for Surgical Gowns and Surgical Drapes, issued on August 1, 1993 and Guidance for Industry and FDA Staff: Premarket Notification Requirements Concerning Gowns Intended for Use in Healthcare Settings, issued on December 9, 2015.

The Sterile Level 3 Surgical Gown is intended to be worn by operating room personnel during surgical procedures to protect both the surgical patient and the operating room personnel from transfer of microorganisms, body fluids, and particulate material. The proposed devices are single use and EtO sterilized. The Sterile Level 3 Surgical Gowns are available in six sizes, including S, M, L, XXL and XXXL. The belt ties are integrated into the body. Per ANSI/AAMI PB70:2012 Liquid barrier performance and classification of protective apparel and drapes intended for use in health care facilities, the proposed devices meet the requirements for Level 3 classification. The Sterile Level 3 Surgical Gown is provided as blue color. The Sterile Level 3 Surgical Gown is made of 50 Gsm PP (SMMS nonwoven polypropylene).

The provided text describes the acceptance criteria and study results for a Sterile Level 3 Surgical Gown. This is not an AI/ML powered medical device, but rather a textile product. As such, the requested information pertaining to AI/ML device studies (such as sample size for test/training sets, data provenance, expert adjudication, MRMC studies, standalone algorithm performance, or ground truth establishment for AI/ML) is not applicable or present in this engineering report.

The report details non-clinical performance testing for the physical properties and barrier performance of the surgical gown.

Here's a breakdown of the requested information based on the provided document for this medical device (a surgical gown):

1. Table of Acceptance Criteria and Reported Device Performance

Regarding AI/ML specific information (not applicable to this product):

2. Sample sizes used for the test set and the data provenance: Not applicable. This is a physical product (surgical gown), not an AI/ML device. Tests were conducted on material samples of the gown. No information on the number of gowns or samples tested is provided beyond the aggregated results. The data provenance is implied to be from the manufacturer's testing, without specific country of origin mentioned for the raw test data. The tests are prospective in nature, as they are part of the premarket submission for a new device.
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts: Not applicable. Ground truth for physical properties is established by standardized testing according to specified ASTM/ISO norms, not by expert consensus.
4. Adjudication method (e.g. 2+1, 3+1, none) for the test set: Not applicable. This refers to consensus among human readers for AI/ML device studies.
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 is a physical product, not an AI/ML system assisting human readers. The document explicitly states "Clinical Performance Testing is not applicable to the subject device."
6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done: Not applicable. This is a physical product, not an algorithm.
7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.): The "ground truth" for this device's performance is objective measurement based on established ASTM and ISO standards for material properties, fluid barrier performance, flammability, biocompatibility, and packaging integrity.
8. The sample size for the training set: Not applicable. This is not an AI/ML device requiring a training set.
9. How the ground truth for the training set was established: Not applicable.

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The Barco Digital Dermatoscope is a non-invasive skin imaging system, which acquires multispectral and white light dermoscopic images and clinical photographs of the skin. These can be stored, retrieved, displayed and reviewed by trained medical practitioners.

The Barco Demetra BDEM-01 is designed to capture images of the skin and optimize the imaging and documentation workflow. The “Barco Demetra BDEM-01” consists of a hardware device and a software application. The hardware device is a portable, battery powered medical device for acquiring and visualizing images of the skin. The device acquires multispectral optical dermoscopic images in a contact mode (device in contact with the skin). In addition, the device can also acquire a clinical close-up image, when it is held at up to 10 to 15 cm from the skin, and a clinical overview image when it is held at a distance of approximately 35-55 cm from the skin. The stand-alone software application is cloud software with a related web application. The cloud software can generate reports containing white light dermoscopic images and clinical photographs of the skin to be reviewed by trained medical practitioners.

The provided document describes a 510(k) premarket notification for the Demetra Dermatoscope BDEM-01 (K213957), which is a "Special 510(k)" submission. This typically means the device has minor changes from a previously cleared predicate device (K192829 in this case).

Crucially, the document does NOT contain information about a clinical study or performance testing that would typically establish acceptance criteria for an AI/ML medical device and prove its performance against those criteria.

Instead, this submission focuses on demonstrating substantial equivalence to a predicate device, specifically highlighting:

• Identical Indications for Use: The current device has the same intended use as its predicate.
• Minor modifications: The changes are related to the Multispectral Image Acquisition Workflow, micro-chip, supplier of camera, data acquisition/handling/display, and inclusion of an Analytics Toolkit (which itself was cleared via a separate 510(k), K201408).
• Bench testing: The document states "bench tests mentioned below were performed to validate the device characteristics that differ from the predicate device in the original 510(k) K192829." These tests are listed as "Functionality Tested" with a "PASS" criterion.

Therefore, I cannot extract the requested information regarding acceptance criteria, study details, sample sizes, expert involvement, ground truth, or MRMC studies, as the provided text pertains to a special 510(k) for a hardware and fixed software device based on substantial equivalence and bench testing, not an AI/ML algorithm requiring a comprehensive performance study as typically seen for novel AI claims.

If this were an AI/ML device with performance claims, such information would be mandatory. Since it is not present, it implies that the device (or at least this specific submission) is not an AI/ML algorithm making clinical claims that require such validation. The "Analytics Toolkit" mentioned is noted as "display of skin parameters map only, cleared via K201408," suggesting its AI component (if any) was addressed in a prior submission or is limited to visualization rather than diagnostic or prognostic output.

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The Barco MDPC-8127 device is intended for in vitro diagnostic use to display digital images of histopathology slides acquired from IVD-labeled whole-slide imaging scanners and viewed digital pathology image viewing software that have been validated for use with this device. It is an aid to the pathologist to review and interpret digital images of histopathology slides for primary diagnosis. It is the responsibility of the pathologist to employ appropriate procedures and safeguards to assure the validity of the interpretation of images using the MDPC-8127. The display is not intended for use with digital images from frozen section, cytology, or non-formalin-fixed, paraffin embedded (non-FFPE) hematopathology specimens.

The Barco MDPC-8127 is a medical, color-calibrated display, specifically intended for review and interpretation of surgical pathology slides from IVD-labeled whole-slide imaging scanners and digital pathology image viewing software that have been validated for use with the display.

The use of the MDPC-8127 display with scanners and viewing software is determined by using verified test methods to establish the display's capability to meet or exceed the performance specifications and the intended color space specified by the IVD-labeled whole-slide imaging scanners and digital pathology image viewing software.

The display uses built-in techniques and technology to ensure constant accuracy over time.

The MDPC-8127 consists of an 8 mega-pixel, 27-inch color LCD-panel with internal electronics platform. It is calibrated to defined color spaces for whole-slide imaging and is compatible with digital pathology viewinq software that utilizes defined color spaces to ensure images are displayed in intended colors.

The provided text describes the Barco MDPC-8127, a medical display intended for in vitro diagnostic use to display digital images of histopathology slides. The document details the device's technical specifications and performance testing to demonstrate substantial equivalence to a predicate device, the MMPC-4227F1 (PP27QHD).

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

Key Takeaway from the Document: The study primarily focuses on non-clinical bench testing to demonstrate the MDPC-8127's technical characteristics and performance are equivalent to or better than a predicate device. It is not a clinical study involving human readers and a comparative effectiveness assessment with AI assistance. The display itself is a tool for pathologists, not an AI algorithm.

1. Table of Acceptance Criteria and Reported Device Performance

The document presents a table comparing the performance of the MDPC-8127 (proposed device) with its predicate device (MMPC-4227F1). While not explicitly labeled as "acceptance criteria," these are the parameters against which the device's performance was evaluated for substantial equivalence. The "Results" column for MDPC-8127 represents the reported device performance.

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The Barco Demetra Analytics Toolkit is a non-invasive skin analysis system. The Barco Demetra Skin Parameter Maps Tool provides maps that show the relative location of blood and pigment. The Barco Demetra Skin Parameter Maps Tool is intended only to complement dermoscopy.

The Barco Demetra Analytics Toolkit is a software application used to support analysis of dermoscopic images captured with the Barco Demetra BDEM-01 dermatoscope (K192829). The system is intended for use by medical practitioners. Scatter Contrast Maps provide additional information to dermoscopy by highlighting surface contours. The Skin Parameter Maps Tool provides images generated from multispectral image sets and aids the user in visualizing blood and pigment patterns in the skin. The output of the Skin Parameter Maps is shown to the user as grayscale two-dimensional maps. The maps are intended only to complement dermoscopy.

The provided text describes several clinical validation activities for the Demetra Analytics Toolkit, but it does not explicitly state specific acceptance criteria in a quantitative manner (e.g., minimum sensitivity, specificity, or agreement score). Instead, it describes general performance testing and multiple clinical studies aiming to support the device's safety and effectiveness and its substantial equivalence to a predicate device.

Given this limitation, I will infer the "acceptance criteria" from the descriptions of the studies and their reported "PASS" results for software verification, usability engineering, and design validation. The implicit acceptance criteria appear to be that the device "performs as well as the legally marketed predicate device" and that the evaluations did not "reveal new issues of safety and performance."

Here's an attempt to structure the information based on your request, acknowledging the gaps in explicit quantitative acceptance criteria:

Acceptance Criteria and Device Performance

The provided document does not explicitly define quantitative acceptance criteria for the Demetra Analytics Toolkit's clinical performance (e.g., specific accuracy, sensitivity, or agreement thresholds). Instead, the studies aim to demonstrate that the device is "as safe, as effective, and performs as well as the legally marketed predicate device." The "Result" column in the table below reflects the general "PASS" status for foundational tests and the overall conclusion of substantial equivalence.

Table of Acceptance Criteria (Implicit) and Reported Device Performance

Study That Proves the Device Meets the Acceptance Criteria:

The document describes five distinct clinical validation activities. It's important to note that these studies collectively support the device's substantial equivalence to the predicate device, rather than each proving specific quantitative "acceptance criteria" for clinical performance.

2. Sample Sizes and Data Provenance:

• "Clinical validation of Demetra Skin Parameter Maps" (Retrospective Reader Study):
  • Test Set Sample Size: 28 cases
  • Data Provenance: Retrospective, mixed dermatology conditions, focus on skin lesions suspicious for skin cancer, no country explicitly stated but experts were from USA.
• "SIAScans vs. Barco Analytics Toolkit" (Retrospective Study):
  • Test Set Sample Size: "representative set of specific types of cases" (exact number not specified)
  • Data Provenance: Retrospective, from OHSU Dermatology Clinic, Portland (USA).
• "Correlation of structures visualized in the Skin Parameter Maps with pathology findings" (Prospective Clinical Study):
  • Test Set Sample Size: 15 cases
  • Data Provenance: Prospective, from Washington DC (USA) and Maryland (USA).
• "Validation of skin structures imaged in the Skin Parameter Maps" (Clinical Study):
  • Test Set Sample Size: 15 cases
  • Data Provenance: From University Hospital Leuven in Belgium.
• Clinical Study at Charité Universitätsmedizin Berlin & Oregon, USA (Retrospective Reader Study):
  • Test Set Sample Size: 28 representative cases
  • Data Provenance: Retrospective, from existing database, conducted in Germany and Oregon, USA.

3. Number of Experts and Qualifications:

• "Clinical validation of Demetra Skin Parameter Maps":
  • Number of Experts: 4
  • Qualifications: Board certified dermatologists from different sites in the USA.
• "SIAScans vs. Barco Analytics Toolkit":
  • Number of Experts: 1
  • Qualifications: Board certified dermatologist from OHSU Dermatology Clinic, Portland.
• "Correlation of structures visualized in the Skin Parameter Maps with pathology findings":
  • Number of Experts: 2
  • Qualifications: Board certified dermatologist from Washington DC; and a dermatopathologist from Maryland.
• "Validation of skin structures imaged in the Skin Parameter Maps":
  • Number of Experts: 1
  • Qualifications: Dermatologist from University Hospital Leuven.
• Clinical Study at Charité Universitätsmedizin Berlin & Oregon, USA:
  • Number of Experts: 4 observers (qualifications not explicitly stated beyond "observers," but context implies medical professionals, likely dermatologists).

4. Adjudication Method for the Test Set:

The document mentions "statistical analysis was performed on the collected ratings" for several studies, but it does not specify an adjudication method (e.g., 2+1, 3+1) for cases where expert opinions might differ in reader studies. For the studies comparing maps to pathology or physical structures, it implies direct comparison or subjective rating/qualitative analysis rather than expert consensus on a ground truth prior to analysis.

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

• Was an MRMC study done? Yes, several of the described studies, particularly the "retrospective reader study" involving multiple board-certified dermatologists, appear to be MRMC in nature, as they involve multiple readers evaluating multiple cases. However, the studies are framed as "validation activities" and comparisons for substantial equivalence, rather than a direct MRMC comparative effectiveness study aiming to quantify human reader improvement with AI vs. without AI assistance.
• Effect Size of Human Readers Improving with AI vs. Without AI Assistance: The document does not report any effect size for how much human readers improve with AI assistance vs. without AI assistance. The described studies focus on validating the skin parameter maps themselves and comparing them to existing methods/predicate devices. The device is intended "only to complement dermoscopy," suggesting its role as an adjunct.

6. Standalone (Algorithm Only) Performance:

The document does not describe a standalone (algorithm only without human-in-the-loop performance) study for the Demetra Analytics Toolkit. The device provides "maps that show the relative location of blood and pigment" which are "intended only to complement dermoscopy." Its function is described as aiding the user in visualizing patterns, indicating it's an assistive tool for a human reader rather than a diagnostic algorithm operating in isolation.

7. Type of Ground Truth Used:

The ground truth for the test sets varied across the studies:

• Expert Consensus/Subjective Rating: For several reader studies ("Clinical validation of Demetra Skin Parameter Maps," "Validation of skin structures imaged in the Skin Parameter Maps," Clinical Study at Charité/Oregon), the "ground truth" for evaluation appears to be the subjective ratings and qualitative feedback directly from the participating dermatologists/observers on the utility and consistency of the maps.
• Pathology Findings: For the "Correlation of structures visualized in the Skin Parameter Maps with pathology findings" study, the ground truth was pathology H&E images.
• Predicate Device Performance: For "SIAScans vs. Barco Analytics Toolkit," the performance of the predicate device (Siascope) served as a comparative reference.
• Physical Structures/Clinical Use: For "Validation of skin structures imaged in the Skin Parameter Maps," the ground truth was the expected clinical utility and visualization of skin structures by a dermatologist. For "Clinical validation of Demetra Skin Parameter Maps," the cases represented "various dermatology conditions for which a dermatologist can use a dermatoscope."

8. Sample Size for the Training Set:

The document does not provide any information regarding the sample size used for the training set of the Demetra Analytics Toolkit's algorithms.

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

The document does not provide any information on how the ground truth for the training set was established. Given the focus on clinical validation for substantial equivalence and the absence of standalone AI performance claims, details about the model's training might not have been explicitly required or provided in this summary.

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The display is intended to be used in displaying digital images, including standard and multiframe digital mammography, for review, analysis, and diagnosis by trained medical practitioners. It is specially designed for breast tomosynthesis applications.

The MDNC-12130 (Nio Fusion 12MP) is a medical computer display designed for both PACS and breast imaging applications. It consists of a 30.9" 12 mega-pixel (4200x2800 resolution) LCD panel with integrated LED backlight. The panel is integrated into the display housing body with an internal mechanics chassis structure, that also integrates the electronics, sensors and power supply. The display module is supported by a display stand. Internal sensors and controllers measure, control and stabilize the device. SoftGlow Lights are integrated as an ancillary function for user comfort and can be lit to illuminate the desk and to create some ambient lighting in the reading room.

This document is a 510(k) Premarket Notification from Barco N.V. to the FDA for their Nio Fusion 12MP (MDNC-12130) display. The document focuses on demonstrating substantial equivalence to a predicate device, the Nio Color 5.8MP (MDNC-6121).

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

Acceptance Criteria and Reported Device Performance

The acceptance criteria for this device are fundamentally based on demonstrating substantial equivalence to a legally marketed predicate device (Nio Color 5.8MP) by showing that the differences in technological characteristics do not raise new questions of safety or effectiveness. The performance testing conducted is to confirm that the new device maintains similar or better performance characteristics compared to the predicate, especially given its specific indication for breast tomosynthesis.

Since this is a medical display, the "performance" is largely about its optical and technical specifications rather than diagnostic accuracy.

Study Proving Device Meets Acceptance Criteria:

The study conducted is a bench testing comparison against the predicate device, following the "Guidance for Industry and FDA Staff: Display Devices for Diagnostic Radiology", issued on October 2, 2017.

1. Sample Size Used for the Test Set and Data Provenance:

   • Sample Size: The document does not specify a quantitative "sample size" in terms of number of devices tested. It refers to "the modified device MDNC-12130" indicating at least one physical unit was tested. For display devices, typically a representative sample or a single unit is tested for performance specifications.
   • Data Provenance: The testing was done internally by Barco N.V. (Belgium-based company). The data provenance is physical bench testing of the device itself.
   • Retrospective or Prospective: This is a prospective test in the sense that a physical device was manufactured and its performance measured against established standards and the predicate device.

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

   • Not applicable in this context. The "ground truth" for a display device is its measured physical characteristics (e.g., luminance, resolution, contrast). These are objective measurements performed by specialized equipment, not by human experts creating a ground truth for diagnostic accuracy. The acceptance criteria (e.g., DICOM GSDF conformance) are established standards, not derived from expert consensus on a test set.

3. Adjudication Method for the Test Set:

   • Not applicable. As noted above, the performance metrics of a display are objectively measured.

4. If a Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study was done:

   • No, an MRMC study was NOT done. The document explicitly states: "No animal testing or clinical testing has been performed." An MRMC study involves human readers interpreting medical images with and without the device/AI assistance; this device is a display, not a diagnostic algorithm. Its function is to accurately render images, which is assessed via technical performance, not diagnostic accuracy by human readers.

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

   • Yes, a standalone performance assessment was done. The entire "Performance testing" section (point 9 in the 510(k) Summary) describes the engineering bench tests performed on the MDNC-12130 as a standalone device to measure its optical and technical characteristics against the predicate and industry standards.

6. The Type of Ground Truth Used:

   • The "ground truth" here is based on objective physical measurements and engineering specifications, benchmarked against industry standards (e.g., DICOM GSDF conformance, and the specific metrics outlined in the FDA's "Guidance for Industry and FDA Staff: Display Devices for Diagnostic Radiology"). The predicate device's performance also serves as a comparative "ground" for equivalence.

7. The Sample Size for the Training Set:

   • Not applicable. This is a hardware display device, not an AI/machine learning algorithm that requires a "training set" of data.

8. How the Ground Truth for the Training Set was Established:

   • Not applicable, as no training set was used.

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The Barco Digital Dermatoscope is a non-invasive skin imaging system, which acquires multispectral and white light dermoscopic images and clinical photographs of the skin. These can be stored, retrieved, displayed and reviewed by trained medical practitioners.

The Barco Demetra BDEM-01 is designed to capture images of the skin and optimize the imaging and documentation workflow. The “Barco Demetra BDEM-01” consists of a hardware device and a software application. The hardware device is a portable, battery powered medical device for acquiring and visualizing images of the skin. The device acquires multispectral optical dermoscopic images in a contact mode (device in contact with the skin). In addition, the device can also acquire a clinical close-up image, when it is held at up to 10 to 15 cm from the skin, and a clinical overview image when it is held at a distance of approximately 35-55 cm from the skin. The stand-alone software application is cloud software with a related web application. The cloud software can also generate one or more reports and feedback to the device.

The provided text is a Premarket Notification (510(k)) summary for the Demetra BDEM-01 device, a non-invasive skin imaging system. It focuses on demonstrating substantial equivalence to a predicate device (Microderm - K040171) rather than presenting a performance study against specific acceptance criteria for an AI/algorithm-driven device.

Therefore, this document does not contain the information required to describe acceptance criteria and a study that proves a device meets them in the context of an AI-driven medical device, as it describes a non-algorithm-driven imaging system.

Here's why and what information is missing:

• No AI/Algorithm: The device description clearly states "The Barco Demetra BDEM-01 is designed to capture images of the skin and optimize the imaging and documentation workflow. The 'Barco Demetra BDEM-01' consists of a hardware device and a software application." There is no mention of any artificial intelligence, machine learning, or algorithm that processes these images beyond storage, retrieval, display, and review by trained medical practitioners. The "multispectral images" are acquired, but there's no indication of AI interpretation of these multispectral data for diagnosis or risk assessment.
• No Diagnostic Aid Claim (for AI): The Indications for Use state "These can be stored, retrieved, displayed and reviewed by trained medical practitioners." This implies a tool for human review, not an autonomous diagnostic or assistive AI.
• Performance Testing Focus: The "Performance testing" section (9) focuses on bench tests validating differences from the predicate device related to hardware modifications (Multispectral LEDs illumination, hand-held design, biocompatibility) and basic image acquisition review, not on the performance of a diagnostic algorithm. The criteria listed are simply "PASS," indicating compliance with safety and engineering standards.

To answer your request, if this were an AI-driven device, the following information would typically be found in a 510(k) summary or an associated clinical study report for an AI/ML product:

1. A table of acceptance criteria and the reported device performance: This would list metrics like sensitivity, specificity, AUC, F1-score, accuracy, etc., along with the target values and the achieved values from the study.
2. Sample sizes used for the test set and the data provenance: Details on the number of cases/images used for testing, where the data came from (e.g., specific hospitals, geographical regions), and whether it was retrospectively collected or prospectively gathered.
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts: Information on the number of clinicians (e.g., dermatologists, radiologists) who provided the reference standard for the test data, and their relevant experience and certifications.
4. Adjudication method for the test set: How disagreements among the experts establishing ground truth were resolved (e.g., majority vote, third expert review, consensus meeting).
5. If a multi-reader multi-case (MRMC) comparative effectiveness study was done: For AI-assisted reading, this study would compare human performance with and without AI assistance, quantifying the improvement (effect size) in diagnostic accuracy, efficiency, or other relevant metrics.
6. If a standalone (i.e., algorithm only without human-in-the-loop performance) was done: Often, an initial study evaluates the AI's performance independently against the ground truth.
7. The type of ground truth used: Whether ground truth was established by expert consensus, biopsy/pathology results, long-term patient outcomes, or a combination.
8. The sample size for the training set: The number of cases/images used to train the AI model.
9. How the ground truth for the training set was established: Similar to the test set, but for the data used during model development.

Because the provided document pertains to a primary imaging device, and not an AI/ML diagnostic aid, it lacks the specific study design elements and performance metrics that would be associated with an AI product's acceptance criteria.

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