Search Results

This medical monitor is intended to provide color video displays and images from medical equipment which include laparoscopy and endoscopy systems for surgery and various medical imaging systems.

32HS710S LCD monitor is intended to provide color video displays of images from surgical endoscope, laparoscopic camera system and other compatible medical imaging systems. This Medical Monitor has multiple video interface ports such as Display port, HDMI, DVI and SDI. This monitor displays color video and images from medical equipment which include various medical imaging systems. This monitor can be covered over 100% of the sRGB spectrum.

The provided FDA 510(k) clearance letter and summary for the Medical Monitor (32HS710S) do not contain information about acceptance criteria or specific studies that prove the device meets such criteria in terms of clinical performance or diagnostic accuracy.

The document primarily focuses on demonstrating substantial equivalence to a predicate device (K241402) by confirming:

• Same Indications for Use: The new device is intended for the same purpose as the predicate.
• Technological Comparison: Differences in specifications (resolution, connectors, power, physical size) are acknowledged but deemed not to affect safety and effectiveness.
• Non-Clinical Bench Tests: Compliance with electrical safety and electromagnetic compatibility standards (IEC60601-1 Ed 3.2, IEC60601-1-2 Ed 4.1, IEC TR 60601-4-2) is confirmed.
• Software Validation: The software belongs to "Basic Documentation Level" and was developed and verified/validated according to IEC 62304 and FDA guidance for device software functions.

Therefore, I cannot extract the requested information (points 1-9) as it pertains to clinical performance, device accuracy, or studies with human readers and ground truth data from this document. This type of information is typically required for devices that perform a diagnostic or analytical function where accuracy metrics like sensitivity, specificity, or reader agreement are critical.

The Medical Monitor (32HS710S) is a display device. Its "performance" in this context refers to its ability to accurately and safely display images from other medical equipment, not its ability to interpret or diagnose medical conditions. For such a device, the acceptance criteria would focus on technical display characteristics (e.g., color accuracy, resolution, brightness, refresh rate) and safety standards, rather than diagnostic accuracy metrics.

Here's what can be inferred or stated based on the provided document:

1. Table of Acceptance Criteria and Reported Device Performance

Based on the provided document, specific quantitative acceptance criteria related to diagnostic accuracy or clinical performance (e.g., sensitivity, specificity, AUC) and corresponding reported device performance values are NOT available.

The document refers to compliance with electrical safety and electromagnetic compatibility standards and software validation standards. These are the "acceptance criteria" for this type of device, which is primarily a display unit.

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

Not applicable / Not provided. The tests described are bench tests for electrical safety and EMC, and software validation. These do not typically involve "test sets" of patient data in the way a diagnostic AI algorithm would.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts (e.g. radiologist with 10 years of experience)

Not applicable / Not provided. Ground truth is not established in this context as the device is a monitor, not an interpretive diagnostic tool.

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

Not applicable / Not provided.

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

No. This type of study is not relevant for a medical display monitor.

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

No. The device is a display monitor; it does not perform an algorithm-only function that would yield diagnostic output without human interpretation.

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

Not applicable / Not provided. The concept of "ground truth" for diagnostic accuracy is not relevant for a display monitor. The "ground truth" for this device would be its ability to accurately display the incoming video signal according to technical specifications.

8. The sample size for the training set

Not applicable / Not provided. This information is relevant for AI/ML algorithms that are trained on data, which is not the case for this medical monitor.

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

Not applicable / Not provided.

Ask a specific question about this device

This medical monitor is intended to provide color video displays and images from medical equipment which include laparoscopy and endoscopy systems for surgery and various medical imaging systems.

27HS714S LCD monitor is intended to provide color video displays of images from surgical endoscope, laparoscopic camera system and other compatible medical imaging systems. This Medical Monitor has multiple video interface ports such as Display port, HDM, DVI and SDI. This monitor displays color video and images from medical equipment which include various medical imaging systems. This monitor can be covered over 99% of the sRGB spectrum.

This document, K243439, is a 510(k) premarket notification for a medical monitor (27HS714S). The information provided focuses on demonstrating substantial equivalence to a predicate device (K241402) rather than presenting a study proving the device meets specific acceptance criteria in the context of AI/ML performance.

Therefore, many of the requested elements regarding acceptance criteria for AI/ML performance, ground truth establishment, expert adjudication, and MRMC studies are not applicable or cannot be extracted from this document, as the device is a monitor, not an AI/ML-driven diagnostic tool.

However, I can extract information related to the device's functional performance and safety validations:

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

The document does not specify quantitative acceptance criteria for performance in the sense of diagnostic accuracy or output quality against a ground truth, as it is a display monitor. Instead, the acceptance criteria are implied by compliance with established medical device standards for electrical safety, electromagnetic compatibility, and software validation. The "reported device performance" is essentially that it met these standard requirements.

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

• Sample Size for Test Set: Not applicable. The document describes bench tests for electrical safety, EMC, and software validation, not a test set of clinical cases or data for AI/ML performance.
• Data Provenance: Not applicable for AI/ML data. The tests are laboratory/bench-based.

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

Not applicable. Ground truth as typically defined for AI/ML (e.g., disease presence/absence determined by experts) is not relevant for this device's validation. Validation was against technical standards.

4. Adjudication method for the test set

Not applicable. There was no expert adjudication of clinical cases.

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 medical monitor, not an AI-assisted diagnostic tool. No MRMC study was conducted or is relevant for its performance validation.

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

Not applicable. This is not an algorithm-only device. Its "performance" relates to its ability to display images accurately and safely, not to independently analyze data.

7. The type of ground truth used

The "ground truth" for this device's validation were the requirements of the specified international standards (IEC 60601-1, IEC 60601-1-2, IEC TR 60601-4-2, IEC 62304) and FDA guidance documents. Compliance with these standards served as the benchmark for acceptance.

8. The sample size for the training set

Not applicable. This is not an AI/ML device that requires a training set of data.

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

Not applicable.

Summary of the Device's Validation Study (as described in the document):

The study to prove the device (Medical Monitor 27HS714S) meets acceptance criteria primarily consists of bench tests and software validation.

• Purpose: To demonstrate the device's compliance with electrical safety, electromagnetic compatibility, and software development standards, and thus its substantial equivalence to the predicate device.
• Methodology:
  • Electrical Safety & EMC: Bench tests were conducted against harmonized standards: IEC60601-1 Edition 3.2 2020-08, IEC60601-1-2 Edition 4.1 2020-09, and IEC TR 60601-4-2 Edition 1.0 2016-05. These tests verify the device's safe operation and its ability to function correctly in its electromagnetic environment.
  • Software Validation: The software was designed, developed, verified, and validated according to IEC 62304, a standard for medical device software life cycle processes. Information was provided in accordance with FDA guidance "The content of premarket submissions for Device Software Functions (June 14, 2023)."
• Results: The tests demonstrated that the proposed device complies with all specified standards.
• Clinical Studies: None were considered necessary or conducted, as the device's function as a display monitor does not typically require clinical performance studies for safety and effectiveness demonstration in a 510(k) context, especially when demonstrating substantial equivalence. The document explicitly states: "No clinical studies were considered necessary, and therefore, none were conducted."

Ask a specific question about this device

This medical monitor is intended to provide color video displays and images from medical equipment which include laparoscopy and endoscopy systems for surgery and various medical imaging systems.

32HR734S LCD monitor is intended to provide color video displays of images from surgical endoscope, laparoscopic camera system and other compatible medical imaging systems. This Medical Monitor has multiple video interface ports such as DP, HDMI, DVI, SDI input. This monitor displays color video and images from medical equipment which include various medical imaging systems. This monitor can be covered over 100% of the sRGB spectrum.

This document is a 510(k) premarket notification for a medical monitor (32HR734S). It states that no clinical studies were considered necessary and therefore none were conducted. The substantial equivalence determination is based on non-clinical tests including electrical safety and electromagnetic compatibility (bench tests against IEC 60601-1, IEC 60601-1-2, IEC TR 60601-4-2 standards) and software validation (developed according to a software development process and verified/validated per IEC 62304).

Given the information provided, it is not possible to fill out the table and answer all questions related to acceptance criteria, device performance, study details, and ground truth as no clinical studies were performed. The document primarily focuses on demonstrating substantial equivalence through technical and regulatory compliance rather than clinical performance metrics.

However, I can extract the information regarding the non-clinical tests that were performed.

Table of Acceptance Criteria and Reported Device Performance (Non-Clinical)

Study Information (Based on provided non-clinical data):

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

   • Test set sample size: Not applicable/not specified for clinical performance. For bench testing, it refers to the physical device unit(s) tested.
   • Data provenance: Bench tests and software validation were conducted to verify compliance with international standards. Specific country of origin for test data is not provided, but the applicant is LG Electronics Inc. from Korea, South. The testing is non-clinical.

2. 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 no clinical ground truth was established for this device based on the provided document. The "ground truth" for non-clinical testing refers to compliance with established technical standards.

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

   • Not applicable as no clinical adjudication was performed. Compliance with technical standards is determined through validated test procedures and results.

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 MRMC or comparative effectiveness study was performed. The device is a medical monitor, not an AI-assisted diagnostic tool.

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

   • Not applicable. The device is a display monitor, not an algorithm.

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

   • Not applicable for clinical performance. The "ground truth" for the non-clinical tests was adherence to and compliance with international standards for electrical safety, EMC, and software validation.

7. The sample size for the training set:

   • Not applicable as no machine learning algorithm was trained for this device. The software validation refers to the development and testing of the monitor's embedded software functionality.

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

   • Not applicable as no machine learning algorithm was trained.

Ask a specific question about this device

Software used in a device that saves, enlarges, reduces, views as well as analyzes, transfers and prints medical images. (excluding fluoroscopic, angiographic, and mammographic applications.)

LG Acquisition Workstation Software ASHK100G is a diagnostic software for final postprocessed X-ray images of body parts of actual patients acquired through the integration of digital X-ray detectors (DXD+ASHK100G; refer to below list for the compatible LG DXD series) and X-ray generators. By integrating the [MWL] and the [PACS] server, this software can be used to check the information and images of the patients' body parts in real time in an HIS (Hospital Information System) based environment.

The provided text is a 510(k) Summary for the X-Clever (ASHK100G) device. Within this summary, information is given about performance testing relating to a new Wide Dynamic View (WDV) algorithm. However, the document does not provide a table of acceptance criteria, specific reported device performance metrics against those criteria, or the detailed study design (sample sizes, expert qualifications, adjudication methods, MRMC study details, ground truth specifics for test and training sets) that would typically be found in a detailed study report.

Here's a breakdown of the available information and what is not present:

1. Table of Acceptance Criteria and Reported Device Performance:

The document states: "The performance test results indicate that the WDV algorithm enhances the performance of the proposed medical device by normalizing tissue through the creation of a regional map based on image location and distribution characteristics. This leads to more natural and consistent images compared to those that rely solely on residual and image brightness signal composition."

However, this is a qualitative statement, not a table with specific acceptance criteria (e.g., quantitative metrics like AUC, sensitivity, specificity, or specific perceptual scores with thresholds) and corresponding numerical results.

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

• Sample size: Not specified.
• Data provenance: Not specified. The document only mentions "clinical opinions on the images processed with WDV," implying human readers were involved in assessing image quality, but it does not detail the origin of these images (e.g., country, retrospective/prospective).

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

The document mentions "clinical opinions on the images processed with WDV." This suggests that experts evaluated the images, but:

• Number of experts: Not specified.
• Qualifications of experts: Not specified (e.g., "radiologist with 10 years of experience").

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

Not specified. The process of how "clinical opinions" were combined or used to establish a ground truth or a performance measure is not detailed.

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:

• MRMC study: The document does not explicitly state that a formal Multi-Reader Multi-Case (MRMC) comparative effectiveness study was done comparing human readers with and without AI assistance. It mentions "clinical opinions on the images processed with WDV," which implies human review of images processed by the device's new algorithm, but it doesn't describe a comparison between human performance with and without the device's assistance.
• Effect size of human reader improvement: Not reported.

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

The device (X-Clever ASHK100G) is described as "Software used in a device that saves, enlarges, reduces, views as well as analyzes, transfers and prints medical images." The "WDV algorithm" is an image processing algorithm. Its performance is assessed in terms of generating "more natural and consistent images." This evaluation implicitly refers to the standalone performance of the algorithm in processing images, but it's not a diagnostic algorithm outputting clinical findings directly. The "clinical opinions" are likely an assessment of the quality of the images produced by the algorithm, rather than its diagnostic accuracy for specific conditions.

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

The "ground truth" seems to be effectively expert opinion/consensus on image quality. The document explicitly states: "the performance test for the WDV algorithm includes clinical opinions on the images processed with WDV." It's not based on pathology, outcomes data, or a definitive diagnostic reference standard for a specific disease. Instead, it's about the perceived improvement in image characteristics.

8. The sample size for the training set:

Not specified. The document discusses a "new WDV algorithm" and "optimization process," indicating machine learning or image processing algorithm development, but it does not provide details on training data.

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

Not specified. As the training set size itself is not mentioned, neither is the method for establishing its ground truth.

Summary of Available Information (from the provided text):

• Device: X-Clever (ASHK100G), a medical image management and processing system.
• Key Change: Addition of a Wide Dynamic View (WDV) algorithm for image processing.
• Performance Claim for WDV: "enhances the performance of the proposed medical device by normalizing tissue through the creation of a regional map based on image location and distribution characteristics. This leads to more natural and consistent images compared to those that rely solely on residual and image brightness signal composition."
• Performance Evaluation: A "performance test for the WDV algorithm includes clinical opinions on the images processed with WDV."
• Conclusion: "the addition of the WDV feature has not had any negative impact on the performance and safety of the proposed device."
• Clinical Studies: "No clinical studies were considered necessary and performed. ... Therefore, a separate clinical study is not applicable in this case."

In essence, the document confirms that a performance test was conducted for the WDV algorithm using clinical opinions on image quality, and the results were positive (improved image naturalness and consistency). However, it lacks the detailed quantitative metrics, sample sizes, and expert qualification specifics typically requested for a comprehensive study description.

Ask a specific question about this device

This Medical Monitor is indicated for use in displaying radiological images (including full-field digital mammography and digital breast tomosynthesis) for review, analysis, and diagnosis by trained medical practitioners.

The Medical monitor is intended to provide high resolution color and grayscale medical imaging for PACS and Radiology system. This Medical Monitor is intended to be used by trained medical practitioners for displaying, reviewing, and analysis of medical images

The LG Medical Monitor (Model 21HQ613D) is indicated for displaying radiological images, including full-field digital mammography and digital breast tomosynthesis, for review, analysis, and diagnosis by trained medical practitioners.

Here's a breakdown of the acceptance criteria and the study conducted:

1. Table of Acceptance Criteria and Reported Device Performance:

The document states that "All display characteristics of 21HQ613D have met the pre-determined criteria." These criteria are implicitly defined by the successful "PASS" result for each measurement stated in the Non-Clinical Test Summary. The acceptance criteria are derived from the FDA guidance "Display Devices for Diagnostic Radiology".

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

The document describes a "Bench Test Performance Test" where "Physical Laboratory Test items suggested in the FDA guidance 'Display Devices for Diagnostic Radiology' were tested on 21HQ613D." This indicates that the testing was performed on the device itself (LG Medical Monitor, Model 21HQ613D). The sample size is not explicitly stated as a number of devices, but rather relates to the inherent characteristics of a single device under various measurement conditions. The data provenance is from laboratory testing of the physical device by LG Electronics Inc., South Korea. This would be considered prospective testing for the specific device model seeking clearance.

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

This information is not provided in the document. The study described is a non-clinical bench test of the display device's performance characteristics, not a study involving human interpretation of images where ground truth would typically be established by expert readers.

4. Adjudication Method for the Test Set:

This information is not applicable to the described study. Adjudication methods are typically used in clinical studies involving multiple readers to resolve discrepancies in diagnoses or assessments. The reported study performed physical laboratory tests with specific objective measurements.

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:

A Multi Reader Multi Case (MRMC) comparative effectiveness study was not done. This submission is for a medical monitor, not an AI-powered diagnostic or assistive tool. The studies focused on the performance of the display hardware itself.

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

A standalone algorithm-only performance study was not done in the context of diagnostic interpretations. The study focused on the performance of a medical display device, which is a "standalone" device in the sense that its performance characteristics are measured independently of a human user making a diagnosis. However, this is not an algorithm performing a diagnostic task.

7. The Type of Ground Truth Used:

The "ground truth" for the non-clinical bench tests was the objective physical measurements of the display's characteristics against pre-determined engineering and display quality standards outlined in the FDA guidance "Display Devices for Diagnostic Radiology." This is analogous to a reference standard for physical characteristics rather than a clinical diagnosis ground truth (e.g., pathology, outcomes data).

8. The Sample Size for the Training Set:

This information is not applicable. The device is a medical monitor, which is hardware for displaying images. It does not utilize a training set in the way a machine learning algorithm would. While its internal calibration tools (LG Calibration Studio Medical and DBI Calibration Feedback System) contain software, these are for maintaining display quality, not for image analysis requiring a training set for diagnostic tasks. The software for these tools was "designed and developed according to a software development process and was verified and validated" according to IEC 62304.

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

This information is not applicable as there is no training set for a diagnostic algorithm.

Ask a specific question about this device

This Medical Monitor is indicated for use in displaying radiological images for review, analysis, and diagnosis by trained medical practitioners. The display is not intended for mammography.

The Medical monitor is intended to provide high resolution color and grayscale medical imaging for PACS and Radiology system. This Medical Monitor is intended to be used by trained medical practitioners for displaying, reviewing, and analysis of medical images.

The provided text describes a 510(k) premarket notification for a medical monitor, 24HR513C, and its comparison to predicate devices, but it does not contain information about acceptance criteria or a study proving the device meets those criteria in the context of clinical performance or AI algorithm effectiveness.

The document primarily focuses on demonstrating substantial equivalence to predicate medical monitors based on technological characteristics and non-clinical performance (electrical safety, EMC, software validation, and display performance measurements).

Therefore, I cannot provide the requested information for acceptance criteria and a study proving the device meets them, as it is not present in the given text.

Here's what I can extract from the provided text, indicating what is not available:

1. Table of Acceptance Criteria and Reported Device Performance:

No specific "acceptance criteria" for clinical performance are mentioned, nor is there a study reporting device performance against such. The document discusses technological characteristics of the monitor itself and its compliance with certain standards for electrical safety, EMC, and software validation.

The "Measurements" table under "Non-Clinical Test summary" (pages 9-10) describes performance items for the display, but these are general display characteristics and not explicitly stated as "acceptance criteria" with quantitative targets met by a device study. Instead, they are evaluated for "Equivalence" or "Same" to predicate devices.

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

• Not Applicable. No test set for clinical performance is mentioned. The non-clinical tests relate to the monitor's display characteristics and software validation. Data provenance for these technical tests is not specified (e.g., country of origin, retrospective/prospective).

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

• Not Applicable. No clinical test set with ground truth established by experts is mentioned. The device is a medical monitor, not an AI diagnostic tool that requires expert ground truth for image interpretation.

4. Adjudication method for the test set:

• Not Applicable. As no clinical test set is mentioned, no adjudication method is relevant.

5. If a multi-reader multi-case (MRMC) comparative effectiveness study was done, and its effect size:

• No. The document explicitly states: "No clinical studies were considered necessary and performed." This device is a medical monitor, not an AI-powered diagnostic aide, so an MRMC study comparing human readers with and without AI assistance is not relevant to its clearance.

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

• Not Applicable. The device is a medical monitor. This question pertains to AI algorithms, which are not described here.

7. The type of ground truth used:

• Not Applicable. No clinical ground truth (expert consensus, pathology, outcomes data) is mentioned as no clinical studies were performed. The non-clinical tests rely on technical specifications and established standards.

8. The sample size for the training set:

• Not Applicable. There is no mention of a "training set" as this device is a medical monitor, not an AI model requiring a training dataset.

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

• Not Applicable. As no training set is mentioned, this information is not relevant.

In summary, the provided document focuses on the technical specifications and non-clinical testing of a medical monitor to demonstrate its substantial equivalence to previously cleared devices. It explicitly states that "No clinical studies were considered necessary and performed," indicating that the type of performance data and acceptance criteria you're asking about (which generally relate to diagnostic accuracy or clinical utility) are not part of this 510(k) submission.

Ask a specific question about this device

21HQ513D, 32HL512D: This Medical Monitor is indicated for use in displaying radiological images for review, analysis, and diagnosis by trained medical practitioners. The display is not intended for mammography.
31HN713D, 32HO713D: This Medical Monitor is indicated for use in displaying radiological images (including full-field digital mammography and digital breast tomosynthesis) for review, analysis, and diagnosis by trained medical practitioners.

The Medical monitor is intended to provide high resolution color and grayscale medical imaging for PACS and Radiology system. This Medical Monitor is intended to be used by trained medical practitioners for displaying, reviewing, and analysis of medical images.

The provided document describes the acceptance criteria and the results of the study for the medical monitors 21HQ513D, 32HL512D, 31HN713D, and 32HQ713D.

Here's the requested information:

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

The document refers to the "performance items suggested in the FDA guidance 'Display Devices for Diagnostic Radiology'" as the acceptance criteria. The performance for each measurement is uniformly reported as "Pass" for the tested items.

Note: The "N/A" for certain measurements for models 21HQ513D and 32HL512D might indicate that these tests were not applicable or not performed for these specific models, possibly due to their differing indications for use (not intended for mammography, unlike 31HN713D and 32HQ713D). The document does not explicitly state the acceptance numerical values for each criterion but implies compliance with the FDA guidance "Display Devices for Diagnostic Radiology".

2. Sample size used for the test set and the data provenance
The document does not specify a "test set" in terms of patient data or images. The study described is a non-clinical bench test on the display devices themselves. The sample size for the test set would be the number of devices tested, which is implied to be one of each model (21HQ513D, 32HL512D, 31HN713D, and 32HQ713D). The data provenance is not applicable in the typical sense of clinical data, as it's a technical performance test of hardware.

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts
This information is not applicable. The study is a non-clinical bench test of display performance against predefined technical standards and FDA guidance, not a study involving expert interpretation of medical images.

4. Adjudication method for the test set
This information is not applicable. There was no expert adjudication process as it was a technical performance test.

5. If a multi-reader multi-case (MRMC) comparative effectiveness study was done, If so, what was the effect size of how much human readers improve with AI vs without AI assistance
No MRMC comparative effectiveness study was done. The device is a medical monitor, not an AI-powered diagnostic tool. The document explicitly states: "No clinical studies were considered necessary and performed."

6. If a standalone (i.e., algorithm only without human-in-the-loop performance) was done
This is not applicable as the device is a medical monitor, not an algorithm or AI system. The study focused on the technical performance of the monitors.

7. The type of ground truth used
For the bench test, the ground truth was based on the performance items suggested in the FDA guidance "Display Devices for Diagnostic Radiology." This refers to quantifiable technical specifications and standards for medical image displays.

8. The sample size for the training set
This is not applicable. The device is a medical monitor, not a machine learning algorithm that requires a training set. The software components underwent validation according to IEC 62304.

9. How the ground truth for the training set was established
This is not applicable, as there was no training set for an AI algorithm. The validation of the software was done according to IEC 62304, which involves verifying the software against its design specifications and requirements.

Ask a specific question about this device

32HL512D: This Medical Monitor is indicated for use in displaying radiological images for review, analysis, and diagnosis by trained medical practitioners. The display is not intended for mammography.

31HN713D, 32HQ713D: This Medical Monitor is indicated for use in displaying radiological images (including full-field digital mammography and digital breast tomosynthesis) for review, analysis, and diagnosis by trained medical practitioners.

The Medical monitor is intended to provide high resolution color and grayscale medical imaging for PACS and Radiology system. This Medical Monitor is intended to be used by trained medical practitioners for displaying, reviewing, and analysis of medical images.

The provided text describes a medical monitor device (32HL512D, 31HN713D, 32HQ713D) and its associated software, "LG Calibration Studio Medical," which is used for calibration. The acceptance criteria and the study performed relate to the performance of the medical monitor and the calibration software, not an AI algorithm for image analysis. Therefore, there is no information about AI-specific aspects such as training sets, ground truth establishment for AI, or comparative effectiveness studies with human readers assisted by AI.

Here's the information derived from the text regarding the device and its software:

1. Table of Acceptance Criteria and Reported Device Performance

The acceptance criteria for the medical monitors are derived from the FDA guidance "Display Devices for Diagnostic Radiology." The reported performance for all three models (32HL512D, 31HN713D, 32HQ713D) is that they "Pass" all tested measurements.

Note: The document states, "All display characteristics of the 32HL512D, 31HN713D and 32HQ713D have met the predefined criteria. Therefore, the performance of 32HL512D, 31HN713D and 32HQ713D were verified through the performance test." The FDA guidance "Display Devices for Diagnostic Radiology" serves as the basis for these predefined criteria.

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

• Test Set Sample Size: Not explicitly stated as the testing is on the physical device's characteristics rather than a dataset of medical images. The tests were performed on one or more units of each model (32HL512D, 31HN713D, 32HQ713D).
• Data Provenance: Not applicable in the context of device performance testing. The "data" here refers to measurements taken directly from the physical monitors during bench testing. The tests adhere to international standards (e.g., IEC standards) and FDA guidance for medical display devices.

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

Not applicable. The ground truth for the performance of the medical monitors is established by objective physical measurements against established technical standards and FDA guidance, not by expert interpretation of images.

4. Adjudication Method for the Test Set

Not applicable. Adjudication methods like 2+1 or 3+1 are typically used for expert consensus in image interpretation. This study involves objective technical measurements.

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

No. There is no mention of an MRMC study. The devices are medical monitors for displaying images, not AI algorithms.

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

A standalone performance test was done for the device, focusing on its technical display characteristics. The "LG Calibration Studio Medical" software (a moderate level of concern software) was also verified and validated, but this is a calibration tool, not an image analysis algorithm. Therefore, "standalone (i.e. algorithm only without human-in-the-loop performance)" is applicable to the technical performance of the monitor as a device, and to the verification and validation of the calibration software, but not in the context of AI for medical image interpretation.

7. The Type of Ground Truth Used

The ground truth used for the device's performance testing is based on objective physical measurements and technical specifications set forth by international standards (e.g., IEC 60601-1, IEC 60601-1-2, IEC 62304) and the FDA guidance "Display Devices for Diagnostic Radiology."

8. The Sample Size for the Training Set

Not applicable, as this is not an AI algorithm for image analysis. The "LG Calibration Studio Medical" is software, validated according to IEC 62304, but the text does not describe a "training set" in the machine learning sense for this calibration software.

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

Not applicable. There is no training set for an AI algorithm mentioned in relation to the devices or their calibration software.

Ask a specific question about this device

This Medical Monitor is indicated for use in displaying radiological images (including full-field digital mammography and digital breast tomosynthesis) for review, and diagnosis by trained medical practitioners.

The Medical monitor is intended to provide high resolution color and grayscale medical imaging for PACS and Radiology system. This Medical Monitor is intended to be used by trained medical practitioners for displaying, reviewing, and analysis of medical images

The provided text describes the 510(k) submission for the LG Electronics 32HQ713D Medical Monitor, a device indicated for displaying radiological images. It's important to note that this document does not contain a study proving the device meets acceptance criteria in the way a clinical or AI performance study would. Instead, it demonstrates substantial equivalence to predicate devices through technical comparisons and non-clinical performance testing.

The acceptance criteria are implicitly defined by the functional characteristics and performance parameters expected of a medical monitor for radiological images, particularly for mammography and digital breast tomosynthesis. The performance is demonstrated by comparing these characteristics to established predicate devices and by confirming adherence to relevant standards and guidance documents.

Here's the information extracted and organized as requested:

1. Table of Acceptance Criteria and Reported Device Performance

The "acceptance criteria" here are framed as demonstrating equivalence to predicate devices and compliance with relevant standards and guidance. The reported performance is the claim of equivalence based on the measurements.

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

• Sample Size: Not applicable in the context of an "AI test set." This submission describes the testing of a medical monitor (hardware/software), not an AI algorithm. The tests conducted (e.g., spatial resolution, luminance) are on the physical device itself. The document does not specify the number of units tested, but this would typically be a small, representative sample of manufactured devices.
• Data Provenance: Not applicable as there is no patient data involved in these non-clinical performance tests of a display monitor. The tests are based on standard test patterns and measurement methodologies.

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

Not applicable. Ground truth in the context of a medical display's performance relates to its physical characteristics and adherence to technical specifications and industry standards (e.g., DICOM Part 14 for Grayscale Standard Display Function). These are objectively measurable and do not require expert human interpretation to establish a "ground truth" in the way an AI diagnostic model would.

4. Adjudication Method for the Test Set

Not applicable. See point 3.

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

No MRMC study was performed or described. This submission is for a medical monitor, not an AI-based diagnostic aid that assists human readers.

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

Not applicable. As noted, this is a medical display, not an AI algorithm. Its "performance" is its ability to accurately and consistently render images, which is tested directly.

7. The Type of Ground Truth Used

The "ground truth" for this device's performance is objective technical specifications and industry standards for medical displays. These include:

• Physical measurements (e.g., luminance, resolution, pixel defects).
• Compliance with electrical safety, EMC, and usability standards (e.g., IEC 60601 series).
• Conformance to display functions like the Greyscale Standard Display Function (implicitly referenced through the "conformance to a grayscale-to-luminance function" test).
• The performance of legally marketed predicate devices, which serve as a benchmark for "substantial equivalence."

8. The Sample Size for the Training Set

Not applicable. This device is a medical monitor, not an AI system that requires a "training set" of data.

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

Not applicable. See point 8.

Ask a specific question about this device

The Flat Panel Digital X-ray Detector 14HQ721G-B is indicated for digital imaging solution designed for general radiographic system for human anatomy. It is intended to replace film or screen based radiographic systems in all general purpose diagnostic procedures. Not to be used for mammography.

This model is an x-ray imaging device, a system that can acquire and process X-ray images as digital images. It utilizes amorphous silicon and a high-performance scintillator to ensure sharp highdefinition image quality with the resolution of 3.6 lp/mm and the pixel pitches of 140 um. This device is a flat panel based X-ray image acquisition device. This device must be used in conjunction with an operating PC and an X-ray qenerator. This device can be used for digitizing and transferring X-ray images for radiological diagnosis. The data transmission between the Detector and PC can be enabled with a wired (cable) or wireless connection. This device does not have a dynamic exposure feature.

This document describes the LG Electronics Inc. Flat Panel Digital X-ray Detector 14HQ721G-B. The information provided outlines the device's acceptance criteria and the studies conducted to demonstrate its performance and substantial equivalence to a predicate device.

1. Table of Acceptance Criteria and Reported Device Performance

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

The document explicitly states: "In this submission, clinical testing was not conducted, since nonclinical information is sufficient to show the substantial equivalence of the proposed devices to the predicate devices for the modifications." This implies that there was no specific "test set" of patient data for clinical performance evaluation. The substantial equivalence was primarily based on non-clinical engineering and performance testing comparing the proposed device to the predicate device.

Therefore, information on sample size, country of origin, and retrospective/prospective nature of a clinical test set is not applicable as no clinical test set was used for this submission's substantial equivalence demonstration.

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

Not applicable, as no clinical test set was used to establish ground truth for this submission.

4. Adjudication Method for the Test Set

Not applicable, as no clinical test set was used.

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

No MRMC comparative effectiveness study was conducted as clinical testing was not performed. The submission relies on non-clinical data to demonstrate substantial equivalence.

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

The device itself is a Flat Panel Digital X-ray Detector, which is a hardware component for acquiring X-ray images. It includes firmware (software of moderate level of concern) that was validated. The performance tests (DQE, MTF, resolution) are measures of the standalone imaging capabilities of the device itself, without a human in the loop for interpretation beyond assessing image quality metrics. The comparison of these metrics to the predicate device effectively serves as a standalone performance assessment.

7. The Type of Ground Truth Used

The ground truth for performance was established through objective engineering and physical measurements based on established standards (e.g., IEC 62220-1-1 for DQE) and comparison to the predicate device's specifications. This is a technical ground truth for imaging performance rather than a clinical ground truth based on expert consensus, pathology, or outcomes data from patients.

8. The Sample Size for the Training Set

The document does not provide information on a training set size. This is consistent with the nature of the device being a digital X-ray detector, where performance is typically validated through engineering tests and comparison to a predicate, rather than through a machine learning model that requires a "training set" of data in the conventional sense. The "software update" mentioned includes the addition of the AEC mode, and this software was designed, developed, verified, and validated internally, but no specific training data for an AI algorithm is indicated.

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

Not applicable, as no training set for a machine learning model is described. The ground truth for the device's technical performance was established through standardized physical measurements and component specifications, as detailed in point 7.

Ask a specific question about this device