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Acquire, store, transmit, and display medical images and patient reports of various types. Teleradiology image acquisition, distribution, archive and viewing.

The IiVS™ Integrated image Viewing Station is a product family, which comes in two different versions: DiVS: a DICOM viewer IiVS: a 3D viewer. The intended use of the devices is to provide solutions to various medical image-viewing problems, which come about as the modalities generate more and more images. They also support image distribution over networks, and are DICOM conformant. Finally, the IIVS™ Integrated image Viewing Station family supports the radiologist in writing a report, and transmitting and storing this report in digital form.

The provided text describes a 510(k) premarket notification for the TeraRecon IiVS™ Integrated Image Viewing Station (K994329). This document primarily focuses on demonstrating substantial equivalence to a predicate device rather than providing a detailed study proving the device meets specific acceptance criteria with quantifiable performance metrics.

Therefore, much of the requested information regarding acceptance criteria, study details, sample sizes, ground truth establishment, and MRMC studies is not explicitly available in the provided text.

However, I can extract and infer some information based on the document's content, particularly from the "Performance Standards" section and the "Comparison Table."

Here's the information that can be extracted and inferred:

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

The document does not explicitly present a table of acceptance criteria with corresponding reported performance metrics in the way a typical clinical study would. Instead, it relies on demonstrating compliance with standards and comparing features to a predicate device.

Inferred "Acceptance Criteria" (based on compliance and comparison):

Note on "Reported Device Performance": For a 510(k) submission like this, "reported performance" often means demonstrating that the device meets its design specifications, is compliant with relevant standards, and is substantially equivalent to a legally marketed predicate device. Actual quantitative performance metrics from a dedicated study are typically not included unless addressing a specific performance claim.

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

The document does not describe a specific "test set" for performance evaluation in the context of a clinical study or image dataset. The submission focuses on technical specifications, compliance with standards (DICOM, ISO/IEC 12207), and a feature-by-feature comparison to a predicate device. Therefore, information about sample size, data provenance, and retrospective/prospective nature is not provided.

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

Since there is no described "test set" or clinical study involving image interpretation, there is no information about experts establishing ground truth.

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

As no test set involving expert review is described, there is no information on an adjudication method.

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

The document does not describe an MRMC comparative effectiveness study, nor does it refer to AI assistance. This device is an image viewing and communication system, not an AI-powered diagnostic aid.

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

The device is an image viewing and communication system designed to be used by human radiologists. It is not an algorithm intended for standalone diagnostic performance. Therefore, analysis of "standalone" algorithm performance is not applicable or described.

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

Since no clinical performance study involving diagnostic interpretation is described, there is no mention of the type of ground truth used. The ground truth for this type of device relates to its technical functionality (e.g., a DICOM file is correctly transferred and displayed, a report is correctly generated).

8. The sample size for the training set

The document does not describe a "training set" as it is not an AI/machine learning device. The software development follows ISO/IEC 12207, implying standard software testing and validation, but not machine learning training.

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

As there is no "training set" in the context of machine learning, this question is not applicable and no information is provided.

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Stentor PageView is a digital medical imaging distribution and display system to be used for both primary diagnosis and efficient distribution of medical images within the healthcare enterprise. It operates on the conventional TCP/IP internetworking infrastructure available in most healthcare organizations, and it uses commercially available computer platforms (Intel Pentium-based) and operating systems (Microsoft Windows NT, Windows 95, and Windows 98). The system does not permanently store or produce original medical images.

Stentor PageView is a digital medical imaging distribution and display system to be used for both primary diagnosis and efficient distribution of medical images within the healthcare enterprise. It operates on the conventional TCP/IP internetworking infrastructure available in most healthcare organizations, and it uses commercially available computer platforms (Intel Pentium-based) and operating systems (Microsoft Windows NT, Windows 95, and Windows 98). The system does not permanently store or produce original medical images.

The provided text does not describe acceptance criteria for device performance or any study conducted to prove the device meets such criteria.

The document is a 510(k) summary for the Stentor PageView software, submitted to the FDA in 1999. Its primary purpose is to demonstrate substantial equivalence to previously cleared devices, not to present performance data against specific acceptance criteria.

Here's a breakdown of why the requested information cannot be extracted from the provided text:

• No Acceptance Criteria or Performance Data: The document states that the software was "designed, developed, tested and validated according to written procedures" and that "Extensive testing of the software package has been performed." However, it does not provide any specific quantitative performance metrics (e.g., accuracy, speed, uptime, image fidelity measures) that would constitute acceptance criteria, nor does it report the results of any such tests.
• Focus on Substantial Equivalence: The bulk of the "Safety and Effectiveness" section and the "Substantial Equivalence Chart" (Section 1.9) compare the PageView's features and technological characteristics to predicate devices (Mitra-Exhibit and PACScache). The conclusion is that differences have "no significant influence on safety or effectiveness," which is a regulatory argument for equivalence, not a detailed performance study.
• No Mention of Specific Study Design: There is no description of a study (retrospective/prospective, sample sizes, data provenance, expert roles, adjudication methods, MRMC studies, or standalone performance) that would be typically conducted to prove performance against specific criteria.
• Ground Truth: Since no performance study is described, there's no mention of how ground truth would be established for any test or training set.
• Training Set Information: There is no information about a training set, as the document does not describe the development or validation of an AI/ML algorithm that would typically require one. The software's function is described as converting DICOM images for display, not a diagnostic algorithm.

In summary, the provided text does not contain the information required to populate the requested table or answer the specific questions about acceptance criteria and performance studies because the document's intent is to establish substantial equivalence based on feature comparison, not to report on quantitative performance metrics.

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The Web Clinical System is intended for the management and displaying of x-ray images, other radiological objects, and information. It can manage images from different modalities, single and multiple file servers, and interfaces to various Radiological Information Systems (RIS), image storage and printing devices using DICOM or similar interface standards within the system or across computer networks at distributed locations.

The Web-Clinical System (WCS) is a component of the RAYPAX System, a PACS solution by Samsung or can be a separate device for other manufacturer's PACS. The WCS is a system for managing radiological images and data by retrieving and browsing medical images through the World Wide Web, (Internet), a clients Intranet or over computer networks.

This document is a 510(k) summary for the 삼성 SDS Co. Ltd. RAYPAX™ Web Clinical System (WCS). It establishes substantial equivalence to a predicate device for marketing approval. However, it does not contain the information requested regarding acceptance criteria and a study proving the device meets those criteria.

Specifically, the following information is not present in the provided text:

1. A table of acceptance criteria and the reported device performance: This document describes the device's intended use and technological characteristics but does not define specific performance metrics or acceptance criteria for those metrics.
2. Sample size used for the test set and the data provenance: No information about a test set, its size, or its origin (country, retrospective/prospective) is provided.
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts: As there's no test set described, there's no mention of experts or ground truth establishment.
4. Adjudication method for the test set: No test set means no adjudication method.
5. If a multi-reader multi-case (MRMC) comparative effectiveness study was done, and the effect size of how much human readers improve with AI vs without AI assistance: This is a system for managing and displaying images, not an AI or diagnostic aid that would typically undergo MRMC studies with human readers for improved performance.
6. If a standalone (i.e., algorithm only without human-in-the-loop performance) was done: The device description emphasizes human interpretation ("A physician, providing ample opportunity for competent human interprets images and information being printed."), indicating it's not a standalone diagnostic algorithm.
7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.): No ground truth is mentioned.
8. The sample size for the training set: No training set is mentioned as this is not an AI/ML device that requires training.
9. How the ground truth for the training set was established: Not applicable without a training set.

Summary of available information related to device evaluation:

The document primarily focuses on demonstrating substantial equivalence to a predicate device (SECTRA IMTEC AB Wise II Image Management System) based on indications for use and technological characteristics. It mentions:

• Hazard Analysis: All potential hazards have been classified as "Minor."
• The system has been designed and will be manufactured in accordance with voluntary standards.
• The FDA's letter (K992426) confirms the device's substantial equivalence to legally marketed predicate devices.

This 510(k) process for this type of device (a digital imaging workstation/image communication accessory) in 1999 typically would not involve the kind of clinical performance studies and AI-specific metrics you've asked for. It's a system to manage and display images, meaning its efficacy is largely tied to its functionality, reliability, and security, rather than diagnostic accuracy as a standalone AI model.

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iTR2000 is a software communications tool intended to be used in the transportation, storage and retrieval of digital medical images for the purpose of off-site review.

iTR2000™ is a stand-alone software product which may be marketed as a software-only product, as well as for use in conjunction with standard PC hardware, off-the-shelf software or third-party teleradiology/PACS software. iTR2000™ provides short-term remote access to medical images by radiologists, referring physicians and other licensed professionals, utilizing a personal computer or workstation with internet access. Images are securely stored on a internet website for on-demand remote retrieval via the iTR2000™ software, or via a standard web browser without the use of propriety software. The iTR2000™ system employs the latest internet security techniques and meets all current federal medical communications standards including recent proposals from HCFA and HHS. iTR2000™ is primarily intended to allow the transmission, retrieval and review of images produced by imaging equipment otherwise not part of a digital Picture Archive and Communication System (PACS) network or legacy equipment not compatible with ACR/NEMA DICOM 3.0. Conventional film-based images can be optically digitized and stored in a standard image format, such as JPEG. iTR2000™ can also be configured to web-enable third-party DICOM-capable teleradiology/PACS systems in low-volume environments for remote or on-call activities. iTR2000™ is designed for use primarily with small-matrix imaging modalities, such as images produced by Computed Tomography (CT), Ultrasound (US), Magnetic Resonance Imaging (MRI), Nuclear Medicine (NM), digital flourography and digital angiography. The use of images produced by large-matrix imaging systems, such as digitized radiographic films and computed radiography or mammography. requires the use of digitization and viewing equipment which exceed the stated minimum requirements for the standard iTR2000™ system. iTR2000™ does not control the actual image-taking system (i.e. x-ray, MRI, ultrasound or scintography machines).

This document describes the iTR2000™ teleradiology software, its intended use, and its substantial equivalence to predicate devices, rather than a study proving the device meets specific performance acceptance criteria. The document is a 510(k) Summary, which is a premarket submission made to FDA to demonstrate that the device to be marketed is as safe and effective, that is, substantially equivalent, to a legally marketed device.

Therefore, many of the requested details about acceptance criteria, study design, and performance metrics are not explicitly provided in this type of document.

Here's an analysis based on the provided text, addressing the points where information is available or noting its absence:

1. Table of Acceptance Criteria and Reported Device Performance

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

• Sample Size for Test Set: "Functional testing including the transfer of diagnostic imaging studies for over 1200 complete studies." The document does not specify if these "studies" were used as a "test set" in a formal performance evaluation against predefined criteria or if this refers to internal development testing. It's likely the latter given the context of a 510(k) summary focused on substantial equivalence.
• Data Provenance: Not specified. It's almost certainly retrospective given the time period and nature of the product (software for image transfer). Country of origin is not mentioned.

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

• Not Applicable / Not Provided: The document does not describe a study involving expert-established ground truth for a test set to measure diagnostic accuracy or similar performance. The device is a communication tool, not a diagnostic algorithm. The "ground truth" for its function would be successful and accurate transfer of image data, which was likely evaluated through technical means rather than expert adjudication of clinical findings.

4. Adjudication Method for the Test Set

• Not Applicable / Not Provided: As no expert-established ground truth or clinical performance study is described for a test set, there is no mention of an adjudication method.

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

• No: No MRMC study is described. The device is a teleradiology software for image transfer, not an AI-assisted diagnostic tool for human readers.

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

• Yes (for its function as a communication tool): The "functional testing" involving the transfer of over 1200 studies represents a standalone evaluation of the software's core function: to transfer images. The criteria for this testing would have related to successful transmission, integrity of the data, and speed, not clinical diagnostic performance.

7. The Type of Ground Truth Used

• Technical Verification: For the described functional testing, the "ground truth" would have been the integrity and successful transmission of the digital image files. This would be verified through technical checks (e.g., checksums, visual comparison to original files, successful display) rather than clinical ground truth like pathology or outcomes data.

8. The Sample Size for the Training Set

• Not Applicable / Not Provided: This device is software for image communication and transfer, not a machine learning or AI algorithm that requires a training set in the typical sense. Functional testing (described above) would be more analogous to system validation.

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

• Not Applicable / Not Provided: As explained in point 8, there's no "training set" in the context of an AI/ML algorithm.

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The Samsung RAYPAX™ is a device that receives digital images and data from various sources (including but not limited to CT scanners, MR scanners, ultrasound systems, R/F Units, computed & direct radiographic devices, secondary capture devices, scanners, imaging gateways or other imaging sources). Images and data can be stored, communicated, processed and displayed within the system and or across computer networks at distributed locations.

The Samsung RAYPAX™ system handles various objects in a Picture Archive and Communication System (PACS) environment. These objects can be images, requests, patients, examination etc. PACS transmits digital electronic images and generates reports over a high-speed network to centralized storage. After transmission, patient information and images are available throughout the facility to many users simultaneously.

The provided text does not contain any information about acceptance criteria or a study proving the device meets said criteria.

The document is a 510(k) summary for the Samsung RAYPAX™ System, which is a Digital Imaging System (PACS). It primarily focuses on demonstrating substantial equivalence to a predicate device.

The information requested in the prompt (acceptance criteria, device performance, sample sizes for test/training sets, data provenance, expert qualifications, adjudication methods, MRMC studies, standalone performance, and ground truth establishment) is not present in the provided text. The document states that the system "has been and will be manufactured in accordance with the voluntary standards listed in the enclosed voluntary standard survey" and that "The submission contains the results of a hazard analysis. All potential hazards have been classified as Minor." However, these are general statements about manufacturing and risk assessment, not specific performance criteria or a detailed study.

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The StealthSERVER™ Image Storage and Communication System is used to store and provide remote viewing access to diagnostic images via a LAN, the internet or modem. In addition, the system can be used to remotely set surgical plans for stereotactic surgery that are compatible with and can be accessed by the StealthStation® Treatment Guidance Platform.

This submission describes a network server system that provides remote access to digital image sets for the purpose of reviewing the images and, optionally, for developing and storing a stereotactic surgical plan that is compatible with the StealthStation® Image Guided Treatment Platform. The submission includes the capability of the StealthStation® System to retrieve the surgical plans and image sets from the network server.

This 510(k) submission for the StealthSERVER™ Image Storage and Communication System does not contain the information requested regarding acceptance criteria or a study proving the device meets said criteria.

The provided document is a summary and an FDA clearance letter, which focuses on:

• Device identification: StealthSERVER™ Image Storage and Communication System.
• Intended Use: Storing and providing remote viewing access to diagnostic images, and remotely setting surgical plans compatible with the StealthStation® Treatment Guidance Platform.
• Substantial Equivalence: Stating that the device was shown to be substantially equivalent to other commercially available medical image storage, review, and communication systems. The FDA clearance letter confirms this finding.

There is no mention of the following in the provided text:

• A table of acceptance criteria or reported device performance.
• Details about a study, including sample sizes, data provenance, ground truth establishment, or expert involvement.
• MRMC comparative effectiveness studies or standalone performance.

Therefore, I cannot provide the requested information based on the given input.

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ImageQUEST is communication system that has a database to allow Imaging Professionals to store and find their images and information. ImageQUEST can create teaching files, professional presentations and E-mail images to other professionals at remote locations and generate reports.

ImageQUEST is communication system that has a database to allow Imaging Professionals to store and find their images and information. ImageQUEST can create teaching files, professional presentations and E-mail images to other professionals at remote locations and generate reports.

This document is a 510(k) clearance letter from the FDA for a device called "ImageQUEST - Communications Software." It states that the device is substantially equivalent to legally marketed predicate devices. However, this letter does not contain any information about acceptance criteria, device performance studies, sample sizes, expert qualifications, or ground truth establishment.

The letter focuses on regulatory approval based on substantial equivalence and general controls, rather than on detailed performance metrics from a specific study.

Therefore, I cannot fulfill your request for the tables and study details based on the provided text. The document acts as an approval notice, not a performance study report.

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The Kodak Digital Science™ (KDS) ImageVIEW is a network-oriented client/server type PACS system for the distribution, viewing, and printing of medical images at distributed locations.

The Kodak Digital Science™ (KDS) ImageVIEW family of products is designed to facilitate the distribution of images from standard DICOM storage devices within the radiology department and provide controlled Internet access to images stored on DICOM storage devices. Browser extensions will allow dynamic viewing and manipulation of these medical images. This family of products will initially provide image viewing and referral printing functionality to referring and clinical physicians, but will be extended to provide diagnostic viewing and printing functionality for radiology use. They will also provide intelligent management of images and information to facilitate productivity enhancements for its users. ImageVIEWs are not represented to be of use in supporting or sustaining human life, nor do they represent a potential of unreasonable risk of illness or injury.

The provided text describes the Kodak Digital Science™ (KDS) ImageVIEW, a Picture Archiving and Communications Systems (PACS) component. However, the document is a 510(k) summary for premarket notification, indicating substantial equivalence to a predicate device, rather than a study report detailing specific acceptance criteria and performance metrics.

Therefore, the document does not contain the information required to populate the tables and answer the questions regarding acceptance criteria, device performance, or study details.

The text states: "We have reviewed your Section 510(k) notification of intent to market the device referenced above and we have determined the device is substantially equivalent (for the indications for use stated in the enclosure) to devices marketed in interstate commerce prior to May 28, 1976, the enactment date of the Medical Device Amendments, or to devices that have been reclassified in accordance with the provisions of the Federal Food, Drug, and Cosmetic Act (Act)." This indicates that the device was cleared based on its equivalence to a predicate device, not necessarily on a new clinical study assessing specific performance metrics against pre-defined acceptance criteria.

Information not found in the document:

• A table of acceptance criteria and reported device performance.
• Sample sizes for test or training sets.
• Data provenance.
• Number or qualifications of experts for ground truth.
• Adjudication method.
• Multi-Reader Multi-Case (MRMC) comparative effectiveness study information.
• Standalone performance details.
• Type of ground truth used.
• How ground truth for the training set was established.

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The JPACS radiology suite provides access to clinical reports and images via computer networks. Given the following information objects, the JPACS radiology suite may be used on any part of the human anatomy: CR, CT, MR, XA, SC, US, NM, PET, and RT. The suite also provides short, medium, and long term storage of these clinical images from any authorized workstation within an intranet or over the Internet. The full feature imaging suite can be used for in-house image distribution, on-call teleradiology, and advance visual display features. The JPACS radiology suite also includes functionality for generation and distribution of clinical reports.

The JPACS radiology suite is intended to provide access to clinical reports and images via computer networks. It is also intended to provide short, medium and long range storage of clinical images from any authorized workstation within an intranet or over the Internet. The full feature imaging suite can be used for in-house image distribution, on-call teleradiology, and advance visual display features. The JPACS radiology suite also includes functionality for generation and distribution of clinical reports. Picker adheres to FDA 21 CFR 820 and voluntary standards for safety and effectiveness (UL 187) all of which mandate that components are tested to minimize hazards (electrical, mechanical, and radiation). In addition, the system is designed to conform to IEC 601-1. The subject device has the same technological characteristics as a legally marketed predicate device. Specifically, the features, specifications, materials, and mode of operation are equivalent. External communication uses DICOM V. 3.0 NEMA Standards Publication Parts PS3.1 through PS3.13, dated 1996.

This document describes the JPACS (Picture Archiving & Communication System) and its substantial equivalence to a predicate device, the Medisurf manufactured by Algotec Systems Ltd. The information provided focuses on the regulatory submission and does not include a specific study proving the device meets acceptance criteria distinct from the substantial equivalence claim.

Therefore, many of the requested details about a specific performance study are not available in the provided text. The submission primarily asserts equivalence based on shared technological characteristics and intended use.

Here's the breakdown of the available information:

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

The document does not explicitly state quantitative acceptance criteria or report device performance in terms of metrics like sensitivity, specificity, accuracy, or other benchmarked performance indicators. Instead, the "acceptance criteria" here refers to the FDA's regulatory framework for substantial equivalence.

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

Not applicable. This was a 510(k) premarket notification for substantial equivalence, not a clinical performance study with a test set of data. The submission relies on demonstrating equivalence to a predicate device rather than a new performance evaluation on a specific dataset.

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

Not applicable, as no such test set or ground truth establishment process is described.

4. Adjudication method for the test set

Not applicable, as no such test set or adjudication process is described.

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

Not applicable. The JPACS is a Picture Archiving and Communication System (PACS), which is infrastructure for managing and displaying medical images and reports. It is not an AI-powered diagnostic tool, and therefore, an MRMC study related to AI assistance for human readers is not relevant to this device's type or its 510(k) submission.

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

Not applicable. The JPACS is a system facilitating human interaction with images and reports, not an algorithm performing a diagnostic task independently.

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

Not applicable, as no internal performance study using ground truth is described. The "ground truth" for the 510(k) submission is the demonstrated safety and effectiveness of the predicate device.

8. The sample size for the training set

Not applicable. No algorithm training is mentioned or relevant for this type of device and submission.

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

Not applicable. No algorithm training is mentioned or relevant.

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The use of DownScan LT is indicated whenever the source and destination of a video signal are incompatible due to different line rates or other signal attributes, and a standard frame rate video signal (30 or 25 frames/second) is required. Examples include conversion of X-ray (stationary, C-arm, angiography, etc.), nuclear medicine, magnetic resonance, and ultrasound images either directly from their source, or from an intermediate storage device (like a video tape or video disk), for use on display monitors, optical, tape, or disk recorders, or other apparatus requiring a standard frame rate signal.

DownScan LT is a digital image processing system that can convert from high line rate video standard of 1023-1125/60 or 1249/50 to low line rate video standards of 525/30 or 625/25. Housed in a 1 3/4" EIA half-rack mount chassis, DownScan LT operates from 100V to 240V AC power.

This document describes the Merlin DownScan LT, a digital image processing system designed to convert high line rate video to low line rate video. It serves as a regulatory submission (510(k)) to the FDA, demonstrating substantial equivalence to predicate devices.

1. Table of Acceptance Criteria and Reported Device Performance

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

The document does not specify a distinct "test set" with a dedicated sample size in the conventional sense of a clinical or image-based study. The performance tests described are related to engineering and signal processing standards. The data provenance is not explicitly stated as retrospective or prospective, but the tests were conducted by the manufacturer, Merlin Engineering Works, to demonstrate compliance with industry standards. The country of origin for the device's testing and manufacturer is the USA.

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

This information is not provided. The "ground truth" for the tests appears to be defined by established industry technical standards (RS-170, RS-343A, SMPTE RP-133) rather than expert consensus on medical images.

4. Adjudication Method for the Test Set

This information is not provided. Given the nature of the tests (compliance with technical specifications), it is likely that measurements and comparisons to defined standards were performed, rather than an adjudication process involving human interpretation of medical images.

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

No, a Multi-Reader Multi-Case (MRMC) comparative effectiveness study was not done for this device. The document describes a video signal processing system, not a diagnostic imaging aid that would typically involve human readers.

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

Yes, the performance tests described are inherently standalone in the sense that they evaluate the device's adherence to technical standards and objective performance metrics (e.g., aspect ratio compensation, low-contrast resolution) without human intervention in the primary function or assessment. The device's role is to convert video signals, not to interpret medical images.

7. The Type of Ground Truth Used

The ground truth used for these tests is based on established technical standards and specifications:

• RS-170 (a standard for monochrome video signals)
• RS-343A (another standard for monochrome video signals, often related to higher resolution)
• SMPTE RP-133 (a recommended practice by the Society of Motion Picture and Television Engineers, likely related to image quality and display characteristics for medical imaging).
  The performance criteria are objective measurements against these predefined benchmarks.

8. The Sample Size for the Training Set

This information is not applicable and not provided. The DownScan LT is a digital image processing system that converts video signals. It is not an AI/ML algorithm that requires a "training set" in the typical sense for learning patterns from data. Its function is based on engineered signal processing algorithms, not learned models.

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

This information is not applicable, as there is no "training set" for this type of device. The device's operation is based on predefined signal processing logic and hardware, not on machine learning from a dataset with established ground truth.

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