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The DirectRay™ Operator Console has application whenever the transmission of radiographic images and associated patient text data is desired to take place from an input device, such as any radiographic equipment which uses the DirectRay™ device, to any output device, such as hardcopy, softcopy, or archive devices.

The DirectRay™ Operator Console is that device, which when used with any X-ray generating equipment and the DirectRay™ device (Sterling Diagnostic Imaging Direct Radiography™ ' K#973206) allows the synchronization of the ready states of the DirectRay™ device and the X-ray generator, and allows the operator to acquire, preview and transmit image and associated text data along with exam administration report capabilities. In one version, this device will also integrate these functions with the typical functions of an X-ray console eliminating the need for a separate X-ray console. This device is located in the control booth and will usually be housed in a specially designed cabinet. An optional feature of the DROC is the ability to link the digital image with the hospital information/radiology information systems (HIS/RIS).

I am sorry, but the provided text does not contain information about the acceptance criteria for the device, any study proving it meets these criteria, or detailed performance metrics. The document focuses on the 510(k) summary for the "DirectRay™ Operator Console," describing its intended use, classification, comparison to predicate devices, and the FDA's clearance.

Therefore, I cannot provide a response with the requested information:

1. A table of acceptance criteria and the reported device performance
2. Sample size used for the test set and the data provenance
3. Number of experts used to establish the ground truth for the test set and their qualifications
4. Adjudication method
5. If a multi-reader multi-case (MRMC) comparative effectiveness study was done, or the effect size of human readers improvement with AI vs without AI assistance
6. If a standalone performance study was done
7. The type of ground truth used
8. The sample size for the training set
9. How the ground truth for the training set was established

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The iiSYS PACS System is a complete PACS System for the Transmission, Display, Archive, and printing of patient images and demographic information. The system is indicated for the assembly, organization, sharing, and display of patient images and demographic information for diagnostic and referral purposes.

Application areas include radiologist central reading rooms or any location where a medical professional would require or desire access to patient image and demographic information.

The iiSYS PACS System is a full featured PACS System capable of transmission, archive, display, and print of patient image and demographic information. Its purpose is to facilitate these operations utilizing shared data to promote the availability of information at remote facilities and at locations other than that at which it was acquired. Data may be received as digital information, video signals, or hard copy prints and may be reviewed via monitor or printed hardcopy. The iiSYS PACS system consists of the following major components:

• Series of Viewing and Reading Workstations
• Teleradiology devices for digitizing and transmission of images over wide area or local area networks for remote or at home review
• Archive for short or long term storage
• LINX Network system for secondary capture and transmission of images to other devices (workstation, telerad, printers, etc.)
• Printers

The provided text describes the "iiSYS PACS System," which is a Picture Archiving and Communication System. This document is a 510(k) submission, confirming that the device is substantially equivalent to existing, cleared devices.

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

Key Takeaway: The provided document is a 510(k) submission for a PACS system, which is essentially an integration of existing, cleared components. The "study" referenced is primarily an integration testing protocol to ensure connectivity and efficiency of these components when used together, rather than a clinical performance study measuring diagnostic accuracy. Therefore, many of the typical acceptance criteria and study details for an AI/CAD device (like sensitivity, specificity, reader performance improvements) are not applicable or not reported in this type of submission.

1. Table of Acceptance Criteria and Reported Device Performance

Given the nature of the device (a PACS system integrating existing components) and the provided document (a 510(k) summary focused on substantial equivalence), the acceptance criteria are centered on connectivity, compatibility, and functional integrity rather than diagnostic performance metrics like sensitivity or specificity.

Study Proving Device Meets Acceptance Criteria

The study described is an integration testing protocol.

• Study Type: Integration Testing / Verification and Validation (V&V) of a system composed of previously cleared devices.
• Purpose: To ensure the connectivity, compatibility, and efficient operation of various PACS components (workstations, teleradiology devices, archive, network system, printers) when assembled as the "iiSYS PACS System." The goal is to demonstrate that the integrated system functions as intended without negatively impacting the safety or efficacy of its individual cleared components.
• Methodology: The document states that the integration is accomplished via a "comprehensive testing protocol" and an "Integration Test Protocol." While specifics of the tests are not detailed, in general, such protocols would involve end-to-end testing of image transmission, storage, retrieval, display, and printing functionalities, along with checks for data integrity and system performance under various loads. Given the 1998 date, these would have likely been manual and automated functional tests.

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

• Sample Size for Test Set: Not explicitly stated as a number of patient cases or images. The "test set" for this type of system would likely involve a varied set of representative digital images and demographic data to test the system's core functionalities (transmission, archive, display, print) and interoperability standards (e.g., DICOM). The focus is on system functionality rather than diagnostic accuracy on a specific disease cohort.
• Data Provenance: Not specified. Given it's a PACS system for general use, the test data would likely be synthetic, anonymized clinical data, or existing institutional data used for system testing. The country of origin and retrospective/prospective nature are not mentioned, but it would typically be retrospective internal testing data.

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

• Number of Experts: Not applicable or not specified. For a PACS integration study, ground truth in the sense of diagnostic accuracy (e.g., presence/absence of disease) is not the primary focus. The "ground truth" would relate to whether the system correctly transmits, stores, retrieves, and displays images and data according to specifications. This is typically verified by engineers, IT professionals, and potentially radiologists acting as end-users validating system functionality, rather than establishing diagnostic ground truth on patient cases.
• Qualifications of Experts: Not specified.

4. Adjudication Method for the Test Set

• Adjudication Method: Not applicable or not specified. Adjudication (like 2+1, 3+1 for resolving diagnostic discrepancies) is used when establishing a diagnostic ground truth for patient cases. For a pure PACS integration test, the "judgement" is whether the system performs according to its functional specifications, which is typically a pass/fail outcome for each test case, often determined by a single tester or a testing team without a formal adjudication panel.

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: No. An MRMC study is designed to evaluate the diagnostic performance of a device, often a CAD or AI algorithm, and its impact on human reader performance. The iiSYS PACS System is an infrastructure device, not a diagnostic aid or AI algorithm. Its purpose is to manage images, not to interpret them or improve human interpretation accuracy.

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

• Standalone Study: No. This question applies to AI/CAD algorithms that provide a diagnostic output. The iiSYS PACS System does not have a standalone "algorithm" for diagnostic purposes. Its standalone performance relates to its functional operability (e.g., successful image transfer rate, storage capacity, display capabilities), which would have been part of the integration testing.

7. The Type of Ground Truth Used (expert consensus, pathology, outcomes data, etc.)

• Type of Ground Truth: For this PACS system, the "ground truth" is primarily based on functional specifications and expected system behavior. This means:
  • Data Integrity: Verifying that images and demographic information are transmitted, stored, and retrieved without corruption or loss.
  • Display Accuracy: Ensuring images are displayed correctly with appropriate resolution and tools.
  • Connectivity: Confirming that all integrated components can communicate and exchange data.
  • Performance: Demonstrating that the system responds within acceptable timeframes for various operations.
  • This is not "expert consensus, pathology, or outcomes data" in the diagnostic sense, but rather verification against engineering and functional requirements.

8. The Sample Size for the Training Set

• Sample Size for Training Set: Not applicable. For a PACS system that integrates existing components, there isn't a "training set" in the context of machine learning. The system's "knowledge" or functionality is engineered through software development and configuration, not learned from data in the AI sense.

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

• How Ground Truth for Training Set Was Established: Not applicable, as there is no "training set" for this type of device in the context of learning or AI.

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These radiographic intensifying screens have application whenever a high image quality radiographic examination such as mammography is required.

Ultra-Vision® Mammography Detail and Ultra-Vision® Mammography Fast Detail are ultraviolet activated rare earth intensifying screens. The phosphor is niobium activated lutetium tantalate.

The provided 510(k) summary for K974423 (Ultra-Vision® Mammography Detail and Ultra-Vision® Mammography Fast Detail) describes a comparison to a predicate device but does not contain a study or detailed acceptance criteria as one would expect for a modern AI/CAD device.

This submission is for radiographic intensifying screens, which are hardware components, not a software algorithm or AI device. Therefore, the questions related to AI/CAD performance (like MRMC studies, standalone performance, training sets, expert ground truth adjudication) are not applicable to this type of device.

Here's the information that can be extracted based on the provided document:

1. Table of Acceptance Criteria and Reported Device Performance:

The document doesn't explicitly state "acceptance criteria" but provides a comparison table against a predicate device, implying that performance similar to or better than the predicate is the basis for substantial equivalence. The "reported device performance" are the values for the new devices from this comparison.

Explanation: The "Acceptance Criteria (Implied by Predicate)" column represents the performance characteristics of the predicate device (Microvision™ Detail). The new devices are considered substantially equivalent because their performance in these metrics is comparable or improved. For example, the new devices show higher speed and potentially higher MTF and X-ray absorption, which are considered improvements for a mammography intensifying screen. The change in phosphor material is noted but acceptable if the performance metrics are met.

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

• Not applicable / Not provided. This submission is for hardware components (intensifying screens). Performance metrics like Speed, MTF, and X-ray Absorption are typically measured in a laboratory setting using standardized testing methods, not a "test set" of patient data in the context of AI. Data provenance (country of origin, retrospective/prospective) is not relevant for this type of 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 in the context of expert review is for diagnostic accuracy of an AI or human reader on patient images. This device is an intensifying screen, whose performance is measured through physical properties, not diagnostic interpretation.

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, If so, what was the effect size of how much human readers improve with AI vs without AI assistance:

• Not applicable. This is not an AI device.

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

• Not applicable. This is not an AI device. The measurements of Speed, MTF, and X-ray Absorption are inherent standalone performance characteristics of the screen itself.

7. The type of ground truth used:

• Laboratory measurements/Physical properties. The "ground truth" for this device's performance relies on rigorous physical measurements of parameters like spectral sensitivity, light output, spatial resolution (MTF), and x-ray absorption, typically conducted under controlled laboratory conditions using established standards for radiographic screen testing.

8. The sample size for the training set:

• Not applicable. This is not an AI device, so there is no "training set."

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

• Not applicable. See point 8.

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The Direct Radiography™ device provides a digital image capture capability for conventional radiographic examinations (excluding fluoroscopic, angiographic, and mammographic applications). The device has application wherever conventional screen-film systems are currently used.

The Sterling Diagnostic Imaging Direct RadiographyTM device is used to directly capture and convert conventional projection X-ray images to digital images. A sub-sampled image can be displayed on a preview monitor for viewing. The diagnostic image can be transmitted through a digital network for diagnostic viewing and printing. The device provides digital image capture for conventional radiographic examinations (excluding fluoroscopic, angiographic, and mammographic applications). The Direct RadiographyTM device differs from traditional X-ray systems in that instead of exposing a film for subsequent wet chemical processing to create a hardcopy image, a device called a digital array is used to capture the image in electronic form. The digital data are then used to produce hardcopy and softcopy images.

The provided document is a 510(k) summary for the "Sterling Diagnostic Imaging Direct Radiography™" device. This device is described as a system to directly capture and convert conventional projection X-ray images to digital images.

However, the document does not contain information regarding detailed acceptance criteria, specific device performance metrics, or any studies demonstrating that the device meets such criteria.

The document primarily focuses on:

• Device Description: What the device is and how it works (digital image capture instead of film).
• Intended Use: For conventional radiographic examinations (excluding fluoroscopic, angiographic, and mammographic applications) wherever screen-film systems are currently used.
• Predicate Device Comparison: It compares the digital nature of the Sterling device to the analog nature and chemical processing of traditional screen-film systems, concluding substantial equivalence for conventional radiographic examinations.
• FDA Clearance Letter: A letter from the FDA stating that the device is substantially equivalent to the predicate and can be marketed.

Since the document is a 510(k) summary, it aims to establish substantial equivalence to a predicate device rather than providing extensive performance studies with detailed acceptance criteria typically found in clinical trial reports or technical specifications for newer, novel devices. The determination of "substantially equivalent" implies that the new device is as safe and effective as a legally marketed predicate device, and thus, extensive de novo performance studies with separate acceptance criteria might not have been required or documented in this specific submission summary.

Therefore, I cannot provide the requested information from the given text.

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The Raven is a free standing device used to print diagnostic images on a polyester base for viewing on a standard view box and/or print reports and referral quality images on paper. It may be used in any situation in which a hard copy of an image generated by a medical imaging device is required or desirable.

The device accepts electrical image signals and produces hard copy images. The image signal source may be analog or digital formatted image data from image readers or unformatted image data from other imaging modalities (e.g. CT, MRI). The Sterling Raven Dry Imager uses the information in the image signals to control discrete elements in a print head which writes on the translating paper or Medical Imaging Film, a thermal recording media. The Raven has no laser, cathode ray tube, or optics. Like the predicate devices, the Raven does not use conventional light-sensitive silver halide photographic media, and thus requires no dark room, film processor, processing chemicals, water, drainage, or dryer ventilation. It produces no chemical waste, and requires no space for chemical storage.

The major elements are the film magazine, transport mechanics, imaging electronics, and head.

The provided text, a 510(k) submission for the Sterling Raven Dry Imager, focuses on demonstrating substantial equivalence to predicate devices rather than presenting a formal study with defined acceptance criteria and performance results in the manner typically seen for new diagnostic algorithms.

Here's an analysis based on the provided text, addressing your points where possible:

1. Table of Acceptance Criteria and Reported Device Performance:

The document doesn't explicitly state acceptance criteria in the form of quantitative performance metrics (e.g., sensitivity, specificity, accuracy, image quality scores) that a study would aim to meet for a new diagnostic device. Instead, the "acceptance criteria" are implicitly focused on the functional equivalence and safety compared to predicate devices. The reported "performance" is primarily a description of the device's capabilities and its similarity to existing, cleared devices.

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

• Sample Size: The document does not mention a specific "test set" or a sample size of medical images used for a performance study. This is expected given the nature of a 510(k) for an imager rather than a diagnostic algorithm. The assessment is based on the device's mechanical and output characteristics, and comparison to existing technology.
• Data Provenance: Not applicable, as no image-based test set is described.

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

• Number of Experts/Qualifications: Not applicable. There is no mention of experts establishing ground truth for a test set of images. The "ground truth" for this device likely revolves around objective physical properties of the printed image (e.g., density, resolution, uniformity) which would be assessed using measurement tools and visual inspection by engineers/quality control, rather than clinical experts.

4. Adjudication Method for the Test Set:

• Adjudication Method: Not applicable, as no test set requiring expert adjudication is described.

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

• MRMC Study: No. The text does not mention an MRMC study. The device is a "dry imager," meaning it generates physical prints. The focus is on the quality of these prints being comparable to those from predicate imagers, not on improving human reader performance with AI assistance. There is no AI component mentioned in the context of diagnostic interpretation.
• Effect Size: Not applicable.

6. Standalone (Algorithm Only) Performance Study:

• Standalone Study: No. The Sterling Raven Dry Imager is a hardware device for printing images, not a diagnostic algorithm. Its performance is intrinsically linked to the physical output. While it has a software component for interfacing and control, this software itself is stated to be "equivalent to the software used in the predicate devices," suggesting no novel algorithmic performance study was needed or performed.

7. Type of Ground Truth Used:

• Type of Ground Truth: For a device like an imager, the "ground truth" would be related to the physical characteristics of the printed output compared to the input digital image, and against the performance of predicate devices. This would likely involve:

  • Objective physical measurements: Densitometry (for gray-scale accuracy), spatial resolution tests (line pairs per mm), modulation transfer function (MTF), uniformity measurements.
  • Visual comparison: Comparing printed images from the Raven to those from predicate devices or reference prints for fidelity, contrast, and overall diagnostic quality when viewed by trained personnel.
  • Functional validation: Ensuring all interfaces work correctly and the device reliably prints without error.

  The document implies that these types of characteristics were assessed to deem the "performance... similar to that of the predicate."

8. Sample Size for the Training Set:

• Sample Size: Not applicable. The Sterling Raven Dry Imager is a printing device, not a machine learning model, so there is no "training set" in the context of AI development. The device's operation is based on established engineering principles and thermal printing technology.

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

• Ground Truth Establishment: Not applicable, as there is no training set.

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The Raven is a free standing device used to print diagnostic images for viewing on a standard view box. It may be used in any situation in which a hard copy of an image generated by a medical imaging device is required or desirable.

The device accepts electrical image signals and produces hard copy images. The image signal source may be digital formatted image data from image readers or unformatted image data from other imaging modalities (e.g. CT, MRI). The image signal source may be analog or digital. The Helios Laser Imager uses the information in the image signals digitally record diagnostic images and patient data on a proprietary product specific medical imaging media. The Helios does not use conventional light-sensitive silver halide photographic media, requires no dark room, film processor, processing chemicals, water, drainage, or dryer ventilation. It produces no chemical waste, and requires no space for chemical storage.

The provided text is a 510(k) premarket notification for a medical device called the "Helios Laser Imager." This document focuses on demonstrating substantial equivalence to a previously cleared device, not on proving device performance against specific acceptance criteria for an AI algorithm. Therefore, much of the requested information regarding AI device performance, sample sizes, ground truth establishment, expert adjudication, and MRMC studies is not available in this document.

Here's an analysis based on the information provided, highlighting the differences in context:

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

The document describes modifications to an existing device (Helios Laser printer, K912073) and asserts that these modifications do not change the technology or safety of the Helios printer and that the new version ("Helios Laser Imager 1417") is without question substantially equivalent to its predecessor and is safe and effective for its intended use.

The "performance" described relates to the system's ability to accept image signals and produce hard copy images, and improvements in throughput and cost efficiency. However, there are no specific, quantifiable acceptance criteria or reported device performance metrics in the way one would expect for an AI algorithm's diagnostic accuracy (e.g., sensitivity, specificity, AUC).

Instead, the closest to "criteria" are general statements about safety and equivalence.

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

This document describes a hardware device modification, not an AI algorithm. Therefore, there is no concept of a "test set" of patient data for evaluating an AI's performance. The "testing" referred to would be internal engineering verification and validation of the hardware and software functionality, not medical image analysis performance.

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

Not applicable. As there's no AI algorithm being evaluated for diagnostic accuracy, there's no "ground truth" to establish from medical images or expert consensus. The function of the device is to print images accurately, not to interpret them. The document mentions that "The output of the device is evaluated by additional trained professionals allowing sufficient review to afford identification and intervention in the event of a malfunction," but this is about quality control of the printed output, not diagnostic ground truth.

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

Not applicable for the reasons mentioned above.

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 an imager, a hardware component for displaying medical images, not an AI-powered diagnostic tool. An MRMC study is not relevant here.

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

Not applicable. This is not an AI algorithm.

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

Not applicable. There is no diagnostic ground truth established for this device, as its function is to print images, not to provide diagnostic interpretations.

8. The sample size for the training set:

Not applicable. This is a hardware device, not an AI algorithm requiring a training set of data.

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

Not applicable.

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This Premarket Notification 510(k) is being filed to cover additions to the LINX™ Networking System with LINX™ Laser Imager (K900935) to include all capabilities that we are adding to meet market demands. The basic product concept remains - to connect various image input devices to one or more output devices. That basic concept has been expanded to meet market demands: new image source interfaces new control interfaces new network interfaces more output devices supported new data types managed image transfer mechanisms (some output devices may require handling in the transmission path to the output device to compensate for differences in devices)

The LINX™ Networking System is a system to connect various image input devices to one or more output devices. Additions to the system include new image source interfaces, new control interfaces, new network interfaces, support for more output devices, management of new data types, and image transfer mechanisms.

I cannot fulfill your request to describe the acceptance criteria and study proving device performance based on the provided text. The document you've supplied is a "SUMMARY OF SAFETY AND EFFECTIVENESS 510(k)" for the "LINX™ Networking System." This document primarily focuses on demonstrating substantial equivalence to a predicate device by detailing expanded capabilities and updated features.

The text does not contain any information regarding:

• Acceptance criteria: There are no defined numerical thresholds or performance metrics.
• Study design or results: There is no mention of a formal clinical or technical study to evaluate the device's performance against specific criteria.
• Sample sizes, data provenance, ground truth establishment, expert qualifications, adjudication methods, or MRMC studies.

Instead, the document highlights expanded functionalities of the LINX™ Networking System compared to its predicate device, the LINX™ Networking System with LINX™ Laser Imager (K900935). This type of 510(k) submission generally relies on demonstrating that the new device does not raise new questions of safety and effectiveness, often by showing it performs as well as or better than the predicate device in terms of features and capabilities, rather than through a rigorous performance study with defined acceptance criteria.

Therefore, I cannot populate the table or answer the specific questions you've posed using the provided input.

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