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The VisiShare Imaging System and/or Software is a digital imaging viewing system that receives digital images and patient demographic information from various imaging sources. The incoming data formats are standard medical image formats, proprietary image formats, and common non-medical image formats. The VisiShare allows the operator to manage, view, retrieve and store images, modify image parameters and annotate image for the purposes of radiological interpretation.

The VisiShare Imaging System is designed to accept images from various modalities or digital image networks and allow the operator to view, retrieve, store and manipulate medical images in a variety of formats.

The VisiShare is a modular system that uses network and proprietary interfaces for communication between the modules. Network interfaces enable the various modules to reside on local or remote machines. System is designed for low-end and high-end systems, supporting single or multiple monitor configurations.

The provided text (K974130) is a 510(k) summary for the VisiShare Imaging System and/or Software. This document focuses on demonstrating substantial equivalence to predicate devices, rather than presenting a performance study with specific acceptance criteria and detailed study results.

Therefore, many of the requested details regarding acceptance criteria, study design, sample sizes, expert qualifications, and ground truth establishment are not provided in this regulatory submission.

Here's a breakdown of the information that can be extracted or inferred, and what is missing:

Description of Acceptance Criteria and Study to Prove Device Meets Acceptance Criteria

The provided 510(k) summary for the VisiShare Imaging System and/or Software does not detail specific acceptance criteria or a dedicated study designed to prove the device meets these criteria in the way one might expect for a diagnostic or AI-powered device. Instead, the submission focuses on demonstrating substantial equivalence to predicate devices. This means the primary "acceptance criterion" from a regulatory perspective is that the new device is as safe and effective as existing legally marketed devices, and any differences do not raise new questions of safety or effectiveness.

The document implicitly "proves" this equivalence through a comparison of design, composition, function, intended use, safety, and efficacy with the predicate devices. The listed predicate devices are:

• Magic View 50 Teleradiology System [Siemens]
• RadWorks Medical Imaging Software [Applicare Medical Imaging, B.V.]
• AutoRad [Cemax-Icon]

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

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

Not provided. The 510(k) summary does not mention a specific "test set" of medical images or patient data used for a performance study. Its focus is on substantial equivalence based on design and intended use, not on a clinical performance evaluation using a dataset.

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. Since no specific "test set" for performance evaluation is described, there's no mention of experts or ground truth establishment in this context.

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

Not applicable/Not provided. No explicit test set or adjudication method 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. This device is an imaging viewing system, not an AI-powered diagnostic tool. Therefore, an MRMC study comparing human readers with and without AI assistance is not relevant to this submission and was not performed.

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

Not applicable. This device is an interactive viewing system with a human operator in the loop for radiological interpretation. It does not perform autonomous algorithmic diagnoses.

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

Not applicable/Not provided. As there is no specified performance study with a test set, the concept of ground truth is not elaborated upon in this document. The "ground truth" for showing substantial equivalence relies on the known safety and effectiveness of the legally marketed predicate devices.

8. The sample size for the training set

Not applicable/Not provided. As this is not an AI/machine learning device that requires training data, there is no mention of a training set.

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

Not applicable/Not provided. Corresponding to point 8, this information is not relevant or provided.

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The Imageshare Computed Radiography Acquisition Station and/or Software is a bi-directional gateway that sends or receives digital images to/from various image sources (including, but not limited to, Fuji CR, Fuji HI-C654, Analogic Corporation's DASM, Computed Radiography or Direct Radiography devices). The incoming data formats may be raw CR data, processed CR data or data formats proprietary to the modality source vendor. The Imageshare Computed Radiography Acquisition Station and/or Software converts the data to DICOM, ACR-NEMA v2.0, SPI format or other proprietary data format and transmits the data to one or more user-specified nodes across a standard, general purpose computing network.

The Imageshare Computed Radiography Acquisition and/or Software is designed to operate on a general purpose computer system running an image acquisition software application to receive, reformat and transmit image and demographic information. The system receives the image messages from a Computed Radiography or other image source and routes them automatically through a conversion to a destination based on information contained in the message source, encoded data or data entered or configured by an operator through a user interface. The Imageshare Computed Radiography Acquisition stores the data on its local hard disk until the destination acknowledges the successful transmission. Image delivery is also guaranteed when the Imageshare Computed Radiography Acquisition Station loses power and is restarted.

The provided text does not contain information about acceptance criteria or a study proving the device meets them. The document is a 510(k) summary for the "Imageshare Computed Radiography Acquisition Station and/or Software." It describes the device's function, intended use, and states its substantial equivalence to a predicate device.

Specifically, the document states:

• No acceptance criteria or device performance table: This information is completely absent from the provided text.
• No study details: There is no mention of any study conducted to assess the device's performance, meeting of acceptance criteria, or any of the other requested details (sample sizes, data provenance, expert ground truth, adjudication, MRMC, standalone performance, training set details).
• No ground truth type: Because no study is described, there's no mention of the type of ground truth used.

The focus of this 510(k) summary is on establishing substantial equivalence to a previously marketed device based on design, composition, function, intended use, safety, and efficacy, rather than presenting a detailed performance study with acceptance criteria.

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The Lasershare Print Spooler and/or Software is a distributable filming management system that receives digital images and format control data from various image sources (including, but not limited to, CT scanners, MR scanners, Ultrasound systems, R/F units, Computed and Direct Radiography devices, secondary capture devices, imaging gateways, removable storage media or other imaging sources) connected point to point or via networks. The incoming data formats are standard medical imaging formats or proprietary to the modality source vendor. The Lasershare Print Spooler and/or Software allows the operator to manage print job requests, create filming sessions and route image and patient data to one or more laser film printers, thermal dye sublimation and dry printers and other medical imaging print devices.

The Lasershare Print Spooler is designed to accept images from various modalities or digital image networks and direct images to one or more medical imaging print devices, providing spooling and queue management facilities. The Lasershare Print Spooler and/or Software is a different model of the current Lasershare Print Spooler and/or Software [predicate device], ported to another operating system. The Lasershare is a modular system that uses network interfaces for communication between the modules. Network interfaces enable the various modules to reside on local or remote machines.

This submission [K973422](https://510k.innolitics.com/search/K973422) is for a "Lasershare Print Spooler and/or Software", which is classified as an "accessory" to a "System, Digital Image Communication". This device is a print spooler that manages and routes digital medical images to various printing devices.

Based on the provided document, the device's main function is to handle and route images for printing. This type of device is very different from diagnostic AI/ML software, and therefore, an acceptance criteria table related to diagnostic performance, sample sizes for test sets, expert involvement, and ground truth establishment, which are typical for AI/ML device studies, are not applicable to this submission.

This is primarily a software utility focused on interoperability and management of imaging data for printing purposes, not diagnostic accuracy or interpretation. The performance characteristics described are related to software portability, network load, and hardware compatibility rather than clinical efficacy in diagnosis.

The submission focuses heavily on substantial equivalence to an existing predicate device, highlighting that any differences between the new device and the predicate have no significant influence on safety or efficacy related to its core function as a print spooler.

Therefore, many of the requested items (1, 2, 3, 4, 5, 6, 7, 8, 9) related to clinical performance studies, such as diagnostic accuracy, expert review, and ground truth, are not present in this 510(k) summary because they are not relevant to the function or regulatory classification of a print spooler as a device accessory.

The device's "performance" in this context refers to its ability to manage print jobs, route images, and operate across different systems, as described in section 4.2: "Performance of the application software is primarily a function of network load; secondarily a function of the hardware platform's computational speed and finally the performance of the printer(s). Intrinsic performance of the application does not change significantly as it is ported from one operating system to another."

The study proving the device meets acceptance criteria, in this case, would have been a demonstration of this software's ability to perform its defined printing and routing functions, likely through internal validation and comparisons to the predicate device in terms of functionality and system compatibility, rather than a clinical trial.

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The Lasershare Print Spooler and/or Software is a distributable filming management system that receives digital images and format control data from various image sources (including, but not limited to, CT scanners, MR scanners, Ultrasound systems, R/F units, Computed and Direct Radiography devices, secondary capture devices, imaging gateways, removable storage media or other imaging sources) connected point to point or via networks. The incoming data formats are standard medical imaging formats or proprietary to the modality source vendor. The Lasershare Print Spooler and/or Software allows the operator to manage print job requests, create filming sessions and route image and patient data to one or more laser film printers, thermal dye sublimation and dry printers and other medical imaging print devices.

The Lasershare Print Spooler is designed to accept images from various modalities or digital image networks and direct images to one or more medical imaging print devices, providing spooling and queue management facilities. Core technology for the Lasershare Print Spooler is based on, and various software components ported from, the Imageshare 910 Protocol Converter (a predicate device). The Lasershare is a modular system that uses network interfaces for communication between the modules. Network interfaces enable the various modules to reside on local or remote machines. The Lasershare Print Spooler and/or Software is designed to run on off-the-shelf, general purpose computing equipment. The application software is designed for maximum portability across operating systems and hardware platforms. Performance of the application software is primarily a function of network load; secondarily a function of the hardware platform's computational speed and finally the performance of the printer(s). Intrinsic performance of the application does not change significantly as it is ported from one operating system to another.

This document is a 510(k) summary for the Lasershare Print Spooler and/or Software. It is a premarket notification for a medical device that aims to demonstrate its substantial equivalence to other legally marketed devices.

Based on the provided text, the device is not an AI/ML diagnostic tool but rather a digital imaging network spooler. Its function is to manage and route digital medical images to various printing devices.

Therefore, the requested information regarding acceptance criteria, study details, sample sizes, expert ground truth, MRMC studies, standalone performance, and training set details are not applicable to this type of device. The provided text focuses on device description, intended use, and substantial equivalence to predicate devices, which is standard for a 510(k) submission for this class of device.

There is no performance data or clinical study presented as would be required for a diagnostic or AI-powered device with performance metrics like sensitivity, specificity, etc. The "performance characteristics" mentioned in section 4.2 refer to technical aspects like network load and computational speed, not diagnostic accuracy.

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The Imageshare Protocol Converting Gateway and/or Software is a uni-directional (or optionally, bi-directional) gateway that receives digital images from various image sources (including, but not limited to, CT scanners, MR scanners, Ultrasound systems, R/F units, Computed and Direct Radiography devices, secondary capture devices, imaging gateways, or other imaging sources), The incoming data formats are DICOM, ACR-NEMA v2.0, SPI (Standard Product Interconnect) or proprietary to the modality source vendor. The Imageshare Protocol Converting Gateway converts them into DICOM, ACR-NEMA v2.0, SPI format or proprietary data format and transmits the data to one or more user-specified nodes across a standard, general purpose computing network.

The Imageshare Protocol Converting Gateway is a general purpose computer system running a protocol conversion software application that uses defined configuration to receive, reformat and transmit image and demographic information. The system receives the image messages from a source and routes them automatically them through a conversion to a remote destination based on information contained in both the message source and encoded data. The system requires no user interaction when in operation. The Imageshare Protocol Converting Gateway stores the image data on its local hard disk until the destination application acknowledges the successful transmission. Images that are not delivered to their destination are queued for retransmission until the remote destination confirms receiving the message. This is also true when the Imageshare Protocol Converting Gateway loses power and is restarted.

The Imageshare Protocol Converting Gateway is adaptable to various PACS environments. The configuration can be modified by the system administrator to adapt the Imageshare Protocol Converting Gateway to perform the desired supported conversions.

Here's an analysis of the provided text regarding acceptance criteria and study information:

Based on the provided Device Summary (K963592), it is not possible to complete most sections of your request. This document is a 510(k) summary for a "Protocol Converting Gateway and/or Software" which is a system for converting and transmitting digital images, not a diagnostic or treatment device with performance metrics related to patient outcomes or disease detection.

The document focuses on its technical function of converting digital image protocols (e.g., DICOM, ACR-NEMA) and its substantial equivalence to predicate devices for this specific technical purpose. It does not describe any clinical studies, performance metrics against ground truth, or human reader effectiveness because its function is a technical data conversion and transmission, not a clinical interpretation or diagnostic aid.

Here's what can be extracted and how it relates to your requested information:

1. Table of Acceptance Criteria and Reported Device Performance

It is not possible to generate this table from the provided text.

The document describes the device's technical function, such as:

• Receiving, reformatting, and transmitting image and demographic information.
• Converting between DICOM, ACR-NEMA v2.0, SPI, or proprietary data formats.
• Storing data locally until successful transmission is acknowledged.
• Queuing undelivered images for retransmission.
• Adaptability to various PACS environments via configuration.
• Designed for "maximum portability across operating systems and hardware platforms."
• Performance "primarily a function of network load; secondarily a function of the hardware platform's computational speed."

These are functional and technical specifications, not acceptance criteria with measurable performance metrics in a clinical context (e.g., sensitivity, specificity, accuracy). The "performance" described is about technical throughput and reliability of data transfer, not clinical efficacy.

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

Not applicable and not provided. There is no mention of a "test set" in the context of clinical data because this device is not making clinical interpretations. The "testing" would have been for its technical ability to convert and transmit data, likely using synthetic or existing image files, but this is not detailed.

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

Not applicable and not provided. The device does not produce an output that requires expert ground truth. Its function is data conversion and transmission.

4. Adjudication Method for the Test Set

Not applicable and not provided. As there's no clinical test set requiring ground truth, there's no adjudication method.

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

No, a MRMC study was not done. This type of study is relevant for devices that assist human readers in making clinical decisions (e.g., CAD systems). This gateway device does not provide any diagnostic or interpretative assistance.

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

Not applicable in the sense of clinical performance. While the device operates "without user interaction when in operation," this refers to its autonomous data conversion and transmission, not standalone performance in a clinical diagnostic context (e.g., an AI algorithm detecting a disease without human input). The entire device functions as a "standalone" technical system.

7. The Type of Ground Truth Used

Not applicable. There is no "ground truth" in the clinical sense (e.g., expert consensus, pathology, outcomes data) as the device's function is technical data handling. The "ground truth" for this device would be whether it accurately converted and transmitted data without corruption, which is a technical validation, not clinical.

8. The Sample Size for the Training Set

Not applicable and not provided. This device is a software application for protocol conversion. It does not utilize machine learning or AI that requires a "training set" of data in the typical sense for learning patterns or making inferences. Its operations are rule-based and configurable.

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

Not applicable and not provided. As there's no training set for machine learning, there's no ground truth established for it.

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The Imageshare Computed Radiography Acquisition Station and/or Software is a uni-directional gateway that receives digital images from various image sources (including, but not limited to, Fuji CR, Fuji HI-C654, Analogic Corporation's DASM, Computed Radiography or Direct Radiography devices). The incoming data formats may be raw CR data, processed CR data or data formats proprietary to the modality source vendor. The Imageshare Computed Radiography Acquisition Station and/or Software converts the data to DICOM, ACR-NEMA v2.0, SPI format or other proprietary data format and transmits the data to one or more user-specified nodes across a standard, general purpose computing network.

The Imageshare Computed Radiography Acquisition Station and/or Software is designed 1 operate on a general purpose computer system running an image acquisition software application to receive, reformat and transmit image and demographic information. The system receives the image messages from a Computed Radiography or other image source and routes them automatically through a conversion to a destination based on information contained in the message source, encoded data or data entered by an operator through a user interface. The Imageshare Computed Radiography Acquisition Station stores the data on its local-fard disk until the destination application acknowledges the successful transmission. Image delivery is also guaranteed when the Imageshare Computed Radiography Acquisition Station loses power and is restarted.

This 510(k) summary does not contain the information needed to answer your questions regarding acceptance criteria and study details. The document describes the "Imageshare Computed Radiography Acquisition Station and/or Software" as a gateway for digital images, converting formats and transmitting data. It focuses on the device's function, intended use, and claims substantial equivalence to previously marketed devices.

Specifically, the document does not include:

1. Acceptance criteria or device performance data.
2. Details of any study (sample size, data provenance, ground truth establishment, expert qualifications, adjudication, MRMC, or standalone performance).
3. Training set information.

The document is purely descriptive of the device's functionality and its claim of substantial equivalence to predicate devices, without presenting any pre-market clinical or performance evaluations in the way you've described.

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The Imageshare Secondary Capture Acquisition Station and/or Software is a uni-directional gateway that receives digital or analog images from various image sources (including, but not limited to, film digitizers, video signal outputs or print output from imaging modalities, or other imaging sources). The incoming data formats are bitmapped, raster images or proprietary to the modality source vendor. The Imageshare Secondary Capture Acquisition Station and/or Software converts the data to DICOM, ACR-NEMA v2.0, SPI format or other proprietary data format and transmits the data to one or more user-specified nodes across a standard, general purpose computing network.

The Imageshare Secondary Capture Acquisition Station is a general purpose computer system running an image acquisition software application to receive, reformat and transmit image and demographic information. The system receives the image messages from a source (film digitizer, video source, ultrasound, print output, etc.) and routes them automatically through a conversion to a destination based on information contained in the message source, encoded data and data entered by an operator through a user interface. The Imageshare Secondary Capture Acquisition Station stores the data on its local hard disk until the destination application acknowledges the successful transmission. Images not delivered to their destination are queved for retransmission until the remote destination confirms receipt of the message. Image delivery is also guaranteed when the Imageshare Secondary Capture Acquisition Station loses power and is restarted.

The Imageshare Secondary Capture Acquisition Station is adaptable to various PACS environments. The configuration can be modified by the system administrator to adapt the Imageshare Secondary Capture Acquisition Station to perform functions specific to the site.

Here's an analysis of the provided text regarding acceptance criteria and supporting studies:

Based on the provided 510(k) summary (K963594), there is no information provided about acceptance criteria or a study proving the device meets specific performance metrics.

The document primarily focuses on:

• Device Identification: Trade name, common name, classification name.
• Predicate Devices: A list of similar devices already on the market.
• Device Description: Functionality (receiving, reformatting, transmitting image and demographic information, handling retransmission, adaptability to PACS).
• Physical and Performance Characteristics: General statements about running on off-the-shelf equipment, portability, performance primarily affected by network load.
• Intended Use: As a uni-directional gateway to convert and transmit digital or analog images in various formats.
• Statement of Substantial Equivalence: Claiming substantial equivalence to predicate devices without significant influence on safety or efficacy.

Therefore, it's not possible to populate the requested table or answer the specific questions about acceptance criteria and studies. The summary is typical for a 510(k) submission seeking equivalence to existing devices, where detailed performance studies might not be explicitly required if substantial equivalence can be demonstrated through design, function, and intended use.

If this were a more modern AI/ML device submission, a 510(k) would typically include detailed performance data, acceptance criteria, and study methodologies. However, this document from 1996 for an "Imageshare Secondary Capture Acquisition Station and/or Software" is focused on general image handling and transmission, not specific diagnostic AI performance.

In summary, for the information requested:

1. A table of acceptance criteria and the reported device performance: Not provided in the document.
2. Sample size used for the test set and the data provenance: Not provided in the document.
3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts: Not provided in the document.
4. Adjudication method for the test set: Not provided in the document.
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 provided in the document. (This type of study is typically for diagnostic AI, which this device is not presented as).
6. If a standalone (i.e., algorithm only without human-in-the-loop performance) was done: Not provided in the document.
7. The type of ground truth used: Not applicable/Provided in the document. (The device is for image capture, conversion, and transmission, not for making diagnostic interpretations requiring ground truth).
8. The sample size for the training set: Not provided in the document.
9. How the ground truth for the training set was established: Not applicable/Provided in the document.

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