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ProVision is an independent diagnostic viewing and processing workstation. It is based on a Silicon Graphics Workstation running under Unix. ProVision communicates with imaging systems of different modalities (currently CT, MRI, CR, RF, NM) utilizing the DICOM - 3 standard. Connection may also be made to any other DICOM device. ProVision functions include :Archiving, displaying, manipulation, filming, 2and 3-dimensional processing. ProVision employs a graphical multi -Window, icon and mouse driven user interface. It is designed to ensure maximum flexibility on the one hand, and intuitive operation on the other.

The ProVision is a multi-modality diagnostic Workstation for processing and archiving radiological images. It is based on off-the shelf Silicon Graphics UNIX based computers that comply with the accepted international standards for computer systems. The systems also comprises software developed and validated by Algotec Systems Ltd.

The provided text does not contain detailed information about specific acceptance criteria or an analytical study proving the device meets those criteria. The document is a 510(k) summary for a software update to a diagnostic workstation (ProVision) and focuses on demonstrating substantial equivalence to a predicate device.

However, based on what is provided, here's a breakdown of the requested information, acknowledging the limitations of the input text:

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

The document states: "The new updates simply provide quicker methods to reach results that would have been time consuming on the previous version of Pro Vision software. The addition of these software updates raises no new issues of safety or effectiveness."

This implies that the key performance criteria for the update are related to efficiency/speed and maintaining safety and effectiveness at least equivalent to the predicate device. However, no quantitative acceptance criteria or specific performance metrics are reported in this document.

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

This information is not provided in the given text.

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)

This information is not provided in the given text. The document refers to the system's intended use "by radiologists as an interactive tool for analyzing radiological data," but doesn't detail any expert involvement in a study.

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

This information is not provided in the given text.

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

There is no indication that a multi-reader multi-case (MRMC) comparative effectiveness study was done. The document focuses on software updates for an existing diagnostic workstation, emphasizing efficiency improvements and substantial equivalence, not the comparative performance of human readers with vs. without AI assistance. The device is described as "an interactive tool for analyzing radiological data," implying human-in-the-loop, but no comparative study is mentioned.

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

The device, ProVision, is described as an "independent diagnostic viewing and processing workstation" and an "interactive tool for analyzing radiological data." This inherently implies a human-in-the-loop system. Therefore, a standalone (algorithm only) performance study would likely not be relevant or described for this type of device, and there is no mention of one.

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

This information is not provided in the given text.

8. The sample size for the training set

This information is not provided in the given text.

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

This information is not provided in the given text.

In summary: The provided document is a 510(k) summary focused on demonstrating substantial equivalence for software updates to an existing diagnostic workstation. It asserts that the updates improve efficiency and do not introduce new safety or effectiveness issues. However, it lacks detailed information regarding specific study designs, acceptance criteria, sample sizes, expert involvement, or ground truth establishment that would typically be found in a comprehensive performance study report. The claims are high-level and refer to the inherent nature of the updates within the existing system.

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MediSurf is intended to facilitate access to clinical images and information in distributed locations. Along with image processing capabilities, MediSurf can be used by radiologists for on-call teleradiology over "intranets", Internet, direct or dial-up lines, and for providing second opinion services. It can also be used by physicians for enhanced interpretation of cases through the inclusion of images in imaging related reports and in patient records in general. Similarly it can be used by healthcare organization staff for facilitated access to clinical images throughout the healthcare organization.

The MediSurf server is an access engine to healthcare information. MediSurf provides its end-users with easy access to DICOM devices and networks for the retrieval of images in full diagnostic quality using adaptive progressive compression for efficient transfer over different communication links. The server also provides the user with the software applets needed to display, process, store, and send the retrieved clinical data.

The MediSurf server is based on off-the-shelf UNIX based computer that complies with the accepted international standards for computer systems. The system also comprises software developed and validated by Algotec Systems Ltd..

The provided text is a 510(k) summary for the MediSurf device, which is a teleradiology system. It explicitly states that the device is substantially equivalent to a predicate device (ProVision Diagnostic Workstation) and does not introduce new features affecting safety or effectiveness. As such, the document does not contain information about acceptance criteria or a study proving the device meets specific performance metrics in the way a medical AI/CADe device would.

The submission focuses on the substantial equivalence argument based on the device's technological characteristics and intended use being similar to the predicate device. It highlights that the core functionalities (image viewing, processing, archiving, communication) are largely shared, with the primary difference being the user interface (web browser vs. dedicated workstation interface).

Therefore, I cannot populate the requested table and answer the specific questions about performance studies, sample sizes, expert ground truth, adjudication, or MRMC studies, as this information is not present in the provided 510(k) summary. These types of studies and details are typically required for different classes of medical devices or for AI/CADe devices demonstrating clinical performance, which MediSurf, as described here, is not.

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The SeleCT/SP is a cost effective spiral scanner, with compact siting requirements and real-time image processing functions.

The provided text describes a CT scanner (SeleCT/SP) and establishes its substantial equivalence to a predicate device (HeliCAT, K930090) based on safety and effectiveness. However, it does not contain specific acceptance criteria, performance metrics, or details of a study as requested in your prompt.

Therefore, I cannot fill in the table or provide information for most of your numbered points. The document focuses on regulatory equivalence rather than a detailed performance study against specific acceptance criteria.

Here's what I can extract and what is missing:

1. 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 document does not describe any specific test set or data. It relies on a comparison to predicate devices.

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. No test set or ground truth establishment process is described.

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

• Not applicable. No 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

• No. This document is for a CT scanner, not an AI-assisted device. No MRMC study is mentioned.

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

• No. This is for a CT scanner.

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

• Not applicable. No test set or ground truth establishment process is described. The "ground truth" for this submission appears to be regulatory compliance and comparison to existing, approved devices.

8. The sample size for the training set

• Not applicable. This document describes a medical imaging device, not a machine learning model that would require a training set.

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

• Not applicable. As above, no training set is relevant to this submission.

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