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The Sectra IDS4 device is intended for the manipulation and displaying of x-ray images. It can show images from different modalities and interfaces to various image storage and printing devices using DICOM or similar interface standards.

Device options make possible telecommunications; fast demonstration; prosthesis CAD; 3-D and angiography, etc .; and teleconferencing.

Typical users of this system are trained professionals, including but not limited to physicians, radiologists, nurses, medical technicians, and assistants.

The IDS4 v3.2 Image Display System is mainly a software product. It is used for visualization and processing of digital radiology images. The system runs under both Window NT and HP-UX operating systems and still provide a common "look and feel" on both platforms.. The requirements on hardware are quite ordinary for a system used for displaying images. Most notably two or more monitors are used.

Here's an analysis of the provided 510(k) summary for the K991643 device, structured to answer your questions regarding acceptance criteria and the supporting study:

1. Table of Acceptance Criteria and Reported Device Performance

Based on the provided 510(k) summary, the acceptance criteria are primarily focused on safety, performance standards compliance, and substantial equivalence to a predicate device, rather than specific quantitative performance metrics for image interpretation (e.g., sensitivity, specificity). The reported device performance centers on its compliance with these standards and its functional equivalence.

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

The provided 510(k) summary does not mention a specific "test set" in the context of image analysis performance. The performance data presented refers to compliance with various technical and safety standards, not a clinical validation study with a defined dataset of patient images.

For this type of device (an image display system), the focus of the 510(k) is typically on demonstrating that the system accurately displays images and functions reliably according to its intended use and prevailing standards, rather than proving diagnostic accuracy.

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

This information is not applicable and not provided in the 510(k) summary. Since there's no mention of a clinical test set requiring expert interpretation for ground truth, there's no information about experts or their qualifications.

4. Adjudication Method for the Test Set

This information is not applicable and not provided in the 510(k) summary for the same reasons as point 3.

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 mentioned or indicated in the provided 510(k) summary. This type of study is more common for AI/CADe devices that directly aid in diagnosis or detection, rather than a general image display system.

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

No, a standalone performance study was not mentioned. The device, an "Image Display System," is inherently a tool for human review and interpretation. The summary explicitly states: "Images and information being reviewed, processed, relayed, and or transmitted are interpreted by a physician or trained medical personnel, providing ample opportunity for competent human intervention."

7. The Type of Ground Truth Used

The concept of "ground truth" as typically applied to diagnostic algorithms (expert consensus, pathology, outcomes data) is not applicable to the data provided in this 510(k) summary. The ground truth, in the context of this device, would be the accuracy and fidelity of image display and system functionality which is assessed through compliance with technical standards and internal testing.

8. The Sample Size for the Training Set

This information is not applicable and not provided. The device described is an "Image Display System" (software for visualization and processing) and does not inherently involve machine learning models that require a "training set" in the typical AI sense.

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

This information is not applicable and not provided for the same reasons as point 8.

Summary of Findings:

The K991643 510(k) summary for the "IDS4 v3.2 Image Display System" primarily focuses on demonstrating substantial equivalence to a predicate device (K971368) by showing compliance with relevant safety, technical, and communication standards. It explicitly states that human users (physicians, radiologists, etc.) are responsible for image interpretation. Therefore, the document does not contain information related to clinical performance studies, AI algorithms, training/test sets, expert ground truth establishment, or human-AI comparative effectiveness, as these types of studies are typically required for devices that provide diagnostic interpretations or automated analysis rather than merely displaying images.

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The Olicon Imaging Systems, NT Archive Systems stores and retrieves digital images together with information about the DICOM standard network protocol. communicates with other devices via the DICOM standard network protocol.

The NT ARCHIVE SYSTEM is a device for filing digital radiological images for storage and retrieval. The system design is layered with three storage technologies; magnetic, magneto-optical and DLT tape.

This document is a 510(k) summary for the Olicon Imaging Systems, Inc. NT Archive Systems. It describes a digital archive system for medical images.

Based on the provided text, the device described is a digital archive system for medical images, not a medical device that diagnoses or treats conditions. Therefore, concepts like "acceptance criteria" related to diagnostic accuracy, "device performance" in terms of clinical outcomes, "sample sizes" for test sets of patients, "ground truth" derived from expert consensus or pathology, or "multi-reader multi-case (MRMC) comparative effectiveness studies" are not applicable to this submission.

The document focuses on demonstrating substantial equivalence to a predicate device (Olicon Archive K922164) based on its technological characteristics, indications for use, and compliance with general regulatory standards.

Here's a breakdown of the requested information based on the provided text, indicating where the information is not applicable (N/A) for this type of device:

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)

• N/A. This device does not involve a "test set" in the sense of clinical data or patient images for performance evaluation of a diagnostic algorithm. It's an archiving system. The "test" would be functional testing of the software and hardware for storage and retrieval, which is not detailed in this summary.

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)

• N/A. Ground truth for clinical interpretation is not applicable as this is an archiving system, not an interpretive device.

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

• N/A. Adjudication methods are not applicable as there is no "test set" for clinical evaluation.

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

• N/A. MRMC studies are not applicable as this is an archiving system, not a diagnostic AI system intended to assist human readers.

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

• N/A. The concept of "standalone performance" for an algorithm for clinical interpretation is not applicable here. The device itself is "standalone" in its primary function of archiving, but its performance is measured by its functional capabilities (storage, retrieval, DICOM compatibility) rather than diagnostic accuracy. The document states: "Images and information being stored and retrieved are interpreted by a physician, providing ample opportunity for competent human intervention," indicating the device's role is to support, not replace, human interpretation.

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

• N/A. No clinical ground truth is applicable. The "ground truth" for this device would be whether it accurately stores and retrieves the digital images as intended.

8. The sample size for the training set

• N/A. There is no "training set" in the context of machine learning or AI as this device is an archive system.

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

• N/A. Not applicable, as there is no training set mentioned or implied for this type of device.

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The SECTRA MS4 device is intended for the manipulation and displaying of x-ray images. It can show images from different modalities and interfaces to various image storage and printing devices using DICOM or similar interface standards. Device options make possible telecommunications; fast demonstration; prosthesis CAD; 3-D and angiography, etc .; and teleconferencing.

The IDS4 series will be used for displaying and handling of radiological images.

This document is a 510(k) summary for the SECTRA-Imtec IDS4 Image Display System series, which was reviewed by the FDA in 1997. It declares substantial equivalence to a predicate device (SECTRA-Imtec TRS 2000, K961983) and outlines the system's intended use for displaying and handling radiological images by trained medical professionals.

Here's an analysis of the provided text with respect to your questions:

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

The provided text does not contain a table of specific numerical acceptance criteria or reported device performance metrics for the IDS4 Image Display System. The 510(k) summary focuses on demonstrating substantial equivalence based on compliance with regulations, voluntary standards, user guides, and hazard analysis, rather than quantitative performance data.

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

The document does not specify a sample size or data provenance for any test set. The claim of substantial equivalence is based on regulatory compliance and comparison to a predicate device, not on clinical performance testing with a specific dataset.

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

The document does not mention a test set with corresponding ground truth established by experts.

4. Adjudication method for the test set

The document does not describe any adjudication method for a test set.

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 mention any MRMC comparative effectiveness study or AI assistance. The device is an "Image Display System," suggesting its primary function is display and handling of images, not AI-driven analysis or interpretation.

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

The document does not describe any standalone algorithm performance testing. The device is an "Image Display System" intended for use by "trained medical professionals," implying human-in-the-loop operation.

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

The document does not mention the use of any specific type of ground truth data.

8. The sample size for the training set

The document does not mention any training set or its sample size.

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

The document does not mention any training set or how its ground truth was established.

Summary of what is present in the document:

The provided text is a 510(k) summary submitted to the FDA for the IDS4 Image Display System. It asserts substantial equivalence to a predicate device (SECTRA-Imtec TRS 2000, K961983) by stating:

• Compliance with Federal Performance Standards (21 CFR, part 1000).
• Manufacturing in accordance with voluntary standards.
• Comprehensive information in user guides for safe and effective use.
• Results of a hazard analysis are included.

The FDA's response confirms the device is substantially equivalent based on the provided information and allows marketing of the device. The indications for use are for the manipulation and displaying of x-ray images from different modalities, with options for telecommunications, fast demonstration, prosthesis CAD, 3-D and angiography, etc., and teleconferencing. The typical users are trained medical professionals.

In conclusion, the document is a regulatory submission for pre-market notification (510(k)) and focuses on demonstrating substantial equivalence through compliance with regulations and standards rather than presenting detailed clinical performance studies, acceptance criteria tables, or data provenance. Therefore, most of your specific questions cannot be answered from the provided text.

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The SECTRA AB, WISE Image Management System device 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.

WISE will be used for management of radiological images.

This document is a 510(k) summary for the SECTRA-Imtec WISE Image Management System, submitted in 1997. It explicitly states that the submission aims to demonstrate "substantial equivalence" of WISE to a predicate device, SECTRA-Imtec ImageServer 2000 (K963395).

The submission does not contain specific acceptance criteria, performance metrics, or study details such as sample sizes, ground truth establishment, or human reader studies typically associated with demonstrating clinical efficacy or diagnostic accuracy. Instead, it focuses on regulatory compliance and equivalence to a previously cleared device.

Therefore, many of the requested details cannot be extracted from the provided text.

Here is an attempt to address the points based on the available information, noting where information is absent:

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

   This information is not provided in the given text. The filing is for demonstrating substantial equivalence, not for presenting performance against specific statistical acceptance criteria for diagnostic accuracy (e.g., sensitivity, specificity, AUC).

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. The document does not describe a test set or clinical study of this nature.

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. There is no mention of a test set or ground truth establishment process.

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

   This information is not provided.

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

   This information is not provided. The device is an "Image Management System," suggesting its primary function is handling and displaying images, not providing diagnostic AI assistance. Therefore, an MRMC study comparing human readers with and without AI assistance would not be relevant or expected for this type of device.

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

   This information is not provided. The WISE system is described as an "Image Management System" for displaying and managing images for "trained medical professionals," implying a human-in-the-loop system for image review and interpretation. It is not presented as an AI algorithm providing standalone diagnostic outputs.

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

   This information is not provided.

8. The sample size for the training set

   This information is not provided. The document does not describe any machine learning or AI models requiring a training set.

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

   This information is not provided.

Summary of available information related to acceptance criteria and study:

The 510(k) summary focuses on demonstrating substantial equivalence to a predicate device (SECTRA-Imtec ImageServer 2000, K963395) rather than providing specific performance metrics from a clinical study. The basis for substantial equivalence is listed as:

• Compliance with Federal Performance Standards (21 CFR, part 1000).
• Manufacture in accordance with voluntary standards.
• Comprehensive user guides ensuring safe and effective use.
• Results of a hazard analysis.

The FDA's response letter (JUN 26 1997) confirms that they reviewed the 510(k) and "decemined the device is substantially equivalent... to devices marketed in interstate commerce prior to May 28, 1976." This indicates that the device met the regulatory bar for substantial equivalence at the time, which did not necessarily require the same type of clinical performance studies expected for newer AI/ML diagnostic devices.

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