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The Sectra IDS5 device is intended for the manipulation and displaying of x-ray images, including mammograms. 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 mammography reading, 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 Sectra IDS5 Radiology Workstation with Sectra 3D and MPR Packages is mainly a software product. It is used for visualization and processing of digital radiology images. The IDS5 is used as a client together with a Sectra provided server (Class 1 Exempt). The system runs on PCs under the Windows operating systems. Most notably two or more monitors are used.

The Sectra IDS5 Radiology Workstation is in fact a family of devices, including several workstations or types of workstations, e.g. the following:

• Primary diagnostics workstation and the most powerful version of IDS5. It contains . tools for assisting the radiologist in making a diagnosis.
• . Dedicated workstation for mammography. It has all functionality as an IDS5/dx.net but with an additional mammography package.
• . Quality assurance workstation, mainly used by the technologists to prepare the images for the reviewing radiologist.
• Clinicians workstation used by the clinicians within the hospital to view the radiology . images and to read the radiology report.
• "Web" workstation that can be used by remote clinics to view images and radiology . reports.
• . "At-home" workstation that can be used by the radiologist over a low bandwidth connection.

The provided text does not contain any acceptance criteria or the study details to prove the device meets said criteria.

The document is a 510(k) summary and subsequent FDA clearance letter for the Sectra IDS5 Radiology Workstation. It primarily focuses on demonstrating substantial equivalence to a predicate device based on technological characteristics and intended use, rather than presenting performance data against specific acceptance criteria.

Here's what is NOT present in the provided text, which would be necessary to answer the request:

• Acceptance Criteria Table: There is no table outlining specific performance metrics (e.g., accuracy, sensitivity, specificity, speed, image quality standards) that the device was expected to meet.
• Reported Device Performance: Consequently, there are no reported performance results against any such criteria.
• Study Details:
  • No mention of a specific performance study (e.g., a clinical trial, validation study, or bench test for specific performance metrics).
  • No information on the sample size used for any test set.
  • No data provenance (country of origin, retrospective/prospective).
  • No number or qualifications of experts used for ground truth.
  • No adjudication method described.
  • No multi-reader, multi-case (MRMC) comparative effectiveness study mentioned or its effect size.
  • No standalone (algorithm only) performance study mentioned.
  • No type of ground truth used (pathology, expert consensus, outcomes data).
  • No sample size for a training set.
  • No information on how ground truth for a training set was established.

The "Performance Data" section in the document states: "The subject device is developed according to ISO 9001:2000 and complies with ACR/NEMA Digital Imaging Communications in Medicine version 3.0." This indicates adherence to quality management systems and communication standards, which are important, but they are not specific performance acceptance criteria for image interpretation or diagnostic accuracy.

The document's conclusion, "Based on the information supplied in this Special 510(k), we conclude that the subject device is safe, effective, and substantially equivalent to the predicate device," relies on the comparison with the predicate device regarding its design, intended use, and general characteristics, not on a new performance study proving it meets specific, quantifiable acceptance criteria.

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The Sectra IDS device is intended for the manipulation and displaying of x-ray images, including mammograms. 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 mammography reading, 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 IDS5 Radiology Workstation with IDS5/mx.net is mainly a software product. It is used for visualization and processing of digital radiology images. The system runs under the Window 2000 and Windows XP operating system. The requirements on hardware are quite ordinary for a system used for displaying images. Most notably up to four monitors can be used.

Here's a breakdown of the acceptance criteria and study information for the Sectra IDS5 Radiology Workstation, based on the provided text:

Important Note: The provided document is a 510(k) Summary and the FDA's clearance letter. It focuses on demonstrating substantial equivalence to a predicate device, not on specific performance studies with detailed acceptance criteria and results in the way a clinical trial might be presented. Therefore, many of the requested details (like sample size for test sets, ground truth methodology, expert qualifications, effect size for MRMC studies, etc.) are not present in this type of document, as they are not typically required for a 510(k) for an image processing system like this. The "Performance Data" section primarily refers to compliance with standards.

Acceptance Criteria and Reported Device Performance

Study Details:

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

   • Not specified in this 510(k) summary. For image processing systems of this nature, performance testing often involves internal verification and validation against specified technical requirements rather than a formal clinical study with a "test set" of patient data in the sense of a diagnostic performance study. The focus is on functionality and safety equivalence.

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

   • Not specified. Ground truth establishment is not detailed as this is not a diagnostic AI device requiring such a process for its 510(k) clearance.

3. Adjudication method for the test set:

   • Not specified. Adjudication is not applicable in the context of the information provided.

4. 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 MRMC study was done or reported. This device is a workstation for viewing and processing images, not an AI-assisted diagnostic tool designed to directly improve human reader performance in a quantifiable clinical output metric (like sensitivity or specificity).

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

   • No standalone performance study of an algorithm was done or reported. This device is not an algorithm that provides a diagnostic output independently; it's a tool for human interaction with images.

6. The type of ground truth used:

   • Not applicable/Not specified. The "ground truth" here would relate to the correct display and manipulation of images according to DICOM standards and user specifications, not a clinical diagnosis.

7. The sample size for the training set:

   • Not applicable/Not specified. This is not a machine learning/AI device that requires a "training set" in the conventional sense for developing a diagnostic algorithm. Its development is based on software engineering principles and compliance with standards.

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

   • Not applicable/Not specified. As mentioned above, this device does not utilize a training set in the way an AI algorithm does.

Ask a specific question about this device

The Sectra IDS 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 IDS5 10.1 Radiology Workstation is mainly a software product. It is used for visualization and processing of digital radiology images. The system runs under the Window 2000 and Windows XP operating system. The requirements on hardware are quite ordinary for a system used for displaying images. Most notably up to four monitors can be used.

This document describes a 510(k) submission for the Sectra IDS5 Radiology Workstation - Version 10.1, a Picture Archiving and Communications System (PACS).

Here's an analysis of the acceptance criteria and study information provided in the input, specifically focusing on what is present and absent from the document:

1. Table of Acceptance Criteria and Reported Device Performance

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

• Absent. The document does not describe a clinical study or a test set of images with a defined sample size. The performance data section refers to compliance with standards rather than a specific study involving image data.

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

• Absent. Since there is no described clinical study or test set for evaluation of image interpretation performance, there is no mention of experts establishing ground truth for such a set. The document states that images and information are "interpreted by a physician or trained medical personnel," implying human oversight during clinical use, but not in the context of device validation.

4. Adjudication method for the test set

• Absent. No test set or corresponding adjudication method is described.

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

• Absent. The device described is a PACS workstation, an image processing and display system. It does not appear to incorporate AI or CAD (Computer-Aided Detection/Diagnosis) directly for interpretation assistance. Therefore, an MRMC study comparing human readers with and without AI assistance is not applicable and not mentioned.

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

• Absent. As noted above, this device is a workstation for displaying and manipulating images, not a standalone AI algorithm for diagnostic interpretation. Its performance is related to its ability to correctly process and present images according to standards, leaving interpretation to human users.

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

• Absent. This information is not relevant in the context of the provided performance data, which focuses on compliance with technical standards (ISO, DICOM) and substantial equivalence to a predicate device for its intended function as an image display and processing system. The core function is presenting images, not independently deriving diagnostic conclusions that would require a ground truth comparison.

8. The sample size for the training set

• Absent. This device is a software product for viewing and manipulating images; it does not explicitly use a "training set" in the context of machine learning for diagnostic tasks. Its development followed ISO 9001:2000, which is a quality management system standard.

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

• Absent. As there is no described training set for a machine learning model, the establishment of ground truth for such a set is not applicable.

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