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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.

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

The device assists orthopedic surgeons when doing preoperative planning and post-operative follow-up.

Typical users of this system are trained professionals, for example orthopedic surgeons, physicians, and radiologists.

Sectra Orthopedic Package is intended to assist orthopedic surgeons when doing preoperative planning and post-operative follow-up. The device has functionality for overlaying prosthesis templates on radiological images, tools for repositioning the templates, and tools for measurements in the images.

More specifically the Sectra Orthopedic Package shall: (1) assist the orthopedic surgeons in choosing which implants to use, (2) assist the orthopedic surgeons in choosing were to place cut lines etc., and (3) assist the orthopedic surgeons in following-up of surgical procedures.

The provided text does not contain information about specific acceptance criteria or a study designed to prove the device meets such criteria.

The "Performance Data" section 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 standards and DICOM communication protocols, but these are general compliance statements, not specific performance metrics or a study demonstrating clinical effectiveness.

The document is a 510(k) summary, which focuses on demonstrating substantial equivalence to a predicate device rather than presenting detailed clinical study results for novel performance claims.

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

Here's how I would present the information if it were available in the text:

1. Table of acceptance criteria and the reported device performance:
(No information available in the provided text.)

2. Sample sized used for the test set and the data provenance:
(No information available in the provided text.)

3. Number of experts used to establish the ground truth for the test set and the qualifications of those experts:
(No information available in the provided text.)

4. Adjudication method for the test set:
(No information available in the provided text.)

5. If a multi-reader multi-case (MRMC) comparative effectiveness study was done, and the effect size:
(No information available in the provided text.)

6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done:
(No information available in the provided text.) However, the "Conclusion" section mentions "Images and information being reviewed, processed, relayed, and or transmitted are interpreted by an orthopedic surgeons or trained medical personnel, providing ample opportunity for competent human intervention," which suggests a human-in-the-loop workflow.

7. The type of ground truth used:
(No information available in the provided text.)

8. The sample size for the training set:
(No information available in the provided text.)

9. How the ground truth for the training set was established:
(No information available in the provided text.)

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The device is intended for use to estimate bone mineral density ("BMD") in the forearm and to assess increased risk of osteoporotic fracture according to World Health Organization ("WHO") criteria.

The device is indicated specifically for use to: (1) assist the physician in diagnosing subjects who already have been identified to be at risk for suffering from osteoporosis, together with other known risk factors (e.g., prior history of fractures, advanced age, low body weight, lack of physical exercise, lack of exposure to sunlight, insufficient dietary intake of vitamin D and/or calcium, and smoking); and (2) compare the BMD estimate with a reference population comprised of young normals and age-matched normals to compute T-scores and Z-scores, respectively.

The Sectra Osteoporosis Package is intended for use to estimate bone mineral densitv (BMD) in the forearm and to assess increased risk of osteoporotic fracture according to World Health Organization (WHO) criteria.

Sectra Osteoporosis Package features:

• indirect "DICOM capability" via the Sectra IDS5 Radiology workstation, i.e. it uses digital image data according to the DICOM standard.
• . single screen user interface reached via a right click on an image in a Sectra IDS5 Radiology Workstation.
• operates in two modes: Clinical mode and Research mode.

Here's a summary of the acceptance criteria and study information for the Sectra Osteoporosis Package Bone Densitometer, based on the provided text:

Lack of Information:
It's important to note that the provided text is a 510(k) summary for a substantial equivalence determination. As such, it does not contain detailed performance study results, acceptance criteria, or ground truth methodologies in the way a clinical study report or more comprehensive technical documentation would. The submission focuses on demonstrating substantial equivalence to a predicate device, primarily by showing that differences in image source (digital CR vs. digitized conventional X-ray) do not raise new safety or effectiveness concerns.

The document explicitly states: "Both in vitro and in vivo performance testing has been conducted to verify that this design difference does not impact either safety or efficacy." However, it does not provide the specific details of these tests, including acceptance criteria, sample sizes, or how performance was measured against a ground truth.

1. Table of Acceptance Criteria and Reported Device Performance

Given the limitations of the provided document, specific numerical acceptance criteria and detailed performance metrics are not explicitly stated. The document primarily focuses on demonstrating "substantial equivalence" to a predicate device rather than providing quantitative performance against pre-defined acceptance criteria.

The implied "acceptance criterion" is that the device's performance in estimating Bone Mineral Density (BMD) and assessing osteoporotic fracture risk is equivalent to that of the predicate device, the X-Posure System Version 2 RAD (K002500), despite using different image sources.

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

The document states: "Both in vitro and in vivo performance testing has been conducted". However, no specific sample sizes for either the in vitro or in vivo test sets are provided.

Data Provenance: The country of origin of the data is not specified. It is also not specified whether the data was retrospective or prospective.

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

Not specified. The document does not provide any information on experts or how ground truth was established for the performance testing.

4. Adjudication Method for the Test Set

Not specified. The document does not describe any adjudication method.

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

Not applicable/Not mentioned. The document does not describe a multi-reader multi-case (MRMC) comparative effectiveness study, nor does it discuss human readers improving with or without AI assistance. The device is a bone densitometer, an automated analysis tool for BMD, not an AI-assisted diagnostic aid for human readers in the typical sense of an MRMC study.

6. Standalone (Algorithm Only) Performance Study

The primary performance assessment described is a standalone (algorithm only) evaluation, as the device is an automated analysis tool. The document states the device "analyzes computed radiography ('CR') images obtained directly in digital format" and "uses essentially the same DXR techniques to analyze the given images" as the predicate device. The in vitro and in vivo tests were conducted to verify the algorithm's performance with the new image source.

7. Type of Ground Truth Used

Not specified. The document does not explicitly state the type of ground truth used for the in vitro or in vivo performance testing (e.g., bone mineral density measured by a gold standard, pathology, clinical outcomes, etc.). For BMD, a common ground truth in such studies might be a highly accurate densitometry method or phantoms with known BMDs for in vitro studies. However, this is not detailed in the provided text.

8. Sample Size for the Training Set

Not applicable/Not mentioned. The document describes the device as using "DXR techniques" and being "essentially the same as the predicate device". This implies a deterministic algorithm or a known analytical method, rather than a machine learning model that would typically require a distinct training set. Therefore, a "training set" in the context of machine learning is not mentioned as being used for this device.

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

Not applicable/Not mentioned. As a training set is not indicated, the method for establishing its ground truth is also not provided.

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The Sectra AB WISE II 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 devicesusing DICOM or similar interface standards.

WISE II is a system for managing digital radiological images. This includes image storage and searching for images in archives and retrieving image for re-consultation. WISE II generally provides functions to:

• Access information related to requests, examinations and images .
• Create, move, copy and delete folders and examination folders .
• Add images to and delete images from examinations .
• File examinations to the archives and retrieve examinations from the archives ●
• Retrieve examinations from DICOM conformant archives ●
• Manage images stored on multiple file servers
• Provide services (DICOM, WWW, etc) to clients via WISE gateways ●
• . Interface RIS for relational integrity
• . Send and retrieve images (teleradiology)

The provided text does not contain detailed information about specific acceptance criteria and a study proving a device meets these criteria in the way typically found in modern medical device submissions (e.g., an AI-powered diagnostic tool).

The document is a 510(k) summary for the "WISE II Image Management System," a device described as a "Digital Imaging System" for managing radiological images. The primary focus of this submission is to demonstrate substantial equivalence to a predicate device (WISE Image Management System, K971451), rather than proving specific performance metrics of an AI algorithm against a ground truth dataset.

Therefore, many of the requested details (like sample size for test sets, number of experts for ground truth, MRMC studies, standalone performance of an algorithm, and training set information) are not applicable or not available in this document.

Here's an attempt to answer the questions based only on the provided text, indicating where information is absent:

Acceptance Criteria and Study for Sectra WISE II Image Management System (K983447)

This 510(k) submission primarily focuses on demonstrating substantial equivalence to a predicate device (Sectra WISE Image Management System, K971451) for an image management system, not on proving the performance of an AI algorithm against specific clinical outcomes or established ground truth with quantified metrics. Therefore, many of the requested parameters related to AI performance studies are not present in this document.

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

The document does not specify quantitative acceptance criteria or reported performance metrics in the way one would for a diagnostic AI algorithm (e.g., sensitivity, specificity, AUC). Instead, the performance is described in terms of compliance with industry standards and safety regulations, and functional capabilities.

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

• Sample Size (Test Set): Not applicable / Not provided. This submission is for an image management system, not a diagnostic algorithm that would typically have a test set of medical images for performance evaluation.
• Data Provenance: Not applicable / Not provided.

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. The device manages images; it does not perform automated diagnoses requiring expert-established ground truth for a test set. The document notes that "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."

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

• Not applicable / 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

• No. This is not an AI-assisted diagnostic device, but an image management system. Therefore, an MRMC study and related effect size are not applicable.

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

• No. This device's function is to manage and display images for human interpretation, not to provide standalone algorithmic diagnoses.

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

• Not applicable / Not provided. Ground truth is not a concept explicitly applied to the performance evaluation described for this type of device.

8. The sample size for the training set

• Not applicable / Not provided. This device is not an AI algorithm that undergoes a training phase with a labeled dataset.

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

• Not applicable / Not provided.

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The ImageServer 2000 System will be used for digitally store images for archival together with informaton about the images.

The ImageServer 2000 System will be used for digitally store images for archival together with informaton about the images.

This document is a 510(k) summary for the SECTRA ImageServer 2000 Picture Archiving System, dated August 16, 1996. It outlines the safety and effectiveness information for regulatory submission.

Based on the provided text, the document does not contain the detailed information required to answer the request about acceptance criteria and a study proving device performance. The summary focuses on regulatory compliance and general safety statements rather than specific performance metrics or clinical study results.

Here's why and what information is missing:

• Acceptance Criteria and Reported Device Performance: The document states that the system will "digitally store images for archival together with information about the images." It doesn't provide any quantitative performance metrics (e.g., accuracy, speed, image quality retention, error rates, clinical outcomes) that would serve as acceptance criteria or reported performance data. Therefore, a table of acceptance criteria and reported device performance cannot be generated.
• Study Details: There is no mention of a specific study, clinical trial, or performance evaluation that was conducted to demonstrate the device's efficacy or safety in terms of clinical outcomes or diagnostic accuracy.
  • Sample size and data provenance: Not mentioned.
  • Number of experts and qualifications: Not mentioned.
  • Adjudication method: Not mentioned.
  • MRMC comparative effectiveness study: Not mentioned.
  • Standalone (algorithm only) performance: Not applicable as this is an archiving system, not an AI diagnostic algorithm.
  • Type of ground truth: Not mentioned.
  • Sample size for training set: Not applicable as this is an archiving system, not an AI diagnostic algorithm.
  • How ground truth for training set was established: Not applicable.

The document primarily states:

• The ImageServer System will be used for digitally storing images and associated information.
• Typical users are trained medical professionals.
• It complies with Federal Performance Standards (21 CFR, part 1000).
• It was manufactured in accordance with voluntary standards.
• The User's Guide contains information for safe and effective use.
• A hazard analysis was performed.

This information is insufficient to address the questions posed, which are typically relevant to diagnostic or therapeutic devices undergoing performance validation. The SECTRA ImageServer 2000, as described, is a Picture Archiving System (PACS), and its "performance" is more related to its functionality in storing, retrieving, and displaying images, rather than diagnostic accuracy.

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The TRS 2000 system is a teleradiology system/transmission and advance reviewing station X-ray images. TRS 2000 is used for advanced reviewing and transmission over private or public telecommunications networks of radiological images.

The TRS 2000 system is a teleradiology system/transmission and advance reviewing station X-ray images.

This document is a 510(k) summary for the SECTRA Teleradiology System TRS 2000, dated May 15, 1996. It asserts the system's substantial equivalence to the Philips PMS Easy Vision (K920950) and discusses its safety and effectiveness.

However, the provided text does not contain the specific information needed to answer the request about acceptance criteria and a study proving those criteria are met.

Here's why and what information is missing:

• No specific acceptance criteria: The document states the system is "safe and effective" and complies with federal performance standards and voluntary standards, but it doesn't list quantifiable performance metrics as acceptance criteria (e.g., sensitivity, specificity, accuracy, image resolution targets, transmission speed guarantees).
• No detailed study description: While it mentions "substantial independent experience from clinical operation" and "close co-operation with radiologists from the specifications to test phases," it does not describe a formal study with the elements requested (sample size, data provenance, ground truth establishment, expert qualifications, adjudication methods, MRMC studies, standalone performance, training set details).

Therefore, based only on the provided text, I cannot complete the table or answer the specific questions about the study. The document is a regulatory summary, not a detailed scientific study report.

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