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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.

• 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). |
  | Compliance with Data Communications Controls | Both subject and predicate devices "use standard data communications controls to detect errors." |
  | Compliance with Safety Standards | Complies with IEC 950 - Safety of Information Technology Equipment. |
  | Compliance with Electromagnetic Compatibility (EMC) Standards | Complies with CISPR 22, class A - Electromagnetic Compatibility, IEC-801-2, IEC-801-3 - Electromagnetic Compatibility, FCC Part 15 sub-part B class A. |
  | Compliance with Information Processing Standards | Complies with IEEE 1003.1 - POSIX standard for Information Processing. |
  | Compliance with Network Standards | Complies with IEEE 802.3 - Ethernet, LAN Interface Standard. |
  | Compliance with Medical Imaging Standards | Complies with ACR/NEMA Digital Imaging Communications In Medicine version 3.0 (DICOM). |
  | Security Measures | "Passwords are required for operation and to protect against unauthorized use." |
  | Error Recovery | "Device failures, which might result in partial or failed transmissions, images, or data, may be recovered from storage or retransmission after correcting the problem(s)." |

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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