(60 days)
CONi provides for the archiving and viewing of medical images from the following DICOM modalities: Computed Tomography (CT), Magnetic Resonance (MR), X-ray (CR), Ultrasound (US), and Visible Light (External Camera (XC) and Other (OT)). Images and information can be viewed and stored via a secure Internet connection.
CONi is not intended for use in mammography.
CONi is not intended for diagnostic use on mobile devices
CONi (Capture Over Network Interface) is a secure cloud-based application for viewing and archiving medical images. The CONi software system is comprised of a Picture Archiving and Communication System (CONiPACS) and an image viewer (CONiView). CONi supports imaging studies from the following DICOM modalities: Computed Tomography (CT), Magnetic Resonance (MR), X-ray (CR), Ultrasound (US), and Visible Light (External Camera (XC) and Other (OT)). Images and information can be viewed and stored via a secure Internet connection.
The provided submission K130624 for GlobalMedia Group, LLC. CONi™ does not contain information about explicit acceptance criteria for device performance or a detailed study proving the device meets said criteria in the way a clinical performance study would.
Instead, this 510(k) summary focuses on demonstrating substantial equivalence to a predicate device (ALZ Web PACS, Version 1.0, K081304) for its intended use, technological characteristics, and safety/effectiveness, without presenting specific performance metrics.
The non-clinical tests listed are related to software development and validation, rather than a performance study with specific acceptance criteria:
- Establishment of Requirements
- Risk Analysis (software and system)
- DICOM Standard Conformance Statement
- HIPAA Compliance Statement
- Software Unit Testing
- Software Integration Testing
- Software System Testing
- Software Hazard Testing
Therefore, it is not possible to fill out the requested table and answer many of the study-specific questions.
Here's an attempt to address the questions based on the available information, noting where information is explicitly absent:
1. Table of acceptance criteria and the reported device performance
A table of acceptance criteria and reported device performance cannot be provided as this information is not present in the 510(k) summary. The submission focuses on demonstrating substantial equivalence, not on specific performance metrics or clinical study results with predefined acceptance criteria. Testing focused on software validation, DICOM conformance, and HIPAA compliance.
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 510(k) summary. The document does not describe a clinical performance test set or data derived from real patient cases. The testing mentioned (Unit, Integration, System, Hazard) implies internal software quality assurance.
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. No ground truth establishment for a test set of medical cases is described.
4. Adjudication method (e.g., 2+1, 3+1, none) for the test set
This information is not provided. No adjudication method is mentioned as there is no described test set requiring expert adjudication.
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
An MRMC comparative effectiveness study was not done and is not applicable to this device. CONi™ is a Picture Archiving Communication System (PACS) and image viewer, not an AI-assisted diagnostic tool designed to improve human reader performance. It does not provide AI assistance for diagnostic interpretation.
6. If a standalone (i.e., algorithm only without human-in-the-loop performance) was done
A standalone performance study was not done and is not applicable. CONi™ is a PACS and image viewer, which inherently involves human interaction for viewing and archiving images. It does not perform any diagnostic algorithms in a standalone capacity that would require such a study.
7. The type of ground truth used (expert consensus, pathology, outcomes data, etc.)
This information is not provided. No ground truth is mentioned as there are no diagnostic claims or performance analyses on medical images in the context of clinical accuracy.
8. The sample size for the training set
This information is not provided. As CONi™ is a PACS and image viewer (not an AI/CAD algorithm), it does not typically involve a "training set" in the machine learning sense. The software development process likely involved internal testing with various types of DICOM images to ensure proper functionality and rendering.
9. How the ground truth for the training set was established
This information is not provided and is not applicable as there is no "training set" in the context of machine learning described for diagnostic purposes.
§ 892.2050 Medical image management and processing system.
(a)
Identification. A medical image management and processing system is a device that provides one or more capabilities relating to the review and digital processing of medical images for the purposes of interpretation by a trained practitioner of disease detection, diagnosis, or patient management. The software components may provide advanced or complex image processing functions for image manipulation, enhancement, or quantification that are intended for use in the interpretation and analysis of medical images. Advanced image manipulation functions may include image segmentation, multimodality image registration, or 3D visualization. Complex quantitative functions may include semi-automated measurements or time-series measurements.(b)
Classification. Class II (special controls; voluntary standards—Digital Imaging and Communications in Medicine (DICOM) Std., Joint Photographic Experts Group (JPEG) Std., Society of Motion Picture and Television Engineers (SMPTE) Test Pattern).