(88 days)
Collaboration Live is indicated for remote console access of the Philips ultrasound system for diagnostic image viewing and review, consultation, guidance, support, and education in real time. Access must be granted by the healthcare professionals operating the ultrasound system. Compliance with the technical and operator requirements specified in the User Manual is required.
It is the responsibility of the healthcare professionals at the remote client to ensure image quality, display contrast, and ambient light conditions are consistent with the generally accepted standards of the clinical application.
Collaboration Live is software-based communication feature integrated in Philips Diagnostic Ultrasound Systems. Collaboration Live together with remote-client Reacts enables two-way communication of text, voice, image, and video information between an ultrasound local system operator and a remote healthcare professional on a Windows device. Collaboration Live-Reacts facilitates: 1) remote diagnostic viewing and review, 2) remote clinical training and education, 3) remote peer collaboration, and 4) remote service support. Collaboration Live functionality includes a remote control feature in which the ultrasound local system operator may grant a qualified remote user control of the ultrasound system parameters via a virtual control panel and virtual touch screen. By meeting the technical, operator, and environment requirements specified in the User Manual, healthcare professionals using Reacts may provide clinical diagnoses from a remote location as they would directly on the ultrasound system.
Please note: The provided document is a 510(k) summary for the "Collaboration Live" device. It focuses on demonstrating substantial equivalence to a predicate device, particularly regarding the change to allow diagnostic use for remote viewing. While it mentions "validation testing with pre-determined criteria," it does not provide the specific details of the acceptance criteria or the full study report. It only states that such testing was conducted and that the labeling was updated based on the findings.
Therefore, much of the requested information regarding detailed acceptance criteria, specific reported performance, sample sizes, ground truth establishment, expert qualifications, and MRMC study details cannot be fully extracted from this document alone.
Here's what can be extracted based on the provided text:
1. A table of acceptance criteria and the reported device performance
The document mentions "pre-determined criteria" for validation testing but does not explicitly list them or the quantitative reported device performance against these criteria. It broadly states that "remote display specifications and network bandwidth requirements for equivalent image quality for diagnostic viewing were determined."
Given the limited information, a hypothetical table based on the statement "equivalent image quality for diagnostic viewing" might look like this, but these are inferred and not explicitly stated criteria or performance metrics in the document:
| Acceptance Criteria (Inferred) | Reported Device Performance (Inferred) |
|---|---|
| Equivalent image quality for diagnostic viewing compared to local ultrasound system | Met (stated that "equivalent image quality for diagnostic viewing were determined") |
| Adherence to remote display specifications | Met (stated that "remote display specifications... were determined") |
| Adherence to network bandwidth requirements | Met (stated that "network bandwidth requirements... were determined") |
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 for test set: Not specified in the provided document.
- Data provenance: Not specified in the provided document. The document only mentions "Validation testing... was conducted."
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 specified in the provided document.
4. Adjudication method (e.g. 2+1, 3+1, none) for the test set
Not specified in the provided document.
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 an MRMC comparative effectiveness study, nor does it refer to AI assistance. The device is a "software-based communication feature" for remote viewing and control of ultrasound systems, not an AI-driven image analysis tool.
6. If a standalone (i.e. algorithm only without human-in-the-loop performance) was done
The device "Collaboration Live" is explicitly designed for "remote console access" and "two-way communication... between an ultrasound local system operator and a remote healthcare professional." It's a system for human users to interact remotely with an ultrasound machine. Therefore, a "standalone algorithm only" performance study, without human involvement, would not be applicable to this type of device. The study implicitly involves human users making diagnostic decisions.
7. The type of ground truth used (expert consensus, pathology, outcomes data, etc)
Not explicitly stated in the provided document beyond implying an evaluation of "equivalent image quality for diagnostic viewing." This would typically require expert assessment of image quality and diagnostic accuracy, but the specifics are not detailed.
8. The sample size for the training set
Not applicable. The "Collaboration Live" device is a software communication feature for remote access and viewing, not a machine learning or AI algorithm that requires a training set.
9. How the ground truth for the training set was established
Not applicable, as there is no training set for this type of device.
§ 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).