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eFilm Video™ is a software application that captures video streams from analogue medical image acquisition devices with video outputs and converts these streams to DICOM compliant cine loops. These images can be sent to DICOM compliant devices for display and processing. Users may also input patient demographic information that is related to the captured images.

eFilm Video™ is a software application that is used for capturing video streams from analogue medical image acquisition devices with video outputs and converting these streams to DICOM modiant cine loops. Users may capture single stills or consecutive images and save them as DICOM files that can be viewed and manipulated in a picture archiving and communications viewing application.

Here's an analysis of the provided 510(k) summary regarding the eFilm Video™ device, focusing on acceptance criteria and study details.

Important Note: The provided 510(k) summary for eFilm Video™ (K013631) is for a "Video Capture System" that converts analog video streams into DICOM cine loops. This is a very different type of device than what is typically associated with AI-driven medical image analysis, which usually involves performance metrics like sensitivity, specificity, or AUC for detecting or classifying medical conditions.

Based on the information provided, the "acceptance criteria" for this specific device are centered around its functional capabilities and safety, rather than diagnostic performance metrics of an AI algorithm. The testing described is primarily software verification and validation.

Acceptance Criteria and Reported Device Performance

Study Details (Based on the Provided Information)

Since eFilm Video™ is a video capture system and not an AI-driven diagnostic tool, the typical "study" parameters for AI performance (like sample size for test sets, ground truth methodology for diagnostic accuracy, MRMC studies, or standalone algorithm performance) are not applicable or detailed in this 510(k) summary. The "study" here refers to the internal software testing and verification/validation processes.

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

   • Test Set Size: Not specified in this summary. The testing refers to software testing against "specifications," rather than a clinical test set of patient data for diagnostic performance.
   • Data Provenance: Not applicable in the context of typical diagnostic imaging studies. The device captures video streams from "analogue medical image acquisition devices," implying it processes various types of medical video.

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

   • Not applicable as this device is not designed to provide a diagnostic output requiring expert-established ground truth for performance evaluation. The "ground truth" for this device would be whether it accurately converts analog video to DICOM cine loops according to its specifications. This would be verified through technical testing, not expert clinical interpretation.

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

   • Not applicable. Adjudication methods are used to establish consensus ground truth in diagnostic studies. This summary focuses on functional and safety verification.

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 comparative effectiveness study was done, as this device is a foundational video capture and DICOM conversion tool, not an AI-assisted diagnostic aid.

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

   • The term "standalone performance" isn't directly applicable in the AI sense. However, the device's core function (capturing and converting video) is an "algorithm only" type of process. Its performance is evaluated against its technical specifications, which are implicitly standalone. The summary states there is "opportunity for competent human intervention [who] interprets images and information being displayed and/or printed," which speaks to its place in a clinical workflow, not its standalone diagnostic capability.

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

   • Not applicable for diagnostic ground truth. The "ground truth" for this device's performance would be successful conversion of analog video to DICOM-compliant cine loops as per technical specifications, verified by software testing and potentially visual inspection of the output for fidelity.

7. The sample size for the training set:

   • Not applicable. This device is a software application for video capture and conversion, not a machine learning model that requires a training set.

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

   • Not applicable, as there is no machine learning model or training set involved.

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