K Number
K963697
Manufacturer
Date Cleared
1996-11-27

(72 days)

Product Code
Regulation Number
892.2050
Reference & Predicate Devices
Predicate For
AI/MLSaMDIVD (In Vitro Diagnostic)TherapeuticDiagnosticis PCCP AuthorizedThirdpartyExpeditedreview
Intended Use

ADV is intended to create 3D images of the anatomy from a set of CT or MRI images.

Device Description

ADV is software used for 3D image processing of CT and MR diagnostic images.

AI/ML Overview

This document describes a 510(k) summary for the ADV software, a device intended for 3D image processing of CT and MR diagnostic images.

Based on the provided information, the following can be extracted:

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

    No specific quantitative acceptance criteria are provided in the document. The general claim is that "ADV is substantially equivalent to the predicate device in its ability to render accurate 3D images for use in medical diagnosis." The reported performance is that "The ADV software renders a 3D image which exhibits great faithfulness to the original and does so with great speed." This is a qualitative statement, not a measurable performance metric.

Acceptance CriteriaReported Device Performance
Not specifiedRenders a 3D image exhibiting "great faithfulness to the original" with "great speed." Substantially equivalent to predicate device (VoxelView® 2.5) in ability to render accurate 3D images for medical diagnosis.
  1. Sample size used for the test set and the data provenance (e.g. country of origin of the data, retrospective or prospective)

    The document explicitly states: "Clinical data have not been submitted as part of this premarket notification." Therefore, there is no mention of a test set, sample size, or data provenance from clinical data. The non-clinical tests likely involved internal testing of the software's functionality and accuracy, but details are not provided.

  2. 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)

    No clinical data were submitted, so there is no mention of experts establishing ground truth for a test set.

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

    No clinical data were submitted, so there is no mention of an adjudication method for a test set.

  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 clinical data were submitted, and the device is described as software for 3D image processing rather than an AI-assisted diagnostic tool. Therefore, an MRMC comparative effectiveness study was not performed or submitted.

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

    The document implies that nonclinical tests were performed on the software itself to determine its "faithfulness to the original" and speed, which would constitute standalone performance relative to its stated function. However, no specific details or results of these standalone tests are provided, beyond the qualitative statement of performance.

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

    For the non-clinical tests, the "ground truth" for evaluating the faithfulness of the 3D rendered image would likely be the original 2D CT or MR images themselves, and potentially the mathematical or graphical accuracy based on the algorithms used for 3D reconstruction. No external clinical "ground truth" (like pathology or outcomes) is mentioned as no clinical data were submitted.

  7. The sample size for the training set

    The document does not describe the device as an AI/machine learning model that requires a training set in the conventional sense. It's described as "software used for 3D image processing." Therefore, no training set or its sample size is mentioned.

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

    As the device is not described as an AI/machine learning model requiring a training set, this question is not applicable based on the provided text.

{0}------------------------------------------------

Appendix K - Class II 510(k) Summary ADV®

K963697

This summary of 510(k) safety and effectiveness information is being submitted in accordance with the requirements of SMDA 1990 and 21 CFR 807.92.

  1. Predicate Device VoxelView® 2.5 (K953259).

  2. Device Description

ADV is software used for 3D image processing of CT and MR diagnostic images.

  1. Intended Use

ADV is intended to create 3D images of the anatomy from a set of CT or MRI images.

  1. Comparison with Predicate Device

The products have substantially equivalent features for processing CT/MR imaging data in a three-dimensional format. The ADV software renders a 3D image which exhibits great faithfulness to the original and does so with great speed. Relative to VV2.5. ADV is a more convenient software package to use.

5. Nonclinical Tests

ADV has been developed in a manner consistent with accepted standards for software development, including testing protocols.

  1. Clinical Data

Clinical data have not been submitted as part of this premarket notification.

  1. Conclusions Drawn from Nonclinical and Clinical Tests

We conclude from these tests that ADV is substantially equivalent to the predicate device in its ability to render accurate 3D images for use in medical diagnosis.

End of Class II 510(k) Summary

§ 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).