K Number
K020546
Device Name
FUSION 7D
Date Cleared
2002-04-26

(66 days)

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

Fusion7D registers pairs of anatomical and functional volumetric images (e.g. MRI-SPECT, MRI-PET, CT-SPECT, CT-PET), or pairs of anatomical volumetric images (e.g. MRI-MRI, CT-CT and MRI-CT) as a means to ease the comparison of image volume data by the clinician. The result of the registration operation aims to help the clinician obtain a better understanding of the joint information that would otherwise have to be compared visually. This is useful for a wide range of clinical and therapeutic applications. It is important to note that the clinician retains the ultimate responsibility for making the pertinent diagnosis based on their standard procedures including visual comparison of the separate unregistered images. Fusion7D is a complement to these standard procedures.

Device Description

Fusion7D is a software program running on a PC platform, which brings into alignment (registers) pairs of images from different imaging modalities. Fusion7D also includes functionality to read, display, and save the original volumetric data and the results of the registration operation by means of a graphic user interface that includes visualization, file browsing and control of input and output as described in the following text.

AI/ML Overview

The provided text describes Fusion7D, a software program for registering and fusing medical images. However, it does not include detailed acceptance criteria or a study that specifically proves the device meets such criteria in terms of quantitative performance metrics, sample sizes, expert involvement, or statistical analysis.

The document is a 510(k) summary, which focuses on demonstrating substantial equivalence to predicate devices, rather than providing a detailed performance study with acceptance criteria.

Therefore, the following information cannot be extracted from the provided text:

  • A table of acceptance criteria and the reported device performance: This information is not present. The document describes the device's capabilities and intended use but does not quantify performance against specific criteria.
  • Sample size used for the test set and the data provenance: No performance study details are given.
  • Number of experts used to establish the ground truth for the test set and the qualifications of those experts: Not mentioned.
  • Adjudication method for the test set: Not mentioned.
  • If a multi-reader multi-case (MRMC) comparative effectiveness study was done, and the effect size of how much human readers improve with AI vs without AI assistance: This type of study is not described. The device is a registration tool, not an AI diagnostic aid in the sense of improving human reader performance on a diagnostic task, although it aims to "ease the comparison of image volume data."
  • If a standalone (i.e., algorithm only without human-in-the-loop performance) was done: Not explicitly stated or quantified in terms of performance. The document implies automated registration capabilities but doesn't provide a standalone performance evaluation against a gold standard.
  • The type of ground truth used (expert consensus, pathology, outcomes data, etc.): Not mentioned.
  • The sample size for the training set: No training data or set is mentioned, as this is more a description of the final device functionality rather than its development.
  • How the ground truth for the training set was established: Not applicable, as no training set is described.

Summary of what can be inferred about "acceptance criteria" and "study" implicitly from the document:

The "acceptance criteria" for Fusion7D, as implied by the 510(k) process, primarily revolve around demonstrating substantial equivalence to legally marketed predicate devices in terms of intended use, technological characteristics, and safety/effectiveness. The "study" largely consists of the submission itself, detailing the device's functionality and comparing it to existing, approved devices.

The document states:

  • "Fusion7D is a software program running on a PC platform, which brings into alignment (registers) pairs of images from different imaging modalities."
  • It supports "manual," "semi-automatic," and "automatic" registration, limited to "rigid body deformation."
  • It provides "standard visualization facilities" and allows "registration results to be displayed in a variety of ways."
  • Intended Use: "Fusion7D registers pairs of anatomical and functional volumetric images... as a means to ease the comparison of image data."

The FDA's approval letter confirms that the device was found "substantially equivalent" based on its comparison to the predicate devices listed (K010336, K983256, K992654). This substantial equivalence is the de facto "acceptance criteria" for this 510(k) submission, and the "study" is the submission argument itself.

§ 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).