K Number
K093703
Device Name
3DI
Manufacturer
Date Cleared
2010-01-19

(49 days)

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

3Di is a software package of PACS workstation for handling multimodality (CT, XA, MR, PET, SPECT & Ultrasound) images, which are using DICOM protocol. It includes volume rendering, Multi-planar reconstruction (MPR) and viewing of the inner and outer surfaces of organs as well as within their walls.

3Di is intended for use as an interactive tool for assisting professional Radiologists, Cardiologists and specialists to reach their own diagnosis, by providing tools of communication, clinics networking, WEB Serving, image viewing, image manipulation, 2D/3D image visualization, image processing, reporting and archiving. This product is not intended for use with or for diagnostic interpretation of Mammography images.

The 3Di indications for use are processing of Cardiac CT angiography studies, including coronaries analysis, cardiac functional assessment and CT colonoscopy.

Device Description

3Di is a PACS device which enables users to access medical images over a network and to utilize 3Di's image visualization tools to review the images. It provides the following functions: Web server, patient browser, PACS capabilities, multi-modality viewing, CT Cardiac and Colonoscopy clinical applications.

AI/ML Overview

Here's an analysis of the provided 510(k) summary regarding the 3Di device, addressing your specific questions.

1. Table of Acceptance Criteria and Reported Device Performance

The provided 510(k) summary does not explicitly state acceptance criteria in a quantitative or pass/fail threshold manner. Instead, it describes a comparative study against a predicate device.

Acceptance Criteria (Implicit)Reported Device Performance
General functionality of image reformatting (various modalities)"results of the two devices are very similar"
Reliability of orientation annotations displayed"results of the two devices are very similar"
Correctness of measurements"results of the two devices are very similar"
Image quality"results of the two devices are very similar"
Cardiac analysis Graphs and Results"results of the two devices are very similar"
Colon analysis results"results of the two devices are very similar"
Overall Safety and Effectiveness (compared to predicate)"substantial equivalent in terms of safety and effectiveness to the predicate devices."

2. Sample Size Used for the Test Set and Data Provenance

The 510(k) summary does not specify the sample size used for the comparative performance study. It also does not mention the data provenance (e.g., country of origin, retrospective or prospective nature of the data).

3. Number of Experts Used to Establish Ground Truth for the Test Set and Their Qualifications

The 510(k) summary does not provide information on the number of experts or their qualifications. The study described is a comparison of the 3Di device against a predicate device (Philips Brilliance) rather than establishing ground truth against expert consensus.

4. Adjudication Method for the Test Set

The 510(k) summary does not describe an adjudication method. The study appears to be a direct comparison of the 3Di's output with the predicate device's output across various functions.

5. Multi-Reader Multi-Case (MRMC) Comparative Effectiveness Study

The provided text does not indicate that a Multi-Reader Multi-Case (MRMC) comparative effectiveness study was done. The comparison is between two devices, not human readers with and without AI assistance. Therefore, there is no effect size reported for human readers improving with AI.

6. Standalone (Algorithm Only Without Human-in-the-Loop Performance) Study

Yes, a standalone performance assessment was effectively done. The summary states: "Its performance has been validated by comparison to the performance of the Philips Brilliance predicate device." This implies an evaluation of the algorithm's output directly against the predicate device's output, without human intervention in the loop during this specific performance validation.

7. Type of Ground Truth Used

The "ground truth" for this study was the performance and output of the legally marketed predicate device (Philips Brilliance). The comparison aimed to demonstrate "substantial equivalence" to this established device, rather than to a clinical ground truth like pathology or patient outcomes.

8. Sample Size for the Training Set

The 510(k) summary does not provide information on the sample size used for the training set. Given the submission date (2010) and the description of the device as a PACS workstation with visualization tools, it's possible that traditional "training sets" in the modern machine learning sense might not have been explicitly documented or emphasized in the same way as they would be for deep learning-based AI devices today. The device focuses on visualization and manipulation tools, which might rely more on established graphics algorithms than on data-driven machine learning models.

9. How the Ground Truth for the Training Set Was Established

The 510(k) summary does not provide information on how ground truth was established for any training set. If internal validation or verification was performed during development, this information is not detailed in the provided text.

§ 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).