Search Results

This 3D Visualization System is intended to compose the imaging signals from video system center and convert them into 3D signals displayed on the monitor.

The 3D Visualization System can convert 2D endoscopic images synchronously. It is equipped HD-SDI and HDMI outputs ports which are compatible with 3D monitors of various interfaces. The 3DVS-S100 series 3D Visualization System includes 5 models, which are 3DVS-S100A, 3DVS-S100B, 3DVS-S100C, 3DVS-S100D and 3DVS-S100E. The differences between the models are in the number and type of imaging modes supported (single-lens endoscope with enhanced or standard 3D effects, and dual-lens endoscope with enhanced or standard 3D effects). The system should be used with endoscopic image processors which have HDMI or SDI output interface, and monitors which have SDI, HDMI or DVI interface. The device is provided non-sterile and for repeat use, does not have patient-contact, and is intended for use by a qualified healthcare professional and is not for home use.

The provided document describes the Scivita Medical Technology Co., Ltd. 3D Visualization System (K183675). The document clarifies that this device is intended to process imaging signals from a video system center and convert them into 3D signals for display on a monitor. The FDA's 510(k) clearance process focuses on substantial equivalence to a predicate device, rather than explicit acceptance criteria with numerical performance targets for the proposed device itself. However, the document does describe non-clinical testing conducted to demonstrate this equivalence and ensure the device meets design specifications.

Here's an analysis of the acceptance criteria and study information provided:

1. Table of Acceptance Criteria and Reported Device Performance

• IEC 60601-1-2005+CORR.1:2006+CORR.2:2007+A1:2012 (General requirements for basic safety and essential performance)
• IEC 60601-1-2:2014 (Electromagnetic compatibility)
• IEC 60601-2-18:2009 (Particular requirements for endoscopic equipment) |
  | Software Validation | The software was validated in accordance with FDA guidance documents:
• "Guidance for the Content of Premarket Submissions for Software Contained in Medical Devices"
• "Off-The-Shelf Software Use in Medical Devices"
• "Cybersecurity for Networked Medical Devices Containing Off-the-Shelf (OTS) Software"
• "General Principles of Software Validation" |
  | Image Quality Equivalence (2D and 3D) | Image quality performance tests were conducted to quantitatively compare the proposed device and predicate devices for both 2D and 3D images.
  Parameters evaluated:
• Field of view
• Direction of view
• Depth of field
• Geometric distortion
• Noise and dynamic range
• Intensity uniformity
• Artifacts
• Image frame frequency and system delay

Result: The image quality of the proposed device was equivalent to that of the predicate device (OLYMPUS LTF-190-10-3D ENDOEYE FLEX 3D DEFLECTABLE VIDEOSCOPE, MAJ-YO154 3D PROCESSOR, OLYMPUS CV-190, EVIS EXERA III VIDEO SYSTEM CENTER - K123365). This equivalence was tested across all four modes of the proposed device in both described combinations. |
| Substantial Equivalence to Predicate Device (K123365) | The non-clinical performance testing summarized supported a substantial equivalence determination, demonstrating the subject device is as safe and as effective as the legally marketed predicate device. |

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

The document does not specify the sample size (e.g., number of images, cases, or videos) used for the image quality performance tests. It vaguely states "image quality performance tests were conducted to quantitatively compare the proposed device and predicate devices."

The data provenance (country of origin, retrospective/prospective) is also not mentioned. Given the manufacturer is based in China, it's plausible the testing was conducted there, but this is not explicitly stated.

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

The document does not mention the use of experts or any process for establishing ground truth as typically understood in a clinical study (e.g., for diagnostic accuracy). The testing described is purely technical and comparative against a predicate device's performance characteristics.

4. Adjudication Method for the Test Set

As no expert review or human assessment of diagnostic accuracy is mentioned, there is no adjudication method described.

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

No MRMC comparative effectiveness study is mentioned. The study described is a non-clinical, technical performance comparison between the proposed device and a predicate device, focusing on image quality characteristics, not on human reader performance with or without AI assistance. The device's function is to convert existing video signals into 3D signals, not to provide AI-assisted diagnostic capabilities.

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

Yes, a form of standalone performance was assessed. The "image quality performance tests" were conducted on the device itself, comparing its output directly to the predicate device's output based on various technical image parameters. This is an evaluation of the algorithm's output (3D visualization) in isolation from human interpretation for diagnostic purposes.

7. Type of Ground Truth Used

For the non-clinical image quality tests, the "ground truth" was implicitly derived from technical performance metrics of the predicate device and established engineering standards for image quality. It was a comparative measurement against the performance characteristics of the legally marketed predicate device, not against clinical outcomes, pathology, or expert consensus on a diagnostic task.

8. Sample Size for the Training Set

The document does not mention a training set. This device is a "3D Visualization System" that converts video signals. It does not appear to be an AI/ML-driven diagnostic or image analysis tool that would typically involve a "training set" in the machine learning sense. Its function is signal processing and conversion.

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

As no training set is mentioned, this section is not applicable.

Ask a specific question about this device