Search Results

The TIPCAM®1 Rubina Video Endoscope System is intended to be used together with the camera control unit during diagnostic and/or surgical procedures when endoscopic video assistance is required. For use in all endoscopy and endoscopic surgery within the peritoneal and thoracic cavity, including gynecological and urological anatomy.

The TIPCAM®1 Rubina video endoscope is an integrated unit that includes a camera and an endoscope. The endoscope receives the illumination light from the light source by the light guide connector connected to the light source device. The illumination light is transferred to the distal end through the optical fiber bundle inside of the endoscope and illuminates the inside of the patient body through the illumination lens at the distal end. The endoscope receives the reflected light from the inner lumen of a patient by the objective lens at the distal end. The built-in dual CMOS sensors convert the light to an electrical signal, and the signal is sent to the camera control unit (Image1 S Connect II + 4U-Link) via the attached cable for processing and display.

The provided text, a 510(k) summary for the TIPCAM®1 Rubina Video Endoscope System, does not contain information about the acceptance criteria and the study that proves the device meets the acceptance criteria in the context of an Artificial Intelligence (AI) or machine learning (ML) enabled device.

The document describes a traditional 510(k) submission for an endoscope system, where the substantial equivalence to a predicate device (SPIES 3D System) is established through non-clinical performance data (bench testing) of the hardware components. The "Technological Characteristics" section mentions "two image viewing features: A 2D auto-leveling (autorotation) mode... and a 2D Auto-switch mode...", which are described as features of the subject device itself, not an external AI/ML system assisting it.

The non-clinical performance data section lists various tests like "Color Reproduction and Color Contrast Enhancement," "Distortion," "Depth of Field," "Spatial Resolution," etc., which are standard tests for video endoscope systems' optical and image quality. These are not acceptance criteria related to the performance of an AI/ML algorithm.

Therefore, I cannot provide the requested information about acceptance criteria for an AI/ML device and the study proving it meets those criteria based on this document. The document explicitly states: "Clinical testing was not required to demonstrate the substantial equivalence to the predicate device. Non-clinical bench testing was sufficient to assess safety and effectiveness and to establish the substantial equivalence of the modifications." This indicates that the device's clearance was based on hardware performance and established equivalence, not on the performance of an AI/ML component assessed via a clinical or reader study.

Ask a specific question about this device