Belmokeddem, Mohamed2026-02-162026-02-162025-05-15https://dspace.univ-tlemcen.dz/handle/112/25724During minimally invasive surgery, the laparoscope lens is often contaminated by surgical artifacts such as blood, smoke, and fog. This project aims to develop a software solution for automatically detecting these artifacts to trigger the cleaning of the laparoscope’s optical surface and propose a hardware solution for optimal cleaning. Concerning the software, two artificial intelligence-based models are developed: a machine-learning method using a cascaded support vector machine and a deep learning model with ResNet-50. The Laparoscopic Video Quality database is used for training, testing, and validating the results. The deep-learning approach demonstrates superior accuracy, achieving detection rates of 97.5% for defocus blur, 97.5% for motion blur, and 85% for smoke. However, the machine-learning approach excels in inference speed, reaching 37 frame by second (FPS), making it better suited for real-time applications on low-cost systems. For the hardware component, we used Computer-Aided Design (CAD) to develop a 3D model of the laparoscopic lens-cleaning device; featuring four nozzles arranged in two pairs positioned 120 degrees apart around the lens. Each pair includes two side-by-side nozzles, one for a physiological saline-based cleaning liquid and the other for a CO₂-based drying gas. A 2D cleaning simulation confirms the efficiency of the proposed prototype.enMinimally invasive surgerySurgical artifactsArtificial intelligenceMachine learningDeep learningLens contaminationLens cleaning systemComputer-Aided Design3D modeling.Thesis