Femtosecond Laser Treatment of Ti Surfaces: Antibacterial Mechanisms and Deep Learning-Based Surface Recognition

Abstract

Bacterial infections have been demonstrated to cause the premature failure of implants. A reliable strategy for preserving biocompatibility is to physically modify the implant surface, without using chemicals, to prevent bacterial adhesion. This study employed femtosecond laser processing to generate various laser-induced periodic surface structures on Ti substrates. The antibacterial properties and osteoblast adhesion characteristics of these surfaces were investigated. Gene expression profiles and transcriptomic data were compared before and after laser treatment, and high-throughput analysis was conducted to evaluate the antibacterial performance related to different surface modifications. A small data set of Ti surface scanning electron microscopy images was compiled, and a deep learning model was trained using transfer learning to facilitate surface recognition and classification. The results demonstrated that femtosecond laser treatment disrupted bacterial adhesion and the expression of adhesion-related genes on the Ti surface, with the laser-treated samples at 5.6 W and 500 mm/s exhibiting an antibacterial efficacy exceeding 60%. In addition, the optimized deep learning model, ResNet50-TL, accurately identified and classified the structures of Ti surfaces post-treatment.