Quantitative Evaluation of the Pore and Window Sizes of Tissue Engineering Scaffolds on Scanning Electron Microscope Images Using Deep Learning.

The morphological characteristics of tissue engineering scaffolds, such as pore and window diameters, are crucial, as they directly impact cell-material interactions, attachment, spreading, infiltration of the cells, degradation rate and the mechanical properties of the scaffolds. Scanning electron microscopy (SEM) is one of the most commonly used techniques for characterizing the microarchitecture of tissue engineering scaffolds due to its advantages, such as being easily accessible and having a short examination time. However, SEM images provide qualitative data that need to be manually measured using software such as ImageJ to quantify the morphological features of the scaffolds.

Engineering periodontal tissue interfaces using multiphasic scaffolds and membranes for guided bone and tissue regeneration.

Periodontal diseases are one of the greatest healthcare burdens worldwide. The periodontal tissue compartment is an anatomical tissue interface formed from the periodontal ligament, gingiva, cementum, and bone. This multifaceted composition makes tissue engineering strategies challenging to develop due to the interface of hard and soft tissues requiring multiphase scaffolds to recreate the native tissue architecture.