Artificial intelligence-assisted determination of available sites for palatal orthodontic mini implants based on palatal thickness through CBCT.

OBJECTIVES: To develop an artificial intelligence (AI) system for automatic palate segmentation through CBCT, and to determine the personalized available sites for palatal mini implants by measuring palatal bone and soft tissue thickness according to the AI-predicted results. MATERIALS AND METHODS: Eight thousand four hundred target slices (from 70 CBCT scans) from orthodontic patients were collected, labelled by well-trained orthodontists and randomly divided into two groups: a training set and a test set. After the deep learning process, we evaluated the performance of our deep learning model with the mean Dice similarity coefficient (DSC), average symmetric surface distance (ASSD), sensitivity (SEN), positive predictive value (PPV) and mean thickness percentage error (MTPE). The pixel traversal method was proposed to measure the thickness of palatal bone and soft tissue, and to predict available sites for palatal orthodontic mini implants. Then, an example of available sites for palatal mini implants from the test set was mapped. RESULTS: The average DSC, ASSD, SEN, PPV and MTPE for the segmented palatal bone tissue were 0.831%, 1.122%, 0.876%, 0.815% and 6.70%, while that for the palatal soft tissue were 0.741%, 1.091%, 0.861%, 0.695% and 12.2%, respectively. Besides, an example of available sites for palatal mini implants was mapped according to predefined criteria. CONCLUSIONS: Our AI system showed high accuracy for palatal segmentation and thickness measurement, which is helpful for the determination of available sites and the design of a surgical guide for palatal orthodontic mini implants.

Nanomedicine for cancer targeted therapy with autophagy regulation.

Nanoparticles have unique physical and chemical properties and are currently widely used in disease diagnosis, drug delivery, and new drug development in biomedicine. In recent years, the role of nanomedical technology in cancer treatment has become increasingly obvious. Autophagy is a multi-step degradation process in cells and an important pathway for material and energy recovery. It is closely related to the occurrence and development of cancer. Because nanomaterials are highly targeted and biosafe, they can be used as carriers to deliver autophagy regulators; in addition to their favorable physicochemical properties, nanomaterials can be employed to carry autophagy inhibitors, reducing the breakdown of chemotherapy drugs by cancer cells and thereby enhancing the drug's efficacy. Furthermore, certain nanomaterials can induce autophagy, triggering oxidative stress-mediated autophagy enhancement and cell apoptosis, thus constraining the progression of cancer cells.There are various types of nanoparticles, including liposomes, micelles, polymers, metal-based materials, and carbon-based materials. The majority of clinically applicable drugs are liposomes, though other materials are currently undergoing continuous optimization. This review begins with the roles of autophagy in tumor treatment, and then focuses on the application of nanomaterials with autophagy-regulating functions in tumor treatment.