Hardware Complication Risks in Head and Neck Cancer Patients Undergoing Reconstructive Surgery With Segmental Mandibulectomy.

Background: The primary objective of this study was to ascertain the risk factors associated with hardware complications following segmental mandibular reconstruction in head and neck cancer patients. Additionally, we sought to develop a nomogram model that enables accurate risk prediction.

Methods: Patients who underwent segmental mandibulectomy with immediate free or local regional tissue transfer between January 2016 and December 2020 were reviewed.

Artificial intelligence-assisted grading for tear trough deformity.

Background: Various classification systems for tear trough deformity (TTD) have been published; however, their complexity can pose challenges in clinical use, especially for less experienced surgeons. It is believed that artificial intelligence (AI) technology can address some of these challenges by reducing inadvertent errors and improving the accuracy of medical practice. In this study, we aimed to establish a reliable and precise digital image grading model for TTD using smartphone-based photography enhanced using AI deep learning technology.