No abstract

Treating head and neck tumors has undergone significant advancements, focusing on improving the patient's quality of life after treatment. Reconstructive surgical techniques with free flaps have been vital in restoring anatomy, function, and aesthetics, reducing morbidity from locoregional treatments. Monitoring free flaps is crucial to detect and address any vascular compromise that may lead to flap failure. Various monitoring techniques have been employed in free flap monitoring. However, standardizing them presents a challenge due to the need for more consensus among surgeons and variability in techniques, costs, and training requirements. Artificial intelligence (AI) shows promise in standardizing monitoring practices and reducing the operator-dependent variability. AI techniques have been explored to improve monitoring and early detection of complications in free flap surgeries, and they have shown high accuracy in analyzing images and predicting flap outcomes. Despite the potential benefits, implementing AI in free flap monitoring remains challenging. Standardization of input data, interpretation, cost, training, and accounting for patient and flap variability are crucial considerations. Further research, including multicenter studies, validation, and collaboration amongst clinicians, researchers, and AI experts is needed to overcome these challenges.

Background and Objectives The current evidence regarding complications after salvage neck dissection (ND) for isolated regional recurrences (IRRs) in head and neck cancers is poor. The aim of this study is to evaluate the incidence and differences in complication rates of salvage ND after primary surgery, radiotherapy, chemoradiotherapy, or combined treatments. Methods This was a multicentric retrospective study on 64 patients who underwent salvage ND for IRR in three Italian institutes between 2008 and May 2020. Results Complications were detected in 7 of the 34 patients (20.8%) and surgeons described difficult dissection in 20 patients (58.82%). Accidental vascular ligations or nervous injury during surgery were never detected. None of the variables analyzed were statistically significant in predicting the risk of complications, disease‐free survival, or overall survival. Conclusions IRR represents a rare entity among total relapses. The incidence of complications after salvage ND for IRR is higher than after primary surgery but at an acceptable rate in experienced hands. However, an adequate balance between functional and oncological outcomes is mandatory.

Early larynx cancer detection plays a crucial role in improving treatment outcomes and recent studies have shown promising results in using artificial intelligence for larynx cancer detection. Artificial intelligence also has the potential to enhance transoral larynx microsurgery. This narrative review summarizes the current evidence regarding its use in larynx cancer detection and potential applications in transoral larynx microsurgery. The utilization of artificial intelligence in larynx cancer detection with white light endoscopy and narrow-band imaging helps improve diagnostic accuracy and efficiency. It can also potentially enhance transoral larynx microsurgery by aiding surgeons in real-time decision-making and minimizing the risk of complications. However, further prospective studies are warranted to validate the findings, and additional research is necessary to optimize the integration of artificial intelligence in our clinical practice.