Publications by authors named "I Vilaseca"

Purpose Of Review: To summarize current evidence regarding the indication of adjuvant treatment after transoral laser microsurgery (TOLMS).

Recent Findings: Apart from well known risk factors, margins represent the key point in the decision-making. If margins are affected, additional treatment is mandatory.

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Objective: To assess the opinion, practices, and challenges of international key opinion leaders about two minimal invasive surgical techniques in supraglottic laryngeal tumours: transoral laser microsurgery (TLM) and the transoral robotic surgery (TORS).

Methods: Design of a questionnaire composed of seven sections and fifty questions covering descriptive data of participants, practitioners experience procedural sequences, considerations related to airways, feeding, and voice, intraoperative haemorrhage, postoperative management, and a comparative analysis of TLM and TORS in treating supraglottic laryngeal cancer.

Results: A total of 27 head and neck surgeons replied to the survey.

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Article Synopsis
  • During the SARS-CoV-2 pandemic, researchers investigated the occurrence of Bell's palsy among patients vaccinated against COVID-19 or those who had recovered from it.
  • The study analyzed data from the ENT emergency department in Catalonia from January 2018 to December 2021 and found a total of 247 Bell's palsy cases among over 22,000 visits.
  • The results indicated no significant increase in Bell's palsy incidence during the pandemic years compared to the pre-pandemic years, suggesting no link between the condition and COVID-related factors.
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Introduction: Anti-IgLON5 disease is a recently described neurological disorder with multisystemic features. The disease is characterized by the presence of IgLON5 antibodies in serum and cerebrospinal fluid. Our objective is to describe in detail the otorhinolaryngological manifestations of this disease, which are frequent and may include dysphagia, dysarthria, vocal cord paralysis and laryngospasm.

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Article Synopsis
  • The paper explores the use of AI for automatic selection of important frames during laryngoscopy, enhancing data extraction, storage, and review processes.
  • Various deep learning models were trained on over 5,000 laryngoscopy images and tested in real-time settings, particularly focusing on performance using ResNet-50.
  • The model showed high precision and recall rates (95% precision and 97% recall), indicating its strong potential for use in clinical scenarios to assist otolaryngologists in improving diagnostic accuracy.
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