Publications by authors named "Alessia Pennacchi"
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.
Despite advancements in multidisciplinary care, oncologic outcomes of oral cavity squamous cell carcinoma (OSCC) have not substantially improved: still, one-third of patients affected by stage I and II can develop locoregional recurrences. Imaging plays a pivotal role in preoperative staging of OSCC, providing depth of invasion (DOI) measurements. However, locoregional recurrences have a strong association with adverse histopathological factors not included in the staging system, and any imaging features linked to them have been lacking.
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- * A study of 76 patients showed that 53% exhibited peritumoral depapillation, which was significantly correlated with PNI rates (54% vs. 28% for those without depapillation).
- * Narrow-band imaging (NBI) performance was not affected by depapillation in terms of intralesional pattern detection or surgical resection margins.