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The assessment of early laryngeal cancer and pre-neoplastic lesions is subjective and depends on doctors' experience, leading to missed diagnoses in primary institutions. Our objective was to develop and validate a deep learning algorithm for the real-time identification of early laryngeal cancer and pre-neoplastic lesions, aiming to enhance diagnostic accuracy. The challenge observed in the domain of deep learning arises from overlooking contextual information. In response, we introduce in this paper a learning methodology that advances from acknowledging the surrounding context to integrating it, providing a resolution to this problem. Initially, we introduce side-aware features to capture relevant characteristics. Subsequently, we employ a rectangular selection technique for accurately determining regions of interest. To assess the effectiveness of our approach in object detection, we perform evaluations on a clinical dataset. Our deep learning approach exhibits robust performance in discriminating cancer. The images were randomly divided into training (80%), testing (10%), and validation (10%) sets. The testing was performed on a laryngoscope dataset consisting of 1123 samples. When compared with other advanced detection models, our methodology surpassed them, demonstrating superior results in laryngoscope detection, including mAP, accuracy, recall, and F1 score. In this study, we identified a learning method conducive to polyp detection in video laryngoscopy under both white-light and narrow-band imaging. The promising detection performance holds the potential to improve diagnostic proficiency and decrease the likelihood of missed diagnoses among primary otolaryngologists. [ABSTRACT FROM AUTHOR]

Background: Mucormycosis is a rare opportunistic invasive fungal disease. Rhinocerebral mucormycosis (RCM) is clinically difficult to diagnose, and patients often die due to delayed diagnosis., Case Description: A patient with concurrent pulmonary aspergillosis was diagnosed with RCM caused by Rhizopus through metagenomic Next-Generation Sequencing (mNGS). Despite comprehensive treatment including surgery, amphotericin B, and posaconazole, the patient tragically passed away. The treatment was delayed due to repeated cultures of secretions were negative and pathological examination could not clarify which fungus is infected., Conclusion: The clinical manifestations of RCM are not specific in the early stage, but the infection progresses rapidly. Therefore, early and accurate diagnosis is very important. mNGS is helpful for patients suspected of RCM, especially when conventional microbiology tests were negative., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Wang, Huang, Ma and Fan.)