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Pioneering nature of this work covers the answers to two questions: (1) Is an up to date anatomical model of the larynx needed for modern endoscopic diagnosis, and (2) If such a digital segmentation model can be utilized for deep learning purposes. The idea presented in this article has never been proposed before, and it is a breakthrough in numerical approaches to aerodigestive videoendoscopy imaging. The approach described in this article assumes defining a process for data acquisition, integration, and segmentation (labeling), for the needs of a new branch of knowledge: digital medicine and digital diagnosis support expert systems. The first and crucial step of such a process is creating a digital model of the larynx, which has to be then validated utilizing multiple clinical, as well as technical metrics. The model will form the basis for further artificial intelligence (AI) requirements, and it may also contribute to the development of translational medicine.

The role of the oral and oropharyngeal microbiota is important in the promoting and the development of cancers in the head and neck area, as discussed in detail in this paper. The subject of this work is to collect scientific information on the importance of the gut-respiratory axis in the promotion, development, and treatment outcomes of head and neck cancers (HNSCC). Scientific knowledge on HNSCC-gut microbiota interplay is rudimentary, therefore the use of knowledge-based on other malignant neoplasms was reported.

IntroductionDiscerning the preoperative nature of vocal fold leukoplakia (VFL) with a substantial degree of certainty is fundamental, seeing that the histological diagnosis of VFL includes a wide spectrum of pathology and there is no consensus on an appropriate treatment strategy or frequency of surveillance. The goal of our study was to establish a clear schedule of the diagnostics and decision-making in which the timing and necessity of surgical intervention are crucial to not miss this cancer hidden underneath the white plaque.Material and MethodsWe define a schedule as a combination of procedures (white light and Narrow Band Imaging diagnostic tools), methods of evaluating the results (a combination of multiple image classifications in white light and Narrow Band Imaging), and taking into account patient-related risk factors, precise lesion location, and morphology. A total number of 259 patients with 296 vocal folds affected by leukoplakia were enrolled in the study. All patients were assessed for three classifications, in detail according to Ni 2019 and ELS 2015 for Narrow Band Imaging and according to Chen 2019 for white light. In 41 of the 296 folds (13.9%), the VFL specimens in the final histology revealed invasive cancer. We compared the results from the classifications to the final histology results.ResultsThe results showed that the classifications and evaluations of the involvement of anterior commissure improve the clinical utility of these classifications and showed improved diagnostic performance. The AUC of this model was the highest (0.973) with the highest sensitivity, specificity, PPV, and NPV (90.2%, 89%, 56.9%, and 98.3%, respectively).ConclusionThe schedule that combines white light and Narrow Band Imaging, with a combination of the two classifications, improves the specificity and predictive value, especially of anterior commissure involvement.