Application of contemporary computer methods in laryngeal cancer diagnosis and treatment

Abstract

Early detection of disease and accurate assessment of its extent are of paramount importance for the course of treatment and prognosis of larynx cancer. Machine learning and artificial intelligence tools have the potential to accelerate and improve diagnostic procedures in medicine, as well as to predict disease outcomes and response to specific therapies. Computer algorithms can analyze two-dimensional images obtained during procedures such as laryngeal spectroscopy and endoscopy. Radiological images can be evaluated using appropriate algorithms to determine whether the laryngeal tissue is benign or malignantly altered. In recent years, machine learning tools have been developed to determine the precise radiation doses, predict tumor radiosensitivity, as well as the possibility and severity of complications based on radiological image analysis. In the field of pathology, significant progress has been made by creating digital records of histopathological preparations, which can be further analyzed. This allows changes in intercellular interaction and tissue architecture that cannot be detected by conventional microscopic methods to be identified. With innovative computer techniques, it is possible to quantify tissue and cell structure parameters, which are calculated based on mathematical formulas and used to measure structural homogeneity and uniformity in both normal and pathologically altered tissue. Future multidisciplinary research aimed at developing new and innovative biosensors for the detection of discrete morphological changes characteristic of squamous cell carcinoma of the larynx will make a significant contribution to the advancement of diagnosis and treatment in the field of otolaryngology. In the future, the use of artificial intelligence and machine learning could enable the fusion of algorithms that combine data obtained from radiological, endoscopic, and histopathological findings, which could significantly increase the accuracy and precision of diagnosis, facilitate the process of deciding on therapeutic options, and improve the success rate of larynx cancer treatment.

Keywords

• laryngeal cancer
• artificial intelligence
• machine learning