The larynx, or the voice-box, is a common site of occurrence of Head and Neck cancers. Yet, automated segmentation of the larynx has been receiving very little attention. Segmentation of organs is an essential step in cancer treatment-planning. Computed Tomography scans are routinely used to assess the extent of tumor spread in the Head and Neck as they are fast to acquire and tolerant to some movement.This paper reviews various automated detection and segmentation methods used for the larynx on Computed Tomography images. Image registration and deep learning approaches to segmenting the laryngeal anatomy are compared, highlighting their strengths and shortcomings. A list of available annotated laryngeal computed tomography datasets is compiled for encouraging further research. Commercial software currently available for larynx contouring are briefed in our work.We conclude that the lack of standardisation on larynx boundaries and the complexity of the relatively small structure makes automated segmentation of the larynx on computed tomography images a challenge. Reliable computer aided intervention in the contouring and segmentation process will help clinicians easily verify their findings and look for oversight in diagnosis. This review is useful for research that works with artificial intelligence in Head and Neck cancer, specifically that deals with the segmentation of laryngeal anatomy.

Less than 10% of the patients with plasma cell neoplasms present with a solitary plasmacytoma. Though the nasal cavity is a common extramedullary site, the occurrence is extremely rare. Two cases of solitary extramedullary plasmacytoma of the sinonasal region are reported. The first of which is sinonasal plasmacytoma with concomitant HIV, an association that has been reported rarely in literature to date and is matter of much debate. In the second case report, we present an instance of surgical excision of the tumor using KTP 532 laser. The diagnosis was established using immunohistochemical techniques and multiple myeloma workups were negative in all cases.

Leiomyoma of nasal cavity is an extremely rare and unusual benign mass that uncommonly arises from the nasal septum. These are vascular or nonvascular masses of myogenic origin. We present a rare case of leiomyoma arising from the nasal septum and discuss its management using KTP 532 laser.

Otoscopy is a diagnostic procedure to visualize the external ear canal and eardrum, facilitating the detection of various ear pathologies and conditions. Timely otoscopy image classification offers significant advantages, including early detection, reduced patient anxiety, and personalized treatment plans. This paper introduces a novel OTONet framework specifically tailored for otoscopy image classification. It leverages octave 3D convolution and a combination of feature and region-focus modules to create an accurate and robust classification system capable of distinguishing between various otoscopic conditions. This architecture is designed to efficiently capture and process the spatial and feature information present in otoscopy images. Using a public otoscopy dataset, OTONet has reached a classification accuracy of 99.3% and an F1 score of 99.4% across 11 classes of ear conditions. A comparative analysis demonstrates that OTONet surpasses other established machine learning models, including ResNet50, ResNet50v2, VGG16, Dense-Net169, and ConvNeXtTiny, across various evaluation metrics. The research’s contribution to improved diagnostic accuracy reduced human error, expedited diagnostics, and its potential for telemedicine applications.