A sinus pneumocele is a rare entity caused by obstruction of a paranasal sinus ostium. It is characterised by dilation and expansion of the sinus, with subsequent bony erosion. The most probable mechanism is air trapping in the paranasal sinus, via a one-way valve mechanism. The case presented concerns a 68-year-old Caucasian man, with recurrent episodes of acute rhinosinusitis. Clinical examination and subsequent imaging of the face, revealed a large pneumocele of the right frontal sinus that significantly eroded the posterior sinus wall. A large mucocele of the right maxillary sinus was also noted, extending to the middle meatus, causing full obstruction of the ostiomeatal complex. Endoscopic sinus surgery was performed, the mucocele was removed and the pneumatisation pathway of the frontal sinus was restored. The patient reports full resolution of symptoms and shows no evidence of recurrence, 6 months postoperatively.

Voice loss constitutes a crucial disorder which is highly associated with social isolation. The use of multimodal information sources, such as, audiovisual information, is crucial since it can lead to the development of straightforward personalized word prediction models which can reproduce the patient’s original voice. In this work we designed a multimodal approach based on audiovisual information from patients before loss-of-voice to develop a system for automated lip-reading in the Greek language. Data pre-processing methods, such as, lip-segmentation and frame-level sampling techniques were used to enhance the quality of the imaging data. Audio information was incorporated in the model to automatically annotate sets of frames as words. Recurrent neural networks were trained on four different video recordings to develop a robust word prediction model. The model was able to correctly identify test words in different time frames with 95% accuracy. To our knowledge, this is the first word prediction model that is trained to recognize words from video recordings in the Greek language.

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