Automating medical simulations.

Objective: This study aims to explore speech as an alternative modality for human activity recognition (HAR) in medical settings. While current HAR technologies rely on video and sensory modalities, they are often unsuitable for the medical environment due to interference from medical personnel, privacy concerns, and environmental limitations. Therefore, we propose an end-to-end, fully automatic objective checklist validation framework that utilizes medical personnel's uttered speech to recognize and document the executed actions in a checklist format.

Modelling the resonant frequency associated with the spatio-temporal evolution of the bone-dental implant interface.

Dental implant stability is greatly affected by the mechanical properties of the bone-implant interface (BII), and it is key to long-term successful osseointegration. Implant stability is often evaluated using the Resonant Frequency Analysis (RFA) method, and also by the quality of this interface, namely the bone-implant contact (BIC). True to this day, there is a scarcity of models tying BIC, RFA and a spatially and mechanically evolving BII.

Automatic segmentation, classification, and follow-up of optic pathway gliomas using deep learning and fuzzy c-means clustering based on MRI.

Purpose: Optic pathway gliomas (OPG) are low-grade pilocytic astrocytomas accounting for 3-5% of pediatric intracranial tumors. Accurate and quantitative follow-up of OPG using magnetic resonance imaging (MRI) is crucial for therapeutic decision making, yet is challenging due to the complex shape and heterogeneous tissue pattern which characterizes these tumors. The aim of this study was to implement automatic methods for segmentation and classification of OPG and its components, based on MRI.