Improving deep learning-based automatic cranial defect reconstruction by heavy data augmentation: From image registration to latent diffusion models.

Modeling and manufacturing of personalized cranial implants are important research areas that may decrease the waiting time for patients suffering from cranial damage. The modeling of personalized implants may be partially automated by the use of deep learning-based methods. However, this task suffers from difficulties with generalizability into data from previously unseen distributions that make it difficult to use the research outcomes in real clinical settings.

Assessment of novel electrophysiology simulator-a survey study.

Background: Invasive electrophysiology (EP) training requires intellectual skills related to the interpretation of intracardiac electrograms. The classic approach to the education of young electrophysiologists focused solely on theoretical knowledge and overseen procedures in patients as no real-life-like simulation of EP studies was available.

Objective: The purpose of this study was to assess a novel tool for EP training based on fully interactive, online simulator providing real clinical experience to the users.

Vision Transformer for Parkinson's Disease Classification using Multilingual Sustained Vowel Recordings.

Parkinson's disease (PD) is the 2 most prevalent neurodegenerative disease in the world. Thus, the early detection of PD has recently been the subject of several scientific and commercial studies. In this paper, we propose a pipeline using Vision Transformer applied to mel-spectrograms for PD classification using multilingual sustained vowel recordings.

End-to-end multimodal system for depression detection from online recordings.

Depression is one of the most occurring civilizational diseases. In this paper, we propose a new approach for detecting depression through the analysis of social media content using face analysis, emotion recognition neural networks, and speech processing. We utilized audio-visual analysis and acquired more than 605 features in the time domain.

Contactless Electrostatic Piloerection for Haptic Sensations.

In this project, we create artificial piloerection using contactless electrostatics to induce tactile sensations in a contactless way. Firstly, we design various high-voltage generators and evaluate them in terms of their static charge, safety and frequency response with different electrodes as well as grounding strategies. Secondly, a psychophysics user study revealed which parts of the upper body are more sensitive to electrostatic piloerection and what adjectives are associated with them.

Deep learning-based framework for automatic cranial defect reconstruction and implant modeling.

Background And Objective: This article presents a robust, fast, and fully automatic method for personalized cranial defect reconstruction and implant modeling.

Methods: We propose a two-step deep learning-based method using a modified U-Net architecture to perform the defect reconstruction, and a dedicated iterative procedure to improve the implant geometry, followed by an automatic generation of models ready for 3-D printing. We propose a cross-case augmentation based on imperfect image registration combining cases from different datasets.

Gender and Age Estimation Methods Based on Speech Using Deep Neural Networks.

The speech signal contains a vast spectrum of information about the speaker such as speakers' gender, age, accent, or health state. In this paper, we explored different approaches to automatic speaker's gender classification and age estimation system using speech signals. We applied various Deep Neural Network-based embedder architectures such as x-vector and d-vector to age estimation and gender classification tasks.

Prediction and Estimation of Parkinson's Disease Severity Based on Voice Signal.

This paper presents the possibilities of using speech signal processing, analysis and regression methods in the context of assessment of neurological state in Parkinson's disease patients up to 3 hours after taking medication which alleviates symptoms of the disease. The obtained results were used to create a system whose goals were the prognosis of values of selected acoustic parameters based on which it will be possible to further estimate a unified Parkinson's disease rating scale score. For the experiment, we used the recordings of the vowel /a/ of 27 patients who were recorded 5 times each at a certain time after levodopa intake.

Deep Learning Approach to Parkinson's Disease Detection Using Voice Recordings and Convolutional Neural Network Dedicated to Image Classification.

This study presents an approach to Parkinson's disease detection using vowels with sustained phonation and a ResNet architecture dedicated originally to image classification. We calculated spectrum of the audio recordings and used them as an image input to the ResNet architecture pre-trained using the ImageNet and SVD databases. To prevent overfitting the dataset was strongly augmented in the time domain.

Voice data mining for laryngeal pathology assessment.

The aim of this study was to evaluate the usefulness of different methods of speech signal analysis in the detection of voice pathologies. Firstly, an initial vector was created consisting of 28 parameters extracted from time, frequency and cepstral domain describing the human voice signal based on the analysis of sustained vowels /a/, /i/ and /u/ all at high, low and normal pitch. Afterwards we used a linear feature extraction technique (principal component analysis), which enabled a reduction in the number of parameters and choose the most effective acoustic features describing the speech signal.