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. METHODS: EP simulator users access a system with simulated electrocardiogram, mimicking signals recorded by a catheter. Assessment of EP simulator by 40 electrophysiologists from 16 countries was collected via online questionnaire. RESULTS: The realism of ECG signals was described as excellent or very good by 90% of responders, of intracardial signals by 82.5%. Realism of signal interactions and user experience was judged as excellent or very good by 75% and 70% accordingly. One hundred percent of users agree definitely or mostly that EP Simulator helps to translate theoretical into practical knowledge. Of responders, 97.5% would include it in EP training programs as it is extremely or very useful for training purposes in the opinion of 87.5%. Of responders, 72.5% think that training on EP simulator can potentially reduce the rate of complications. In 87.5%, the overall experience was completely or mostly satisfying and would be recommended by 100% of responders. CONCLUSION: EP simulator is a feasible tool for training of young electrophysiologist, and it may be potentially included in the cardiologist curriculum. We should particularly emphasize the positive respondents' assessment of EP simulator overall realism.

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. Those are fed to machine learning and deep learning models for depression classification. Our approach outperforms the other state-of-the-art models, achieving the F1-score 0.77. The results have the potential to provide valuable insights for mental health professionals, offer early detection and intervention, and serve as a resource for individuals seeking help with their mental health. This study enables real-time analysis and represents a significant advancement in mental health and technology and has the potential to impact society.Clinical relevance-The system aims to provide a fast and accurate way to detect depression in individuals through online recordings. The use of multimodal information (e.g. audio, image) enhances the performance of the non-verbal behavioral analysis. The end-to-end system reduces the need for manual analysis by mental health professionals and increases the efficiency of depression screening. The system can potentially help identify individuals who are at risk for depression, enabling early intervention and treatment. The results from the system can complement traditional assessments and support mental health professionals in making a diagnosis. The system can be used in real-time processing, f.e. during online calls, and provide objective measurements summarizing the overall behavior based on computer vision and audio analysis.

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

Parkinson's disease (PD) is the 2nd 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. Furthermore, our proposed transformed-based model shows a great potential to use voice as a single modality biomarker for automatic PD detection without language restrictions, a wide range of vowels, with an F1-score equal to 0.78. The results of our study fall within the range of the estimated prevalence of voice and speech disorders in Parkinson's disease, which ranges from 70-90%. Our study demonstrates a high potential for adaptation in clinical decision-making, allowing for increasingly systematic and fast diagnosis of PD with the potential for use in telemedicine.Clinical relevance- There is an urgent need to develop non invasive biomarker of Parkinson's disease effective enough to detect the onset of the disease to introduce neuroprotective treatment at the earliest stage possible and to follow the results of that intervention. Voice disorders in PD are very frequent and are expected to be utilized as an early diagnostic biomarker. The voice analysis using deep neural networks open new opportunities to assess neurodegenerative diseases' symptoms, for fast diagnosis-making, to guide treatment initiation, and risk prediction. The detection accuracy for voice biomarkers according to our method reached close to the maximum achievable value.

Towards clinical applicability and computational efficiency in automatic cranial implant design: An overview of the AutoImplant 2021 cranial implant design challenge.

Cranial implants are commonly used for surgical repair of craniectomy-induced skull defects. These implants are usually generated offline and may require days to weeks to be available. An automated implant design process combined with onsite manufacturing facilities can guarantee immediate implant availability and avoid secondary intervention. To address this need, the AutoImplant II challenge was organized in conjunction with MICCAI 2021, catering for the unmet clinical and computational requirements of automatic cranial implant design. The first edition of AutoImplant (AutoImplant I, 2020) demonstrated the general capabilities and effectiveness of data-driven approaches, including deep learning, for a skull shape completion task on synthetic defects. The second AutoImplant challenge (i.e., AutoImplant II, 2021) built upon the first by adding real clinical craniectomy cases as well as additional synthetic imaging data. The AutoImplant II challenge consisted of three tracks. Tracks 1 and 3 used skull images with synthetic defects to evaluate the ability of submitted approaches to generate implants that recreate the original skull shape. Track 3 consisted of the data from the first challenge (i.e., 100 cases for training, and 110 for evaluation), and Track 1 provided 570 training and 100 validation cases aimed at evaluating skull shape completion algorithms at diverse defect patterns. Track 2 also made progress over the first challenge by providing 11 clinically defective skulls and evaluating the submitted implant designs on these clinical cases. The submitted designs were evaluated quantitatively against imaging data from post-craniectomy as well as by an experienced neurosurgeon. Submissions to these challenge tasks made substantial progress in addressing issues such as generalizability, computational efficiency, data augmentation, and implant refinement. This paper serves as a comprehensive summary and comparison of the submissions to the AutoImplant II challenge. Codes and models are available at https://github.com/Jianningli/Autoimplant_II.

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. Finally, we combine an electrostatic generator to produce artificial piloerection on the nape with a head-mounted display, this device provides an augmented virtual experience related to fear. We hope that work encourages designers to explore contactless piloerection for enhancing experiences such as music, short movies, video games, or exhibitions.

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. Additional ablation studies compare different augmentation strategies and other state-of-the-art methods. RESULTS: We evaluate the method on three datasets introduced during the AutoImplant 2021 challenge, organized jointly with the MICCAI conference. We perform the quantitative evaluation using the Dice and boundary Dice coefficients, and the Hausdorff distance. The Dice coefficient, boundary Dice coefficient, and the 95th percentile of Hausdorff distance averaged across all test sets, are 0.91, 0.94, and 1.53 mm respectively. We perform an additional qualitative evaluation by 3-D printing and visualization in mixed reality to confirm the implant's usefulness. CONCLUSION: The article proposes a complete pipeline that enables one to create the cranial implant model ready for 3-D printing. The described method is a greatly extended version of the method that scored 1st place in all AutoImplant 2021 challenge tasks. We freely release the source code, which together with the open datasets, makes the results fully reproducible. The automatic reconstruction of cranial defects may enable manufacturing personalized implants in a significantly shorter time, possibly allowing one to perform the 3-D printing process directly during a given intervention. Moreover, we show the usability of the defect reconstruction in a mixed reality that may further reduce the surgery time.

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. The speech signal was parameterized, where in the acoustic parameters describing the signal were extracted and constituted input vectors to machine learning regression methods to search for characteristic diagnostic symptoms enabling automatic monitoring of the course of Parkinson's disease. The results of the acoustic analysis were correlated with the clinical description and disease severity was assessed using the unified Parkinson's disease rating scale. As a result, it was possible to create software which will support the work of the clinician in the field of therapy monitoring and provide a quantitative assessment of treatment results and a forecast of the effects of the therapy in short-term monitoring.

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. Furthermore, we have applied a transfer learning-based training scheme with pre-training the embedder network for a speaker recognition task using the Vox-Celeb1 dataset and then fine-tuning it for the joint age estimation and gender classification task. The best performing system achieves new state-of-the-art results on the age estimation task using popular TIMIT dataset with a mean absolute error (MAE) of 5.12 years for male and 5.29 years for female speakers and a root-mean square error (RMSE) of 7.24 and 8.12 years for male and female speakers, respectively, and an overall gender recognition accuracy of 99.60%.

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. The Parkinson's dataset (from PC-GITA database) consists of 100 patients (50 were healthy / 50 were diagnosed with Parkinson's disease). Each patient was recorded 3 times. The obtained accuracy on the validation set is above 90% which is comparable to the current state-of-the-art methods. The results are promising because it turned out that features learned on natural images are able to transfer the knowledge to artificial images representing the spectrogram of the voice signal. What is more, we showed that it is possible to perform a successful detection of Parkinson's disease using only frequency-based features. A spectrogram enables visual representation of frequencies spectrum of a signal. It allows to follow the frequencies changes of a signal in time.

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. We have also performed non-linear data transformation which was calculated using kernel principal components. The results of the presented methods for normal and pathological cases will be revealed and discussed in this paper. The initial and extracted feature vectors were classified using the k-means clustering and the random forest classifier. We found that reasonably good classification accuracies could be achieved by selecting appropriate features. We obtained accuracies of up to 100% for classification of healthy versus pathology voice using random forest classification for female and male recordings. These results may assist in the feature development of automated detection systems for diagnosis of patients with symptoms of pathological voice.