Parkinson's disease is a progressive nervous system disorder that affects movement and muscle control. Lithuanian researchers from Kaunas University of Technology (KTU) utilized AI to identify the early signs of Parkinson's disease using voice data. The diagnosis of Parkinson's disease has shaken many lives, with over 10 million people currently living with the condition. Although there is no cure, early detection of symptoms can lead to better management of the disease. As the disease progresses, changes in speech can occur alongside other symptoms.

Lithuanian researcher from Kaunas University of Technology (KTU), Rytis Maskeliunas, together with colleagues from the Lithuanian University of Health Sciences (LSMU), tried to identify early symptoms of Parkinson's disease using voice data. Parkinson's disease is usually associated with loss of motor function – hand tremors, muscle stiffness, or balance problems. According to Maskeliunas, a researcher at KTU's Department of Multimedia Engineering, as motor activity decreases, so does the function of the vocal cords, diaphragm, and lungs: "Changes in speech often occur even earlier than motor function disorders, which is why the altered speech might be the first sign of the disease." According to Professor Virgilijus Ulozas, at the Department of Ear, Nose, and Throat at the LSMU Faculty of Medicine, patients with early-stage of Parkinson's disease, might speak in a quieter manner, which can also be monotonous, less expressive, slower, and more fragmented, and this is very difficult to notice by ear. As the disease progresses, hoarseness, stuttering, slurred pronunciation of words, and loss of pauses between words can become more apparent.

Researchers from Kaunas universities in Lithuania developed a deep learning-based method that can predict the possible onset of Alzheimer's disease from brain images with an accuracy of over 99 per cent. The method was developed while analysing functional MRI images obtained from 138 subjects and performed better in terms of accuracy, sensitivity and specificity than previously developed methods. According to World Health Organisation, Alzheimer's disease is the most frequent cause of dementia, contributing to up to 70 per cent of dementia cases. Worldwide, approximately 24 million people are affected, and this number is expected to double every 20 years. Owing to societal ageing, the disease will become a costly public health burden in the years to come.

In recent years Alzheimer's disease has been on the rise throughout the world and is rarely diagnosed at an early stage when it can still be effectively controlled. Using artificial intelligence, KTU researchers conducted a study to identify whether human-computer interfaces could be adapted for people with memory impairments to recognize a visible object in front of them. Rytis Maskeliūnas, a researcher at the Department of Multimedia Engineering at Kaunas University of Technology (KTU), considers that the classification of information visible on the face is a daily human function: "While communicating, the face "tells" us the context of the conversation, especially from an emotional point of view, but can we identify visual stimuli based on brain signals?" The visual processing of the human face is complex. Information such as a person's identity or emotional state can be perceived by us, analyzing the faces.

Time-lapse images are taken to allow embryologists to track how well an embryo is developing, but manual analysis of these images is time-consuming. AI tools have been developed that analyse these images to classify embryos as good or poor quality, but these tools do not work well with the poor quality of many time-lapse images. Time-lapse imaging, whereby regular images are taken of the embryo, is used to improve assessment by providing the embryologist with more information, however analysing this information is time consuming and often involves analysing multiple images of an embryo taken at the same time. To tackle this challenge researchers at Kaunas University of Technology decided to automate the fusion of time-lapse images taken of embryos, in order to create a better-quality image for analysis by embryologists. The resulting fused images were clearer than the individual images and the two embryologists who took part in the study found they were up to three times faster analysing the fused images than the separate images.

In the near future, the physicians will be able to monitor the patients at their homes using virtual reality tools, computer scientists are convinced. A team of researchers from Kaunas University of Technology (KTU), Lithuania, proposed a deep-learning-based method for the three-dimensional human shape reconstruction when the original figure is only partly visible. The main advantage of the method is its relatively low cost, high compression of the images obtained and easy integration with the existing virtual reality tools. The method was developed using a real-world dataset, the clinical trial is pending. The rapid advancements in computer vision and three-dimensional object representation enables the development of virtual reality tools and the expansion of their application sphere.

Summary: A new AI algorithm can predict the onset of Alzheimer's disease with an accuracy of over 99% by analyzing fMRI brain scans. Researchers from Kaunas University, Lithuania developed a deep learning-based method that can predict the possible onset of Alzheimer's disease from brain images with an accuracy of over 99 percent. The method was developed while analyzing functional MRI images obtained from 138 subjects and performed better in terms of accuracy, sensitivity, and specificity than previously developed methods. According to World Health Organisation, Alzheimer's disease is the most frequent cause of dementia, contributing to up to 70 percent of dementia cases. Worldwide, approximately 24 million people are affected, and this number is expected to double every 20 years.

Lithuanian Airports has integrated the Eddy Travels artificial intelligence (AI) assistant to help travelers plan their trips seamlessly. The AI assistant is now available on Vilnius Airport, Kaunas Airport, and Palanga Airport websites. Lithuania is one of the first countries globally to have an AI-powered travel assistant on national airport websites. A record number of new destinations will be offered by Lithuanian Airports this summer. Holiday seekers can choose from almost 80 direct flight routes from the country's airports.

With the increasing volume and impact of communication on social media, social media analysis has become one of the most trending topics in natural language research, which can be observed in a growing number of workshops and conferences dedicated to this topic, projects funded, and research centers established. As a result, a number of social media resources containing chats, online commentaries, reviews, blogs, emails, forums, etc., as well as audio and video recordings, have been accumulated in the repositories of CLARIN centers. What is more, due to their distinct communicative characteristics, they pose new technical challenges for the standard natural language processing tools as well as new legal and ethical challenges for the dissemination of such resources, which has also been addressed by CLARIN, making the available infrastructure an important means for attracting new users to the CLARIN community.

The aims of the CLARIN-PLUS workshop "Creation and Use of Social Media Resources" are: to demonstrate the possibilities of social media resources and natural language processing tools for researchers with a diverse research background who are interested in empirical research of language and social practices in computer-mediated communication; to promote interdisciplinary cooperation possibilities; to initiate a discussion on the various approaches to social media data collection and processing.