Experimental Analysis of the Prediction Model Based on String Invariants

A new approach of the string theory called the Prediction Model Based on String Invariants (PMBSI) was applied here to time-series forecast. We used 2-end-point open string that satisfies the Dirichlet and Neumann boundary conditions. The initial motivation was to transfer modern physical ideas into the neighboring field called econophysics. The physical statistical viewpoint has proved to be fruitful, namely in the description of systems where many-body effects dominate. However, PMBSI is not limited to financial forecast. The main advantage of PMBSI include absence of the learning phase when large number of parameters must be set. Comparative experimental analysis of PMBSI vs. SVM was performed and the results on artificial and real-world data are presented. PMBSI performance was in a close match with SVM

Artificial Intelligence Aggregating Opinions of a Group of People

This study deals with the problems of aggregating the opinions of a group of people in such a way that the quality of the group decision surpasses the quality of the decision of the most experienced individual within the group. The methods we have studied fall in the research domain of the so called collective intelligence. We provide an overview of the state-of-the-art in the collective intelligence. We describe the method based on adaptive boosting we have proposed aggregatig the opinions of a group of people. We have implemented a web application to gather opinions of people and used the application to collect data for the experimental analysis. The model problem was to identify whether there is or there is not a tumor present in the series of X-ray images of human lungs. We have compared our proposed method to conventional methods such as majority voting. We have concluded that our proposed method can be successfully used to aggregate opinions of a group of people to increase their collective intelligence above the level of the most successful individual within the group in many cases. We have observed that the highest increase in the collective intelligence may be achieved for intelligence wise homogeneous groups what confirms the results of previous studies

Advanced Prototype of Manus Diagnostics and Rehabilitation Device

Hand fine motor functions may be impaired by various conditions, from injury to neurodegenerative diseases. Previously, we developed a prototype called Rehapiano that used load cells to measure the force exerted by the individual fingers. Rehapiano could distinguish between Parkinson’s patients and healthy individuals by analysing the finger tremors. Based on the experiences with the prototype and consultations with experts, we developed a more advanced prototype, Rehabimano. We show here how we improved the ergonomics and electronics. In addition, we have performed experimental validation of the device and confirmed its ability to detect and measure frequencies of tremors. These results are a stepping stone for consecutive software development and pre-clinical trials

Artifact Detection in Lung Ultrasound: An Analytical Approach

Lung ultrasound is used to detect various artifacts in the lungs that support the diagnosis of different conditions. There is ongoing research to support the automatic detection of such artifacts using machine learning. We propose a solution that uses analytical computer vision methods to detect two types of lung artifacts, namely A- and B-lines. We evaluate the proposed approach on the POCUS dataset and data acquired from a hospital. We show that by using the Fourier transform, we can analyze lung ultrasound images in real-time and classify videos with an accuracy above 70%. We also evaluate the method’s applicability for segmentation, showcasing its high success rate for B-lines (89% accuracy) and its shortcomings for A-line detection. We then propose a hybrid solution that uses a combination of neural networks and analytical methods to increase accuracy in horizontal line detection, emphasizing the pleura

Lung Ultrasound Reduces Chest X-rays in Postoperative Care after Thoracic Surgery: Is There a Role for Artificial Intelligence?—Systematic Review

Background: Chest X-ray (CXR) remains the standard imaging modality in postoperative care after non-cardiac thoracic surgery. Lung ultrasound (LUS) showed promising results in CXR reduction. The aim of this review was to identify areas where the evaluation of LUS videos by artificial intelligence could improve the implementation of LUS in thoracic surgery. Methods: A literature review of the replacement of the CXR by LUS after thoracic surgery and the evaluation of LUS videos by artificial intelligence after thoracic surgery was conducted in Medline. Results: Here, eight out of 10 reviewed studies evaluating LUS in CXR reduction showed that LUS can reduce CXR without a negative impact on patient outcome after thoracic surgery. No studies on the evaluation of LUS signs by artificial intelligence after thoracic surgery were found. Conclusion: LUS can reduce CXR after thoracic surgery. We presume that artificial intelligence could help increase the LUS accuracy, objectify the LUS findings, shorten the learning curve, and decrease the number of inconclusive results. To confirm this assumption, clinical trials are necessary. This research is funded by the Slovak Research and Development Agency, grant number APVV 20-0232