In this work, we demonstrate that it is possible to create supervised machine-learning models using a support vector machine and random forest algorithms to separate yeast cells exposed to hyperosmotic stress from intact cells. We performed fractal, gray level co-occurrence matrix (GLCM), and discrete wavelet transform analyses on digital micrographs of nuclear regions of interest of a total of 2000 Saccharomyces cerevisiae cells: 1000 exposed to hyperosmotic environments and 1000 control cells. For each nucleus, we calculated values for fractal dimension, angular second moment, inverse difference moment, textural contrast, correlation feature, textural variance, and discrete wavelet coefficient energy. The support vector machine achieved an acceptable classification accuracy of 71.7% in predicting whether the cell belonged to the experimental or control group. The random forest model performed better than the support vector machine, with a classification accuracy of 79.8%. These findings can serve as a starting point for developing AI-based methods that use GLCM, fractal, and wavelet data to classify damaged and healthy cells and make predictions about various physiological and pathological phenomena associated with osmotic stress.

Gray-level co-occurrence matrix (GLCM) and discrete wavelet transform (DWT) analyses are two contemporary computational methods that can identify discrete changes in cell and tissue textural features. Previous research has indicated that these methods may be applicable in the pathology for identification and classification of various types of cancers. In this study, we present findings that squamous epithelial cells in laryngeal carcinoma, which appear morphologically intact during conventional pathohistological evaluation, have distinct nuclear GLCM and DWT features. The average values of nuclear GLCM indicators of these cells, such as angular second moment, inverse difference moment, and textural contrast, substantially differ when compared to those in noncancerous tissue. In this work, we also propose machine learning models based on random forests and support vector machine that can be successfully trained to separate the cells using GLCM and DWT quantifiers as input data. We show that, based on a limited cell sample, these models have relatively good classification accuracy and discriminatory power, which makes them suitable candidates for future development of AI-based sensors potentially applicable in laryngeal carcinoma diagnostic protocols.

Auditory evoked potentials (AEPs) are electroencephalographic (EEG) responses to auditory stimuli and are frequently used to evaluate auditory processing and cognitive integrity. Interpretation of AEPs today predominantly relies on standard linear techniques such as time-domain averaging and frequency-domain spectral decomposition. These approaches may not always capture nonlinear, nonstationary, and scale-free characteristics of EEG signals; therefore, in contemporary neurophysiology research, there may be a need for the utilization of additional nonlinear frameworks. Fractal analysis may be a powerful tool for the quantification of subtle changes in EEG and AEP complexity, irregularity, and variability. This approach is often overlooked due to methodological and conceptual limitations but nevertheless holds significant potential in revealing alterations in geometrical and spatial complexity of AEPs under various physiological conditions. Here, we discuss potential applications and shortcomings of fractal AEP analysis, as well as its possible integration with supervised machine learning algorithms. We also focus on novel artificial intelligence-based concepts that could, in theory, utilize the power of fractal AEP and EEG analysis to improve the classification and prediction of neurophysiological processes and phenomena.

Anthropologic, artistic and medical significance of heart inspired usto undertake this multidisciplinary study. Amongst the 24 obtained echocardiograms and phonograms, 1 was used for a Photoshop processing. In addition, over 20,000 art work reproductions were examined in this study. Artistic and symbolic presentation of heart started some 15,000 years ago. First heart models were made by the Egyptian and Olmec civilisations. Ancient cultures regarded heart as the seat of the soul, spirit and intelligence. First anatomical and artistic images of heart were created by Leonardo da Vinci in the15th century, and first wax models by the Italian anatomists in the 17th century. Mediaeval religious symbolism of heart was replaced in the Renaissance and later on mainly by its role in the romantic love. Anatomical heart art continued in the 18th and 19th centuries through the works of Sénac, Cloquet, Hirschfeldand Bourgery. Some modern artists, such as Dalí, Kahlo, Rivera, Warhol, Ivanjicki, Vital, Kober and Mastrlova, created the anatomical heart images or sculptures, whereas some others, such as Duchamp, Klee, Miró, Matisse and Dine, presented heart symbol in their artworks. New radiologic technologies produce fine images of heart, some of which are similar to the works of modern artists. Heart biology and symbolism have had a tremendous influence on our culture, including art and medical sciences. New radiologic techniques and computer technology have produced such images of heart, which substantially improved diagnosis, but also enhanced the heart aesthetics.