Coronavirus disease 2019 (COVID-19) is likely to have long-term mental health effects on individuals who have recovered from COVID-19. According to Centers for Disease Control and Prevention (CDC), individuals diagnosed with COVID-19 can see a range of long-term side effects. The aim of the study was to evaluate the effectiveness of neurotherapy (EEG neurofeedback and goal-oriented cognitive training) in the treatment of neurocognitive dysfunctions in a patient after the infection of SARS-CoV-2 and the long long-term side effects after the contraction of COVID-19. The 48-year-old woman ZR, an accountant by profession, an employee of the administration of the Municipal Board of Municipal Resources, fell ill on October 13, 2020. The disease began with very severe burning headache, eyeballs pain, muscle aches. Ten days later more symptoms joined: loss of smell (anosmia) and loss of taste (ageusia), hearing disorders, shortness of breath and chest pains. The symptoms were associated with SARS-CoV-2 coron- avirus infection confirmed by an rt-PCR genetic test. Brain MRI with intravenous paramagnetic contrast medium injection did not show either lesions of acute microischemic significance or areas of pathological enhancement after paramagnetic contrast medium ad- ministration. The patient was treated at home. In mid-November 2020, about a month after the infection of SARS-CoV-2 and con- tracting NeuroCOVID-19, neurocognitive impairment developed and after half a year she was deteriorating and not able to live in- dependently in society because of her condition. She called her problem "brain fog", and was referred for further diagnosis and therapy to the Reintegration and Training Center of the Polish Neuropsychological Society. We diagnosed a range of long-term side effects and introduced neurotherapy (EEG neurofeedback and goal-oriented cognitive training) in the treatment of neurocognitive dysfunctions. It was found that almost all the long-term side effects were reduced in magnitude. The patient improved and she was able to return to work. EEG neurofeedback and goal-oriented cognitive training might be helpful in the reduction of neurocognitive dysfunctions in patients following the infection of SARS-CoV-2 and long-term side effects after the contraction of COVID-19.

Objectives: The purpose of this paper is to assess the determination of male and female sex from trabecular bone structures in the pelvic region. The study involved analyzing digital radiographs for 343 patients and identifying fourteen areas of interest based on their medical significance, with seven regions on each side of the body for symmetry. Methods: Textural parameters for each region were obtained using various methods, and a thorough investigation of data normalization was conducted. Feature selection approaches were then evaluated to determine a small set of the most representative features, which were input into several classification machine learning models. Results: The findings revealed a sex-dependent correlation in the bone structure observed in X-ray images, with the degree of dependency varying based on the anatomical location. Notably, the femoral neck and ischium regions exhibited distinctive characteristics between sexes. Conclusions: This insight is crucial for medical professionals seeking to estimate sex dependencies from such image data. For these four specific areas, the balanced accuracy exceeded 70%. The results demonstrated symmetry, confirming the genuine dependencies in the trabecular bone structures.

The aim of this study was to establish a relationship between the textural parameters observed in X-ray images of bones and the age of the individual. The study utilized a meticulous visual analysis of the images to identify significant correlations between textural features and age. Five distinct regions of interest, namely the Wing of the Ilium, Neck of the Femur, Greater Trochanter, Ischium, and Shaft of the Femur, were identified on both sides of the body. Textural parameters were then measured for each of these regions. The left femoral neck showed the most noteworthy associations, with the textures generated from the histogram of oriented gradients and gray-level co-occurrence matrix exhibiting the strongest correlations (ρ −0.52, p-value 4.95 × 10⁻¹⁴). The main finding of the current study is that correlation of age-dependent bone structure differences in the femoral neck area is higher than in other structures of the femur. This proposed methodology has the potential to aid in the early detection of osteoporosis, which is crucial for devising treatment plans and identifying potential risks associated with bone fragility.

This study tackles the challenge of automatically estimating age from pelvis radiographs. Furthermore, we aim to develop a methodology for applying artificial intelligence to classify or regress medical imagery data. Our dataset comprises 684 pelvis X-ray images of patients, each accompanied by annotations and masks for various regions of interest (e.g., the femur shaft). Radiomic features, e.g., the co-occurrence matrix, were computed to characterize the image content. We assessed statistical analysis, machine learning, and deep learning methods for their effectiveness in this task. Correlation analysis indicated that using certain features in specific regions of interest is promising for accurate age estimation. Machine learning models demonstrated that when using uncorrelated features, the optimal mean absolute error (MAE) for age estimation is 5.20, whereas when employing convolutional networks on the texture feature maps yields the best result of 9.56. Automatically selecting radiomic features for machine learning models achieves a MAE of 7.99, whereas utilizing well-known convolutional architectures on the original image results in a system efficacy of 7.96. The use of artificial intelligence in medical data analysis produces comparable outcomes; however, when dealing with a large number of descriptors, selecting the most optimal ones through statistical analysis enables the identification of the best solution quickly.