Oculomotor activity (eye movements) is an essential component of visual data acquisition, analysis and use. The aim of this study was to determine the characteristics of oculomotor response to static images in primary school children with mild intellectual disability (ID). Our sample included a total of 49 schoolers (23 children with mild ID and 26 typically developing children). Oculomotor activity was evaluated using a GP3 Gazepoint eye tracker. The participants were presented with 15 visual stimuli: 10 pictorial and 5 mixed (pictures + text) static color images. Children with mild ID generated significantly fewer fixations (р = 0.038) than typically developing children. So, learning materials containing both pictorial and textual images are ineffective because textual elements are completely ignored by children with mild ID. The total duration of gaze fixations was significantly longer (р = 0.029) in typically developing children than in children with mild ID. However, the average duration of a single gaze fixation was longer in children with mild ID. The identified features of oculomotor response can help to optimize the format of instructional materials for primary school children with mild ID.
Cognitive and mnestic impairments have a significant negative impact on the quality of parkinsonian patients’ life. Memory impairment causes changes in the mechanisms of information processing. The aim of this study was to investigate the characteristics of transformations undergone by memorized visual and semantic content during memory consolidation and reconsolidation in patients with Parkinson’s disease. The study was conducted on 32 male patients with PD (ICD code: G20). Among the patients, 9 had rigidity/bradykinesia-d ominant PD, 11 had tremor-dominant PD, and 12 suffered from a mixed type of PD. Short-term memory span was assessed using the 10 words and the visual memory tests proposed by Luria. As stimulus materials we used a symbolic representation of the old Greek letter resembling an owl and a translated excerpt from a Canadian aboriginal epic. Regardless of the PD form, the quality of the memorized information was either altered or completely lost. The mechanisms underlying such transformations differed quantitively depending on the PD form. Transformation of the memorized information occurred in the conditions of both incidental and deliberate memorization and was represented by distortions (substitution of the original content with confabulations) and simplifications of the structural and semantic organization. We consolidated significantly lesser amount of auditory verbal (р = 0.018) and visual (p = 0.029) information. This trend was consistent with the pronounced distortion of content during its retrieval.
Sensory impairments (visual and auditory) reduce quantity and quality of the information input. The associated memory loss can be classified as intrinsic decline in memory functionalities or mere physiological effect of sensory deprivation. This study aimed to specify this issue by analyzing memory consolidation and reconsolidation processes in older people with sensory deficits. The study enrolled 65–75 year-old individuals (n = 61) distributed into four groups: patients with unilateral sensorineural hearing loss (n = 17); patients with bilateral sensorineural hearing loss (n = 14); patients with visual impairment (n = 19); and patients with combined sensory deficits (n = 11). The methods included Luria’s auditory-verbal (“10 words”) and visual memory tests and Bartlett’s experimental procedure. A decrease in memory volume for auditory-verbal and visual-figurative short-term memories was observed in all groups. The results reveal significant adverse dynamics of qualitative and quantitative indicators for memory consolidation and reconsolidation processes, associated with decreased volume of short-term memories, both auditory-verbal and visual-figurative. Based on these findings, we conclude that consolidation and reconsolidation efficiency depends on proper accommodation of the newly incoming information to already memorized modules (previous experience) and requires dosing of the newly incoming information in order to preserve its integrity at the stage of consolidation.
Introduction. Liver tumors account for 1.1 % of all newly diagnosed neoplasms in children. The rarity of this pathology causes difficulties in differential diagnosis. Currently, magnetic resonance imaging (MRI) is the main and most promising method for diagnosing liver diseases. In our work, we decided to quantify the data from this study.Purpose of the study – determination of the possibilities of quantitative assessment of multiparametric MRI data in the differential diagnosis of benign and malignant liver tumors in children.Material and methods. 133 patients with 307 liver lesions aged from 5 months to 20 years were examined. All patients underwent MRI on high-field MRI machines using an extracellular contrast agent, which included T2 weighted images with and without suppression of the signal from adipose tissue, diffusion-weighted images with automatic calculation of maps of the apparent diffusion coefficient (ADC), T1 weighted images with suppression of the signal from adipose tissue before and after the introduction of a contrast agent (in the arterial, portal, venous and delayed phases). Quantitative characteristics of changes in signal intensity in the lesion, intact liver parenchyma, spleen, kidney, aorta, and inferior vena cava (IVC) were obtained. To level the influence of external factors, we used not the absolute values of the signal intensity, but the ratios: lesion/intact liver parenchyma, lesion/kidney, lesion/aorta, lesion/spleen, lesion/IVC. For each lesion, 5 coefficients were calculated in each of the sequences, with the exception of patients (n = 4) after splenectomy, in whom 4 coefficients were calculated. In addition, for images obtained after the injection of a contrast agent, the ratios of the signal on post-contrast images to the native phase were calculated. Quantitative parameters such as the maximum size of the tumor, its volume and the age of the patient were included in the calculation. Tumors were represented by benign (n = 139) and malignant (n = 169) formations. The diagnosis of all malignant neoplasms and some benign ones was confirmed morphologically, benign ones – using MRI with intravenous contrast and dynamic observation.Results. A mathematical model was built:A = 1/(1+e-Z),where Z = 6,25019 + 1,03132 × S + 1,30077 × P2le/li – 0,00459 × DCle + 4,01375 × P1le/a – 2,05533 × Part le/li – 2,55823 × Pport le/k + 7,56980 × Pdel5 le/k – 15,91047 × Pdel5 le/a.The model is informative and statistically significant (p < 0.001). If A > 0.5, it should be considered that the studied focus is of a malignant nature, if A ≤ 0.5, the formation is benign. Model sensitivity and specificity were, respectively, 0.947 and 0.917.Conclusion. The mathematical model makes it possible to differentiate between malignant and benign formations with a high degree of informativeness, which is a priority task in detecting a mass formation in the liver.
Background: Artificial intelligence (AI) technologies can help solve the significant problem of missed findings in radiology studies. An important issue is assessing the economic benefits of implementing AI. Aim: to evaluate the frequency of missed pathologies detection and the economic potential of AI technology for chest CT, validated by expert radiologists, compared with radiologists without access to AI in a private medical center. Methods: An observational, single-center retrospective study was conducted. The study included chest CTs without IV contrast performed from 01.06.2022 to 31.07.2022 in "Yauza Hospital" LLC, Moscow. The CTs were processed using a complex AI algorithm for ten pathologies: pulmonary infiltrates, typical for viral pneumonia (COVID-19 in pandemic conditions); lung nodules; pleural effusion; pulmonary emphysema; thoracic aortic dilatation; pulmonary trunk dilatation; coronary artery calcification; adrenal hyperplasia; osteoporosis (vertebral body height and density changes). Two experts analyzed CTs and compared results with AI. Further routing was determined according to clinical guidelines for all findings initially detected and missed by radiologists. The lost potential revenue (LPR) was calculated for each patient according to the hospital price list. Results: From the final 160 CTs, the AI identified 90 studies (56%) with pathologies, of which 81 studies (51%) were missing at least one pathology in the report. The "second-stage" LPR for all pathologies from 81 patients was RUB 2,847,760 ($37,251 or CNY 256,218). LPR only for those pathologies missed by radiologists but detected by AI was RUB 2,065,360 ($27,017 or CNY 185,824). Conclusion: Using AI for chest CTs as an "assistant" to the radiologist can significantly reduce the number of missed abnormalities. AI usage can bring 3.6 times more benefits compared to the standard model without AI. The use of complex AI for chest CT can be cost-effective.
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