A relative role of lines and corners of images of outline geometric figures in recognition performance was studied psychophysically. Probability of correct response to the shape of the whole figure (control) and figures with lines or corners masked to a different extent was compared. Increase in the extent of masking resulted in a drop of recognition performance that was significantly lower for figures without corners, than for figures without part of their lines. The whole 3D figures were recognized better than 2D ones, whereas the opposite relations were observed under conditions of masking. Significant gender difference in a recognition performance was found: men recognize entire and partly masked figures better than women. Possible mechanisms of relatively better recognition of figures with corners than with lines are discussed in connection with finding of high sensitivity of many neurons in the primary visual cortex to line crossing and branching.
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http://dx.doi.org/10.55782/ane-2003-1475 | DOI Listing |
Singapore Med J
January 2025
Department of Respiratory and Critical Care Medicine, Singapore General Hospital, Singapore.
Introduction: Rapid response teams (RRTs) are prevalent in healthcare institutions worldwide. Repeated activations are associated with increased morbidity and higher resource utilisation, and represent a heterogeneous population that may benefit from early identification. To date, there are no published data on repeat RRT activations in Singapore.
View Article and Find Full Text PDFEClinicalMedicine
December 2024
Department of Pediatrics, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
Background: Infant alertness and neurologic changes can reflect life-threatening pathology but are assessed by physical exam, which can be intermittent and subjective. Reliable, continuous methods are needed. We hypothesized that our computer vision method to track movement, pose artificial intelligence (AI), could predict neurologic changes in the neonatal intensive care unit (NICU).
View Article and Find Full Text PDFBackground Fetal growth restriction (FGR) is a leading risk factor for stillbirth, yet the diagnosis of FGR confers considerable prognostic uncertainty, as most infants with FGR do not experience any morbidity. Our objective was to use data from a large, deeply phenotyped observational obstetric cohort to develop a probabilistic graphical model (PGM), a type of "explainable artificial intelligence (AI)", as a potential framework to better understand how interrelated variables contribute to perinatal morbidity risk in FGR. Methods Using data from 9,558 pregnancies delivered at ≥ 20 weeks with available outcome data, we derived and validated a PGM using randomly selected sub-cohorts of 80% (n = 7645) and 20% (n = 1,912), respectively, to discriminate cases of FGR resulting in composite perinatal morbidity from those that did not.
View Article and Find Full Text PDFFront Oncol
December 2024
Honorary Research Associate, Department of Operations and Quality Management, Durban University of Technology, Durban, South Africa.
Introduction: Lung cancer is one of the main causes of the rising death rate among the expanding population. For patients with lung cancer to have a higher chance of survival and fewer deaths, early categorization is essential. The goal of thisresearch is to enhance machine learning to increase the precision and quality of lung cancer classification.
View Article and Find Full Text PDFEClinicalMedicine
November 2024
State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, 100875, China.
Background: Attention deficit hyperactivity disorder (ADHD) is one prevalent neurodevelopmental disorder with childhood onset, however, there is no clear correspondence established between clinical ADHD subtypes and primary medications. Identifying objective and reliable neuroimaging markers for categorizing ADHD biotypes may lead to more individualized, biotype-guided treatment.
Methods: Here we proposed a graph convolution network for biological subtype detection (GCN-BSD) using both functional network connectivity (FNC) and non-imaging phenotypic data for ADHD biotype.
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