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INSIGHT: Combining Fixation Visualisations and Residual Neural Networks for Dyslexia Classification From Eye-Tracking Data.
Dyslexia
February 2025
Department of Machine Learning and Data Processing, Faculty of Informatics, Masaryk University, Brno, Czech Republic.
Current diagnostic methods for dyslexia primarily rely on traditional paper-and-pencil tasks. Advanced technological approaches, including eye-tracking and artificial intelligence (AI), offer enhanced diagnostic capabilities. In this paper, we bridge the gap between scientific and diagnostic concepts by proposing a novel dyslexia detection method, called INSIGHT, which combines a visualisation phase and a neural network-based classification phase.
View Article and Find Full Text PDFDiscrimination of Radiologists' Experience Level Using Eye-Tracking Technology and Machine Learning: Case Study.
JMIR Form Res
January 2025
Department of Psychology, The University of Texas at San Antonio, San Antonio, TX, United States.
Background: Perception-related errors comprise most diagnostic mistakes in radiology. To mitigate this problem, radiologists use personalized and high-dimensional visual search strategies, otherwise known as search patterns. Qualitative descriptions of these search patterns, which involve the physician verbalizing or annotating the order he or she analyzes the image, can be unreliable due to discrepancies in what is reported versus the actual visual patterns.
View Article and Find Full Text PDFSystematic Reviews on the Management of Xerostomia and Hyposalivation-An Umbrella Review.
Gerodontology
January 2025
School of Dentistry, Universidade Comunitária da Região de Chapecó-Unochapecó, Área de Ciências da Saúde, Chapecó, Santa Catarina, Brazil.
Introduction: Dry mouth is moderately prevalent in the older population. Pharmacological and non-pharmacological alternatives have been assessed to manage its manifestation. This umbrella review synthesised the evidence on approaches to managing xerostomia and hyposalivation.
View Article and Find Full Text PDFPunctuational evolution is pervasive in distal site metastatic colonization.
Proc Biol Sci
January 2025
Cancer Ecology Center, The Brady Urological Institute, Johns Hopkins School of Medicine, Baltimore, MD 21287, USA.
The evolution of metastasis, the spread of cancer to distal sites within the body, represents a lethal stage of cancer progression. Yet, the evolutionary dynamics that shape the emergence of metastatic disease remain unresolved. Here, using single-cell lineage tracing data in combination with phylogenetic statistical methods, we show that the evolutionary trajectory of metastatic disease is littered with bursts of rapid molecular change as new cellular subpopulations appear, a pattern known as punctuational evolution.
View Article and Find Full Text PDFThe Relationship Between Preferred Learning Styles and Academic Achievement of Undergraduate Health Sciences Students Compared to Other Disciplines at a Middle Eastern University Utilizing the VARK Instrument.
Adv Med Educ Pract
January 2025
College of Health Sciences, University of Buraimi, Buraimi Governorate, Oman.
Introduction: Learning style denotes a learner's approach to acquiring, processing, interpreting, organizing, and contemplating information. VARK, formulated by Fleming and Mills (1992), assesses learning styles: Visual (V), Aural (A), Reading/Writing (R), and Kinesthetic (K). Visual learners prefer observing; Aural learners favor listening to lectures; Reading/Writing learners engage through texts and notes; Kinesthetic learners benefit from tactile activities.
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