Reading of isolated words in conditions mimicking artificial vision has been found to be a difficult but feasible task. In particular at relatively high eccentricities, a significant adaptation process was required to reach optimal performances [Vision Res. 43 (2003) 269]. The present study addressed the task of full-page reading, including page navigation under control of subject's own eye movements. Conditions of artificial vision mimicking a retinal implant were simulated by projecting stimuli with reduced information content (lines of pixelised text) onto a restricted and eccentric area of the retina. Three subjects, naïve to the task, were trained for almost two months (about 1 h/day) to read full-page texts. Subjects had to use their own eye movements to displace a 10 degrees x 7 degrees viewing window, stabilised at 15 degrees eccentricity in their lower visual field. Initial reading scores were very low for two subjects (about 13% correctly read words), and astonishingly high for the third subject (86% correctly read words). However, all of them significantly improved their performance with time, reaching close to perfect reading scores (ranging from 86% to 98% correct) at the end of the training process. Reading rates were as low as 1-5 words/min at the beginning of the experiment and increased significantly with time to 14-28 words/min. Qualitative text understanding was also estimated. We observed that reading scores of at least 85% correct were necessary to achieve 'good' text understanding. Gaze position recordings, made during the experimental sessions, demonstrated that the control of eye movements, especially the suppression of reflexive vertical saccades, constituted an important part of the overall adaptive learning process. Taken together, these results suggest that retinal implants might restore full-page text reading abilities to blind patients. About 600 stimulation contacts, distributed on an implant surface of 3 x 2 mm2, appear to be a minimum to allow for useful reading performance. A significant learning process will however be required to reach optimal performance with such devices, especially if they have to be placed outside the foveal area.
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http://dx.doi.org/10.1016/j.visres.2004.01.017 | DOI Listing |
Rev Neurosci
January 2025
557765 Network of Neurosurgery and Artificial Intelligence (NONAI), Universal Scientific Education and Research Network (USERN ), Tehran, Iran.
The recognition and classification of facial expressions using artificial intelligence (AI) presents a promising avenue for early detection and monitoring of neurodegenerative disorders. This narrative review critically examines the current state of AI-driven facial expression analysis in the context of neurodegenerative diseases, such as Alzheimer's and Parkinson's. We discuss the potential of AI techniques, including deep learning and computer vision, to accurately interpret and categorize subtle changes in facial expressions associated with these pathological conditions.
View Article and Find Full Text PDFJ Food Sci
January 2025
College of Mechanical and Electrical Engineering, Shaanxi University of Science and Technology, Xi'an, China.
To enhance the drying quality of peony flowers, this study developed an integrated intelligent control and monitoring system. The system incorporates computer vision technology to enable real-time continuous monitoring and analysis of the total color change (ΔE) and shrinkage rate (SR) of the material. Additionally, by integrating drying time and temperature data, a hybrid neural network model combining convolutional neural networks, long short-term memory, and attention mechanisms (CNN-LSTM-Attention) was employed to accurately predict the moisture ratio (MR) of peony flowers.
View Article and Find Full Text PDFTech Vasc Interv Radiol
December 2024
Department of Interventional Radiology, MedStar Georgetown University Hospital, Washington, DC. Electronic address:
Artificial intelligence and robotics are transforming interventional radiology, driven by advancements in computer vision, robotics and procedural automation. Historically focused on diagnostics, AI now also enhances procedural capabilities in IR, enabling future robotic systems to handle complex tasks such as catheter manipulation or needle placement with increasing precision and reliability. Early robotic systems in IR demonstrated improved accuracy in both vascular and percutaneous interventions, though none were equipped with automatic decision-making.
View Article and Find Full Text PDFBrain Stimul
January 2025
Graduate School of Biomedical Engineering, UNSW, Sydney, NSW 2052, Australia; Tyree Foundation Institute of Health Engineering (IHealthE), UNSW, Sydney, NSW 2052, Australia. Electronic address:
Introduction: Current brain-based visual prostheses pose significant challenges impeding adoption such as the necessarily complex surgeries and occurrence of more substantial side effects due to the sensitivity of the brain. This has led to much effort toward vision restoration being focused on the more approachable part of the brain - the retina. Here we introduce a novel, parameterized simulation platform that enables study of human retinal degeneration and optimization of stimulation strategies.
View Article and Find Full Text PDFJ Hazard Mater
January 2025
School of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan 430070, China; Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System, Wuhan 430070, China. Electronic address:
Artificial intelligence-assisted imaging biosensors have attracted increasing attention due to their flexibility, allowing for the digital image analysis and quantification of biomarkers. While deep learning methods have led to advancements in biomarker identification, the diversity in the density and adherence of targets still poses a serious challenge. In this regard, we propose CellNet, a neural network model specifically designed for detecting dense targets.
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