Published: July 27, 2024
Language: Английский
Ageing Research Reviews, Journal Year: 2024, Volume and Issue: unknown, P. 102497 - 102497
Published: Sept. 1, 2024
Language: Английский
Citations
20
Journal of Clinical Medicine, Journal Year: 2025, Volume and Issue: 14(2), P. 550 - 550
Published: Jan. 16, 2025
The convergence of Artificial Intelligence (AI) and neuroscience is redefining our understanding the brain, unlocking new possibilities in research, diagnosis, therapy. This review explores how AI’s cutting-edge algorithms—ranging from deep learning to neuromorphic computing—are revolutionizing by enabling analysis complex neural datasets, neuroimaging electrophysiology genomic profiling. These advancements are transforming early detection neurological disorders, enhancing brain–computer interfaces, driving personalized medicine, paving way for more precise adaptive treatments. Beyond applications, itself has inspired AI innovations, with architectures brain-like processes shaping advances algorithms explainable models. bidirectional exchange fueled breakthroughs such as dynamic connectivity mapping, real-time decoding, closed-loop systems that adaptively respond states. However, challenges persist, including issues data integration, ethical considerations, “black-box” nature many systems, underscoring need transparent, equitable, interdisciplinary approaches. By synthesizing latest identifying future opportunities, this charts a path forward integration neuroscience. From harnessing multimodal cognitive augmentation, fusion these fields not just brain science, it reimagining human potential. partnership promises where mysteries unlocked, offering unprecedented healthcare, technology, beyond.
Language: Английский
Citations
5
Computerized Medical Imaging and Graphics, Journal Year: 2024, Volume and Issue: 116, P. 102400 - 102400
Published: May 25, 2024
Language: Английский
Citations
9
International Journal of Advanced Computer Science and Applications, Journal Year: 2024, Volume and Issue: 15(4)
Published: Jan. 1, 2024
Alzheimer's disease (AD) poses a significant healthcare challenge, with an escalating prevalence and forecasted surge in affected individuals. The urgency for precise diagnostic tools to enable early interventions improved patient care is evident. Despite advancements, existing detection frameworks exhibit limitations accurately identifying AD, especially its stages. Model optimisation accuracy are other issues. This paper aims address this critical research gap by introducing ConvADD, advanced Convolutional Neural Network architecture tailored AD detection. By meticulously designing study endeavours surpass the of current methodologies enhance metrics, optimisation, reliability diagnosis. dataset was collected from Kaggle consists preprocessed 2D images extracted 3D images. Through rigorous experimentation, ConvADD demonstrates remarkable performance showcasing potential as robust effective. proposed model shows results tool 98.01%, precision 98%, recall F1-Score only 2.1 million parameters. However, despite promising results, several challenges remain, such generalizability across diverse populations need further validation studies. elucidating these gaps challenges, contributes ongoing discourse on improving lays groundwork future domain.
Language: Английский
Citations
7
Artificial Intelligence Review, Journal Year: 2024, Volume and Issue: 57(12)
Published: Oct. 10, 2024
Abstract The emergence of neuromorphic computing, inspired by the structure and function human brain, presents a transformative framework for modelling neurological disorders in drug development. This article investigates implications applying computing to simulate comprehend complex neural systems affected conditions like Alzheimer’s, Parkinson’s, epilepsy, drawing from extensive literature. It explores intersection with neurology pharmaceutical development, emphasizing significance understanding processes integrating deep learning techniques. Technical considerations, such as circuits into CMOS technology employing memristive devices synaptic emulation, are discussed. review evaluates how optimizes discovery improves clinical trials precisely simulating biological systems. also examines role models comprehending disorders, facilitating targeted treatment Recent progress is highlighted, indicating potential therapeutic interventions. As advances, synergy between neuroscience holds promise revolutionizing study brain’s complexities addressing challenges.
Language: Английский
Citations
7
Artificial Intelligence Review, Journal Year: 2024, Volume and Issue: 57(9)
Published: Aug. 6, 2024
Abstract Deep learning is revolutionizing various domains and significantly impacting medical image analysis. Despite notable progress, numerous challenges remain, necessitating the refinement of deep algorithms for optimal performance in This paper explores growing demand precise robust analysis by focusing on an advanced technique, multistage transfer learning. Over past decade, has emerged as a pivotal strategy, particularly overcoming associated with limited data model generalization. However, absence well-compiled literature capturing this development remains gap field. exhaustive investigation endeavors to address providing foundational understanding how approaches confront unique posed insufficient datasets. The offers detailed types, architectures, methodologies, strategies deployed Additionally, it delves into intrinsic within framework, comprehensive overview current state while outlining potential directions advancing methodologies future research. underscores transformative analysis, valuable guidance researchers healthcare professionals.
Language: Английский
Citations
6
Deleted Journal, Journal Year: 2025, Volume and Issue: 4(1)
Published: Jan. 1, 2025
Multiple studies have attempted to use a single type of data predict various stages Alzheimer’s disease (AD). However, combining multiple modalities can improve prediction accuracy. In this study, we utilized combination biomarkers, including magnetic resonance imaging (MRI), electronic health records, and cerebrospinal fluid (CSF), classify subjects into three groups based on clinical tests—normal cognitive controls (CN), mild impairment (MCI), AD. To determine the significant parameters, employ novel technique that utilizes sparse autoencoders extract features from CSF, data, convolutional neural networks’ (CNN’s) MRI data. Our results indicate deep learning methods outperform traditional machine models such as decision trees, support vector machines, random forests K-nearest neighbors. The proposed method significantly outperforms models, achieving an accuracy 0.87 for CN versus AD, precision 0.93 CN, recall 0.88 AD external test set. integration application techniques enhance accuracy, demonstrating potential improved diagnostic tools in settings.
Language: Английский
Citations
0
The Journal of Supercomputing, Journal Year: 2025, Volume and Issue: 81(2)
Published: Jan. 23, 2025
Language: Английский
Citations
0
Computers in Biology and Medicine, Journal Year: 2025, Volume and Issue: 187, P. 109810 - 109810
Published: Feb. 11, 2025
Language: Английский
Citations
0
Communications in computer and information science, Journal Year: 2025, Volume and Issue: unknown, P. 134 - 144
Published: Jan. 1, 2025
Language: Английский
Citations
0