Elsevier eBooks, Год журнала: 2024, Номер unknown, С. 27 - 40
Опубликована: Ноя. 1, 2024
Язык: Английский
Journal of Clinical Medicine, Год журнала: 2025, Номер 14(2), С. 550 - 550
Опубликована: Янв. 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.
Язык: Английский
Процитировано
8
Seizure, Год журнала: 2025, Номер unknown
Опубликована: Янв. 1, 2025
Язык: Английский
Процитировано
0
JOURNAL OF HIGH-FREQUENCY COMMUNICATION TECHNOLOGIES, Год журнала: 2025, Номер 03(02), С. 299 - 314
Опубликована: Апрель 22, 2025
The heterogeneity in the causes and responses to pain patients makes neuralgia, a condition defined by persistent severe nerve pain, challenging treatment problem. However, inconsistent therapeutic results long patient suffering are common of traditional therapy procedures that depend on generic methodologies. This research presents technological framework combines data mining transcranial focused ultrasound (tFUS) improve strategies for with aim overcoming these limitations. first step proposed system is gather multimodal datasets have been preprocessed using normalization, noise reduction, feature extraction methods. These sets include patient-reported ratings, clinical history, brain imaging (fMRI, EEG). Next, algorithms such as clustering classification used find patterns activity attributes. Dimensionality reduction methods variational autoencoders (VAEs) make complex associations easier observe understand. Optimal tFUS parameters frequency, intensity, focal depth predicted individual machine learning models (MLM), gradient-boosted decision trees (GBDT) Random Forests (RF). Based biomarkers detected, predictions direct deployment specific area brain. During treatment, real-time neural feedback systems track patients’ reactions, allowing adaptive alterations boost effectiveness. Incorporating post-treatment into an iterative loop allows continued improvement prediction future sessions. An increase measures was observed compared techniques, greater neuroplasticity fewer side effects when evaluated from neuralgia. method achieves 97.86% 97.14%, 34.61% 37.83%, 98.64% 96.36%, effectiveness safety 97.04% 98.67%.
Язык: Английский
Процитировано
0
Magnetic Resonance Imaging Clinics of North America, Год журнала: 2025, Номер unknown
Опубликована: Май 1, 2025
Язык: Английский
Процитировано
0
Current Pain and Headache Reports, Год журнала: 2025, Номер 29(1)
Опубликована: Май 20, 2025
Язык: Английский
Процитировано
0
Journal of Diabetes Research, Год журнала: 2024, Номер 2024(1)
Опубликована: Янв. 1, 2024
Peripheral neuropathy is a common cause of morbidity in diabetes. Despite recent advancements early diagnosis methods, there need for practical, highly sensitive, and cost-effective screening methods clinical practice. This study summarizes evidence from systematic reviews meta-analyses on the diagnostic accuracy validated diabetic peripheral neuropathy. Two independent reviewers assessed methodological quality bias using AMSTAR ROBIS tools. Seven with 19,531 participants were included. The monofilament test showed inconsistent sensitivity (
Язык: Английский
Процитировано
1
Lecture notes in computer science, Год журнала: 2024, Номер unknown, С. 3 - 18
Опубликована: Янв. 1, 2024
Язык: Английский
Процитировано
0
Annual reports in medicinal chemistry, Год журнала: 2024, Номер unknown
Опубликована: Янв. 1, 2024
Язык: Английский
Процитировано
0
Elsevier eBooks, Год журнала: 2024, Номер unknown, С. 27 - 40
Опубликована: Ноя. 1, 2024
Язык: Английский
Процитировано
0