Artificial Intelligence and Neuroscience: Transformative Synergies in Brain Research and Clinical Applications

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

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The hidden rhythms of epilepsy: exploring biological clocks and epileptic seizure dynamics

Acta Epileptologica, Journal Year: 2025, Volume and Issue: 7(1)

Published: Jan. 3, 2025

Abstract Epilepsy, characterized by recurrent seizures, is influenced biological rhythms, such as circadian, seasonal, and menstrual cycles. These rhythms affect the frequency, severity, timing of although precise mechanisms underlying these associations remain unclear. This review examines role clocks, particularly core circadian genes Bmal1 , Clock Per Cry in regulating neuronal excitability epilepsy susceptibility. We explore how sleep-wake cycle, non-rapid eye movement sleep, increases risk discuss modulation neurotransmitters like gamma-aminobutyric acid glutamate. clinical implications, including chronotherapy which refers to practice medical treatments align with body's natural rhythm. Chronotherapy aligns anti-seizure medication administration rhythms. also rhythm-based neuromodulation strategies, adaptive deep brain stimulation, may dynamically change stimulation response predicted seizures patients, provide additional therapeutic options. emphasizes potential integrating rhythm analysis into personalized management, offering novel approaches optimize treatment improve patient outcomes. Future research should focus on understanding individual variability seizure harnessing technological innovations enhance prediction, precision treatment, long-term management.

Language: Английский

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Artificial Intelligence in Pediatric Epilepsy Detection: Balancing Effectiveness With Ethical Considerations for Welfare

Health Science Reports, Journal Year: 2025, Volume and Issue: 8(1)

Published: Jan. 1, 2025

ABSTRACT Background and Aim Epilepsy is a major neurological challenge, especially for pediatric populations. It profoundly impacts both developmental progress quality of life in affected children. With the advent artificial intelligence (AI), there's growing interest leveraging its capabilities to improve diagnosis management epilepsy. This review aims assess effectiveness AI epilepsy detection while considering ethical implications surrounding implementation. Methodology A comprehensive systematic was conducted across multiple databases including PubMed, EMBASE, Google Scholar, Scopus, Medline. Search terms encompassed “pediatric epilepsy,” “artificial intelligence,” “machine learning,” “ethical considerations,” “data security.” Publications from past decade were scrutinized methodological rigor, with focus on studies evaluating AI's efficacy management. Results systems have demonstrated strong potential diagnosing monitoring epilepsy, often matching clinical accuracy. For example, AI‐driven decision support achieved 93.4% accuracy diagnosis, closely aligning expert assessments. Specific methods, like EEG‐based detecting interictal discharges, showed high specificity (93.33%–96.67%) sensitivity (76.67%–93.33%), neuroimaging approaches using rs‐fMRI DTI reached up 97.5% identifying microstructural abnormalities. Deep learning models, such as CNN‐LSTM, also enhanced seizure video by capturing subtle movement expression cues. Non‐EEG sensor‐based methods effectively identified nocturnal seizures, offering promising care. However, considerations around privacy, data security, model bias remain crucial responsible integration. Conclusion While holds immense enhance management, transparency, fairness, security must be rigorously addressed. Collaborative efforts among stakeholders are imperative navigate these challenges effectively, ensuring integration optimizing patient outcomes

Language: Английский

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AI in neurocritical care: what to expect

Intensive Care Medicine, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 11, 2025

Language: Английский

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Non-Invasive Biosensing for Healthcare Using Artificial Intelligence: A Semi-Systematic Review

Biosensors, Journal Year: 2024, Volume and Issue: 14(4), P. 183 - 183

Published: April 9, 2024

The rapid development of biosensing technologies together with the advent deep learning has marked an era in healthcare and biomedical research where widespread devices like smartphones, smartwatches, health-specific have potential to facilitate remote accessible diagnosis, monitoring, adaptive therapy a naturalistic environment. This systematic review focuses on impact combining multiple techniques algorithms application these models healthcare. We explore key areas that researchers engineers must consider when developing model for biosensing: data modality, architecture, real-world use case model. also discuss ongoing challenges future directions this field. aim provide useful insights who seek intelligent advance precision

Language: Английский

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Residual and bidirectional LSTM for epileptic seizure detection

Frontiers in Computational Neuroscience, Journal Year: 2024, Volume and Issue: 18

Published: June 17, 2024

Electroencephalogram (EEG) plays a pivotal role in the detection and analysis of epileptic seizures, which affects over 70 million people world. Nonetheless, visual interpretation EEG signals for epilepsy is laborious time-consuming. To tackle this open challenge, we introduce straightforward yet efficient hybrid deep learning approach, named ResBiLSTM, detecting seizures using signals. Firstly, one-dimensional residual neural network (ResNet) tailored to adeptly extract local spatial features Subsequently, acquired are input into bidirectional long short-term memory (BiLSTM) layer model temporal dependencies. These output further processed through two fully connected layers achieve final seizure detection. The performance ResBiLSTM assessed on datasets provided by University Bonn Temple Hospital (TUH). achieves accuracy rates 98.88–100% binary ternary classifications dataset. Experimental outcomes recognition across seven types TUH corpus (TUSZ) dataset indicate that attains classification 95.03% weighted F1 score with 10-fold cross-validation. findings illustrate outperforms several recent state-of-the-art approaches.

Language: Английский

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Autonomic biosignals, seizure detection, and forecasting

Epilepsia, Journal Year: 2024, Volume and Issue: unknown

Published: June 5, 2024

Abstract Wearable devices have attracted significant attention in epilepsy research recent years for their potential to enhance patient care through improved seizure monitoring and forecasting. This narrative review presents a detailed overview of the current clinical state art while addressing how that assess autonomic nervous system (ANS) function reflect seizures central (CNS) changes. includes description interactions between CNS ANS, including physiological epilepsy‐related changes affecting dynamics. We first discuss technical aspects measuring biosignals considerations using ANS sensors practice. then detection forecasting studies, highlighting performance capability biomarkers. Finally, we address field's challenges provide an outlook future developments.

Language: Английский

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Improving wearable-based seizure prediction by feature fusion using growing network

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 29, 2025

A bstract The unpredictability of seizures is one the most compromising features reported by people with epilepsy. Non-stigmatizing and easy-to-use wearable devices may provide information to predict based on physiological data. We propose a patient-agnostic seizure prediction method that identifies group-level patterns across data from multiple patients. employ supervised long-short-term networks (LSTMs) add unsupervised deep canonically correlated autoencoders (DCCAE) 24-hour using time-of-day information. fuse these three techniques growing neural network, allowing incremental learning. Our all improves accuracy over baseline LSTM 7.3%, 74.4% 81.7%, averaged patients, outperforms in 84% Compared all-at-once fusion, network 9.5%. analyze impact preictal duration, quality, clinical variables performance.

Language: Английский

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Wearable sensors in paediatric neurology

Developmental Medicine & Child Neurology, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 31, 2025

Wearable sensors have the potential to transform diagnosis, monitoring, and management of children who neurological conditions. Traditional methods for assessing disorders rely on clinical scales subjective measures. The snapshot disease progression at a particular time point, lack cooperation by during assessments, susceptibility bias limit utility these sensors, which capture data continuously in natural settings, offer non-invasive objective alternative traditional methods. This review examines role wearable various paediatric conditions, including cerebral palsy, epilepsy, autism spectrum disorder, attention-deficit/hyperactivity as well Rett syndrome, Down Angelman Prader-Willi neuromuscular such Duchenne muscular dystrophy spinal atrophy, ataxia, Gaucher disease, headaches, sleep disorders. highlights their application tracking motor function, seizure activity, daily movement patterns gain insights into therapeutic response. Although challenges related population size, compliance, ethics, regulatory approval remain, technology promises improve trials outcomes patients neurology.

Language: Английский

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The use of AI in epilepsy and its applications for people with intellectual disabilities: commentary

Acta Epileptologica, Journal Year: 2025, Volume and Issue: 7(1)

Published: Feb. 19, 2025

Abstract Epilepsy is one of the most common neurological disorders, affecting more than 50 million people worldwide. Management particularly complex in individuals with intellectual disabilities, who are at a much higher risk having severe seizures compared to general population. People disabilities regularly excluded from epilepsy research, despite significantly risks negative health outcomes and early mortality. Recent advances artificial intelligence (AI) have shown great potential improving diagnosis, monitoring, management epilepsy. Machine learning techniques been used analysing electroencephalography data for efficient seizure detection prediction, as well individualised treatment, which facilitates timely customised intervention Research implementation AI-based solutions still remains limited due lack accessible long-term clinical model training, difficulties communicating ethical challenges ensuring safety AI systems this This paper presents an overview recent applications highlighting key necessity including research on epilepsy, strategies promote development use vulnerable Given prevalence consequences associated application care has significant positive impact. To achieve impact avoid increasing existing inequity, there urgent need greater inclusion around management.

Language: Английский

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