Advancing the Frontier: Neuroimaging Techniques in the Early Detection and Management of Neurodegenerative Diseases

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

Published: May 29, 2024

Alzheimer's and Parkinson's diseases are among the most prevalent neurodegenerative conditions affecting aging populations globally, presenting significant challenges in early diagnosis management. This narrative review explores pivotal role of advanced neuroimaging techniques detecting managing these at stages, potentially slowing their progression through timely interventions. Recent advancements MRI, such as ultra-high-field systems functional have enhanced sensitivity for subtle structural changes. Additionally, development novel amyloid-beta tracers other emerging modalities like optical imaging transcranial ultrasonography improved diagnostic accuracy capability existing methods. highlights clinical applications technologies diseases, where they shown performance, enabling earlier intervention better prognostic outcomes. Moreover, integration artificial intelligence (AI) longitudinal research is a promising enhancement to refine detection strategies further. However, this also addresses technical, ethical, accessibility field, advocating more extensive use overcome barriers. Finally, we emphasize need holistic approach that incorporates both neurological psychiatric perspectives, which crucial optimizing patient care outcomes management diseases.

Language: Английский

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Biomechanical Risk Classification in Repetitive Lifting Using Multi-Sensor Electromyography Data, Revised National Institute for Occupational Safety and Health Lifting Equation, and Deep Learning

Biosensors, Journal Year: 2025, Volume and Issue: 15(2), P. 84 - 84

Published: Feb. 1, 2025

Repetitive lifting tasks in occupational settings often result shoulder injuries, impacting both health and productivity. Accurately assessing the biomechanical risk of these remains a significant challenge ergonomics, particularly within manufacturing environments. Traditional assessment methods frequently rely on subjective reports limited observations, which can introduce bias yield incomplete evaluations. This study addresses limitations by generating utilizing comprehensive dataset containing detailed time-series electromyography (EMG) data from 25 participants. Using high-precision wearable sensors, EMG were collected eight muscles as participants performed repetitive tasks. For each task, index was calculated using revised National Institute for Occupational Safety Health (NIOSH) equation (RNLE). Participants completed cycles low-risk high-risk four-minute period, allowing muscle performance under realistic working conditions. extensive dataset, comprising over 7 million points sampled at approximately 1259 Hz, leveraged to develop deep learning models classify risk. To provide actionable insights practical ergonomics assessments, statistical features extracted raw data. Three models, Convolutional Neural Networks (CNNs), Multilayer Perceptron (MLP), Long Short-Term Memory (LSTM), employed analyze predict level. The CNN model achieved highest performance, with precision 98.92% recall 98.57%, proving its effectiveness real-time assessments. These findings underscore importance aligning architectures characteristics optimize management. By integrating sensors this enables precise, real-time, dynamic significantly enhancing workplace safety protocols. approach has potential improve planning reduce incidence severity work-related musculoskeletal disorders, ultimately promoting better outcomes across various settings.

Language: Английский

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AI-Optimized Electrochemical Aptasensors for Stable, Reproducible Detection of Neurodegenerative Diseases, Cancer, and Coronavirus

Heliyon, Journal Year: 2024, Volume and Issue: 11(1), P. e41338 - e41338

Published: Dec. 18, 2024

AI-optimized electrochemical aptasensors are transforming diagnostic testing by offering high sensitivity, selectivity, and rapid response times. Leveraging data-driven AI techniques, these sensors provide a non-invasive, cost-effective alternative to traditional methods, with applications in detecting molecular biomarkers for neurodegenerative diseases, cancer, coronavirus. The performance metrics outlined the comparative table illustrate significant advancements enabled integration. Sensitivity increases from 60 75 % ordinary 85-95 %, while specificity improves 70-80 90-98 %. This enhanced allows ultra-low detection limits, such as 10 fM carcinoembryonic antigen (CEA) 20 mucin-1 (MUC1) using Electrochemical Impedance Spectroscopy (EIS), 1 pM prostate-specific (PSA) Differential Pulse Voltammetry (DPV). Similarly, Square Wave (SWV) potentiometric have detected alpha-fetoprotein (AFP) at 5 epithelial cell adhesion molecule (EpCAM) 100 fM, respectively. integration also enhances reproducibility, reduces false positives negatives (from 15-20 5-10 %), significantly decreases times 10-15 s 2-3 s). These improve data processing speeds min per sample 2-5 min) calibration accuracy (<2 margin of error compared expanding application scope multi-target biomarker detection. review highlights how position powerful tools personalized treatment, point-of-care testing, continuous health monitoring. Despite higher cost ($500-$1,500/unit), their portability promise revolutionize healthcare, environmental monitoring, food safety, ultimately improving public outcomes.

Language: Английский

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Key Aspects of Biosensing for Instant Screening Tests

Biosensors and Bioelectronics X, Journal Year: 2024, Volume and Issue: 20, P. 100529 - 100529

Published: Aug. 13, 2024

Language: Английский

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Wearable Optical Sensors: Toward Machine Learning-Enabled Biomarker Monitoring

Chemistry Africa, Journal Year: 2024, Volume and Issue: 7(8), P. 4175 - 4192

Published: Aug. 16, 2024

Language: Английский

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Update on Patient Self-Testing with Portable and Wearable Devices: Advantages and Limitations

Diagnostics, Journal Year: 2024, Volume and Issue: 14(18), P. 2037 - 2037

Published: Sept. 13, 2024

Laboratory medicine has undergone a deep and multifaceted revolution in the course of human history, both organizational technical terms. Over past century, there been growing recognition need to centralize numerous diagnostic activities, often similar or identical but located different clinical departments, into common environment (i.e., medical laboratory service), followed by progressive centralization tests from smaller laboratories larger facilities. Nevertheless, technological advances that emerged at beginning new millennium have helped create testing culture characterized countervailing trend decentralization some closer patients caregivers. The forces driven this (centripetal) counter-revolution essentially include few key concepts, namely “home testing”, “portable even wearable devices” “remote patient monitoring”. By their very nature, services remote testing/monitoring are not contradictory, may well coexist, with choice one other depending on demographic characteristics patient, type analytical procedure logistics local organization care system. Therefore, article aims provide general overview self-testing, particular focus portable (including implantable) devices.

Language: Английский

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Monitoring Substance Use with Fitbit Biosignals: A Case Study on Training Deep Learning Models Using Ecological Momentary Assessments and Passive Sensing

AI, Journal Year: 2024, Volume and Issue: 5(4), P. 2725 - 2738

Published: Dec. 3, 2024

Substance use disorders affect 17.3% of Americans. Digital health solutions that machine learning to detect substance from wearable biosignal data can eventually pave the way for real-time digital interventions. However, difficulties in addressing severe between-subject heterogeneity have hampered adaptation approaches detection, necessitating more robust technological solutions. We tested utility personalized using participant-specific convolutional neural networks (CNNs) enhanced with self-supervised (SSL) drug use. In a pilot feasibility study, we collected 9 participants Fitbit Charge 5 devices, supplemented by ecological momentary assessments collect labels implemented baseline 1D-CNN model traditional supervised and an experimental SSL-enhanced improve individualized feature extraction under limited label conditions. Results: Among participants, achieved average area receiver operating characteristic curve score across 0.695 CNNs 0.729 SSL models. Strategic selection optimal threshold enabled us optimize either sensitivity or specificity while maintaining reasonable performance other metric. Conclusion: These findings suggest potential enhance monitoring systems. small sample size this study limits its generalizability diverse populations, so call future research explores SSL-powered personalization at larger scale.

Language: Английский

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