AIMC Topic: Adult

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Identification and Connectomic Profiling of Concussion Using Bayesian Machine Learning.

Journal of neurotrauma
Accurate early diagnosis of concussion is useful to prevent sequelae and improve neurocognitive outcomes. Early after head impact, concussion diagnosis may be doubtful in persons whose neurological, neuroradiological, and/or neurocognitive examinatio...

Machine learning model for predicting stroke recurrence in adult stroke patients with moyamoya disease and factors of stroke recurrence.

Clinical neurology and neurosurgery
OBJECT: The aim of this study was at building an effective machine learning model to contribute to the prediction of stroke recurrence in adult stroke patients subjected to moyamoya disease (MMD), while at analyzing the factors for stroke recurrence.

Expert-level sleep staging using an electrocardiography-only feed-forward neural network.

Computers in biology and medicine
Reliable classification of sleep stages is crucial in sleep medicine and neuroscience research for providing valuable insights, diagnoses, and understanding of brain states. The current gold standard method for sleep stage classification is polysomno...

Mapping Method of Human Arm Motion Based on Surface Electromyography Signals.

Sensors (Basel, Switzerland)
This paper investigates a method for precise mapping of human arm movements using sEMG signals. A multi-channel approach captures the sEMG signals, which, combined with the accurately calculated joint angles from an Inertial Measurement Unit, allows ...

Differentiating Epileptic and Psychogenic Non-Epileptic Seizures Using Machine Learning Analysis of EEG Plot Images.

Sensors (Basel, Switzerland)
The treatment of epilepsy, the second most common chronic neurological disorder, is often complicated by the failure of patients to respond to medication. Treatment failure with anti-seizure medications is often due to the presence of non-epileptic s...

ZleepAnlystNet: a novel deep learning model for automatic sleep stage scoring based on single-channel raw EEG data using separating training.

Scientific reports
Numerous models for sleep stage scoring utilizing single-channel raw EEG signal have typically employed CNN and BiLSTM architectures. While these models, incorporating temporal information for sequence classification, demonstrate superior overall per...

A machine-learning method isolating changes in wrist kinematics that identify age-related changes in arm movement.

Scientific reports
Normal aging often results in an increase in physiological tremors and slowing of the movement of the hands, which can impair daily activities and quality of life. This study, using lightweight wearable non-invasive sensors, aimed to detect and ident...

LST-AI: A deep learning ensemble for accurate MS lesion segmentation.

NeuroImage. Clinical
Automated segmentation of brain white matter lesions is crucial for both clinical assessment and scientific research in multiple sclerosis (MS). Over a decade ago, we introduced an engineered lesion segmentation tool, LST. While recent lesion segment...

Determining individual suitability for neoadjuvant systemic therapy in breast cancer patients through deep learning.

Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico
BACKGROUND: The survival advantage of neoadjuvant systemic therapy (NST) for breast cancer patients remains controversial, especially when considering the heterogeneous characteristics of individual patients.