Comparative analysis of electroencephalogram (EEG) data gathered from the frontal region with other brain regions affected by attention deficit hyperactivity disorder (ADHD) through multiresolution analysis and machine learning techniques.

Attention deficit hyperactivity disorder (ADHD) is a neurodevelopmental disorder characterized by repeated patterns of hyperactivity, impulsivity, and inattention that limit daily functioning and development. Electroencephalography (EEG) anomalies correspond to changes in brain connection and activity. The authors propose utilizing empirical mode decomposition (EMD) and discrete wavelet transform (DWT) for feature extraction and machine learning (ML) algorithms to categorize ADHD and control subjects.

Analysis of K-Wire and Olive Wire in Ilizarov Apparatus: A Comparative Study.

Background: Orthopedic fixators depend on mechanical characteristics like stiffness and firmness. The Ilizarov Apparatus (IA) is a common surgical management approach to restructure the bone fractures. IA includes two wires, specifically K-wire and olive wire, to treat the fractures.

Exploring role of prefrontal cortex region of brain in children having ADHD with machine learning: Implications and insights.

Objective: Attention deficit hyperactivity disorder (ADHD), is a general neurodevelopmental syndrome. This affects both adults and children, causing issues like hyperactivity, inattention, and impulsivity. Diagnosis, typically reliant on patient narratives and questionnaires, can sometimes be inaccurate, leading to distress.

Contributions of brain regions to machine learning-based classifications of attention deficit hyperactivity disorder (ADHD) utilizing EEG signals.

Objective: The study presented focuses on the creation of a machine learning (ML) model that uses electrophysiological (EEG) data to identify kids with attention deficit hyperactivity disorder (ADHD) from healthy controls. The EEG signals are acquired during cognitive tasks to distinguish children with ADHD from their counterparts.

Methodology: The EEG data recorded in cognitive exercises was filtered using low pass Bessel filter and notch filters to remove artifacts, by the data set owners.

Imaging-based stratification of adult gliomas prognosticates survival and correlates with the 2021 WHO classification.

Background: Because of the lack of global accessibility, delay, and cost-effectiveness of genetic testing, there is a clinical need for an imaging-based stratification of gliomas that can prognosticate survival and correlate with the 2021-WHO classification.

Methods: In this retrospective study, adult primary glioma patients with pre-surgery/pre-treatment MRI brain images having T2, FLAIR, T1, T1 post-contrast, DWI sequences, and survival information were included in TCIA training-dataset (n = 275) and independent validation-dataset (n = 200). A flowchart for imaging-based stratification of adult gliomas(IBGS) was created in consensus by three authors to encompass all adult glioma types.