AIMC Topic: Diagnosis, Differential

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Deep learning based classification of breast tumors with shear-wave elastography.

Ultrasonics
This study aims to build a deep learning (DL) architecture for automated extraction of learned-from-data image features from the shear-wave elastography (SWE), and to evaluate the DL architecture in differentiation between benign and malignant breast...

Classification Preictal and Interictal Stages via Integrating Interchannel and Time-Domain Analysis of EEG Features.

Clinical EEG and neuroscience
The life quality of patients with refractory epilepsy is extremely affected by abrupt and unpredictable seizures. A reliable method for predicting seizures is important in the management of refractory epilepsy. A critical factor in seizure prediction...

Integrative Analysis of Proteomic, Glycomic, and Metabolomic Data for Biomarker Discovery.

IEEE journal of biomedical and health informatics
Studies associating changes in the levels of multiple biomolecules including proteins, glycans, glycoproteins, and metabolites with the onset of cancer have been widely investigated to identify clinically relevant diagnostic biomarkers. Advances in l...

Validation of a novel classification model of psychogenic nonepileptic seizures by video-EEG analysis and a machine learning approach.

Epilepsy & behavior : E&B
The aim of this study was to validate a novel classification for the diagnosis of PNESs. Fifty-five PNES video-EEG recordings were retrospectively analyzed by four epileptologists and one psychiatrist in a blind manner and classified into four distin...

Deep Learning Representation from Electroencephalography of Early-Stage Creutzfeldt-Jakob Disease and Features for Differentiation from Rapidly Progressive Dementia.

International journal of neural systems
A novel technique of quantitative EEG for differentiating patients with early-stage Creutzfeldt-Jakob disease (CJD) from other forms of rapidly progressive dementia (RPD) is proposed. The discrimination is based on the extraction of suitable features...

Use of machine learning for behavioral distinction of autism and ADHD.

Translational psychiatry
Although autism spectrum disorder (ASD) and attention deficit hyperactivity disorder (ADHD) continue to rise in prevalence, together affecting >10% of today's pediatric population, the methods of diagnosis remain subjective, cumbersome and time inten...

Ultrasound Image Discrimination between Benign and Malignant Adnexal Masses Based on a Neural Network Approach.

Ultrasound in medicine & biology
The discrimination between benign and malignant adnexal masses in ultrasound images represents one of the most challenging problems in gynecologic practice. In the study described here, a new method for automatic discrimination of adnexal masses base...

Serum Interleukin-33 level in Saudi children with inflammatory bowel disease.

International journal of clinical and experimental pathology
Interleukin-33 (IL-33) is a cytokine that belongs to the interleukin-1 family and has been shown to be associated with mucosal inflammation. The aim of this study was to determine the serum level of IL-33 in children with ulcerative colitis (UC) and ...

Computer-Aided Diagnosis of Parkinson's Disease Using Enhanced Probabilistic Neural Network.

Journal of medical systems
Early and accurate diagnosis of Parkinson's disease (PD) remains challenging. Neuropathological studies using brain bank specimens have estimated that a large percentages of clinical diagnoses of PD may be incorrect especially in the early stages. In...