Discriminant analysis using MRI asymmetry indices and cognitive scores of women with temporal lobe epilepsy or schizophrenia.

Purpose: This study aims to assess the diagnostic power of brain asymmetry indices and neuropsychological tests for differentiating mesial temporal lobe epilepsy (MTLE) and schizophrenia (SCZ).

Methods: We studied a total of 39 women including 13 MTLE, 13 SCZ, and 13 healthy individuals (HC). A neuropsychological test battery (NPT) was administered and scored by an experienced neuropsychologist, and NeuroQuant (CorTechs Labs Inc.

Detection of the separated root canal instrument on panoramic radiograph: a comparison of LSTM and CNN deep learning methods.

Objectives: A separated endodontic instrument is one of the challenging complications of root canal treatment. The purpose of this study was to compare two deep learning methods that are convolutional neural network (CNN) and long short-term memory (LSTM) to detect the separated endodontic instruments on dental radiographs.

Methods: Panoramic radiographs from the hospital archive were retrospectively evaluated by two dentists.

Predicting the Prognosis of MCI Patients Using Longitudinal MRI Data.

The aim of this study is to develop a computer-aided diagnosis system with a deep-learning approach for distinguishing "Mild Cognitive Impairment (MCI) due to Alzheimer's Disease (AD)" patients among a list of MCI patients. In this system we are using the power of longitudinal data extracted from magnetic resonance (MR). For this work, a total of 294 MCI patients were selected from the ADNI database.

Distinguishing age-related cognitive decline from dementias: A study based on machine learning algorithms.

Background And Aim: This study aims to examine the distinguishability of age-related cognitive decline (ARCD) from dementias based on some neurocognitive tests using machine learning.

Materials And Methods: 106 subjects were divided into four groups: ARCD (n=30), probable Alzheimer's disease (AD) (n=20), vascular dementia (VD) (n=21) and amnestic mild cognitive impairment (MCI) (n=35). The following tests were applied to all subjects: The Wechsler memory scale-revised, a clock-drawing, the dual similarities, interpretation of proverbs, word fluency, the Stroop, the Boston naming (BNT), the Benton face recognition, a copying-drawings and Öktem verbal memory processes (Ö-VMPT) tests.

Classification of phosphorus magnetic resonance spectroscopic imaging of brain tumors using support vector machine and logistic regression at 3T.

This study aims classification of phosphorus magnetic resonance spectroscopic imaging ((31)P-MRSI) data of human brain tumors using machine-learning algorithms. The metabolite peak intensities and ratios were estimated for brain tumor and healthy (31)P MR spectra acquired at 3T. The spectra were classified based on metabolite characteristics using logistic regression and support vector machine.