AI Medical Compendium Topic:
Diagnosis, Differential

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An approach to fuzzy soft sets in decision making based on grey relational analysis and Dempster-Shafer theory of evidence: An application in medical diagnosis.

Artificial intelligence in medicine
OBJECTIVE: The existing methods of fuzzy soft sets in decision making are mainly based on different kinds of level soft sets, and it is very difficult for decision makers to select a suitable level soft set in most instances. The goal of this paper i...

Support vector machine classification of brain metastasis and radiation necrosis based on texture analysis in MRI.

Journal of magnetic resonance imaging : JMRI
PURPOSE: To develop a classification model using texture features and support vector machine in contrast-enhanced T1-weighted images to differentiate between brain metastasis and radiation necrosis.

Robust phase-based texture descriptor for classification of breast ultrasound images.

Biomedical engineering online
BACKGROUND: Classification of breast ultrasound (BUS) images is an important step in the computer-aided diagnosis (CAD) system for breast cancer. In this paper, a novel phase-based texture descriptor is proposed for efficient and robust classifiers t...

Robotic and video-assisted thoracic surgery lung segmentectomy for malignant and benign lesions.

Interactive cardiovascular and thoracic surgery
OBJECTIVES: The experience with robotic techniques (RATS) and video-assisted thoracic surgery (VATS) in pulmonary segmentectomy is still limited. We evaluated our prospectively recorded database to compare two different minimally invasive techniques.

Analysis of underlying causes of inter-expert disagreement in retinopathy of prematurity diagnosis. Application of machine learning principles.

Methods of information in medicine
OBJECTIVE: Inter-expert variability in image-based clinical diagnosis has been demonstrated in many diseases including retinopathy of prematurity (ROP), which is a disease affecting low birth weight infants and is a major cause of childhood blindness...

Using Relevance Feedback to Distinguish the Changes in EEG During Different Absence Seizure Phases.

Clinical EEG and neuroscience
We carried out a series of statistical experiments to explore the utility of using relevance feedback on electroencephalogram (EEG) data to distinguish between different activity states in human absence epilepsy. EEG recordings from 10 patients with ...

Machine learning models for the differential diagnosis of vascular parkinsonism and Parkinson's disease using [(123)I]FP-CIT SPECT.

European journal of nuclear medicine and molecular imaging
PURPOSE: The study's objective was to develop diagnostic predictive models using data from two commonly used [(123)I]FP-CIT SPECT assessment methods: region-of-interest (ROI) analysis and whole-brain voxel-based analysis.

Artificial neural networks in neurosurgery.

Journal of neurology, neurosurgery, and psychiatry
Artificial neural networks (ANNs) effectively analyze non-linear data sets. The aimed was A review of the relevant published articles that focused on the application of ANNs as a tool for assisting clinical decision-making in neurosurgery. A literatu...

Machine Learning Analysis of Single-Voxel Proton MR Spectroscopy for Differentiating Solitary Fibrous Tumors and Meningiomas.

NMR in biomedicine
Solitary fibrous tumor (SFT), formerly known as hemangiopericytoma, is an uncommon brain tumor often confused with meningioma on MRI. Unlike meningiomas, SFTs exhibit a myoinositol peak on magnetic resonance spectroscopy (MRS). This study aimed to de...