Dermatology

Demographic, Clinical, and Allergic Characteristics of Children with Eosinophilic Esophagitis in Isfahan, Iran.

Eosinophilic esophagitis (EoE) is a chronic immune-mediated disease isolated to the esophagus Food allergy is thought to play an important role in the pathophysiology of EOE. The aim of this study is to evaluate demographic features and sensitivity of patients with reference to common food allergens. Children with documented EoE were enlisted for this study. Skin prick test and patch test were don...

Prediction of findings at screening colonoscopy using a machine learning algorithm based on complete blood counts (ColonFlag).

Adenomatous polyps are a common precursor lesion for colorectal cancer. ColonFlag is a machine- learning-based algorithm that uses basic patient information and complete blood cell counts (CBC) to identify individuals at elevated risk of colorectal cancer for intensified screening. The purpose of this study was to determine whether ColonFlag is also able to predict the presence of high risk adenom...

Level of circulating steroid hormones in malaria and cutaneous leishmaniasis: a case control study.

Epidemiological and clinical studies have shown a great difference in the severity and prevalence of infectious diseases in men and women and various ...

Artificial Intelligence Based Skin Classification Using GMM.

This study describes the usage of neural community based on the texture evaluation of pores and skin a variety of similarities in their signs, inclusi...

Deep Semantic Segmentation of Kidney and Space-Occupying Lesion Area Based on SCNN and ResNet Models Combined with SIFT-Flow Algorithm.

Renal segmentation is one of the most fundamental and challenging task in computer aided diagnosis systems. In order to overcome the shortcomings of a...

Domain-specific classification-pretrained fully convolutional network encoders for skin lesion segmentation.

BACKGROUND AND OBJECTIVE: Fully convolutional neural networks have been shown to perform well for automated skin lesion segmentation on digital dermat...

Predicting conversion from clinically isolated syndrome to multiple sclerosis-An imaging-based machine learning approach.

Magnetic resonance imaging (MRI) scans play a pivotal role in the evaluation of patients presenting with a clinically isolated syndrome (CIS), as thes...

A graph-based lesion characterization and deep embedding approach for improved computer-aided diagnosis of nonmass breast MRI lesions.

Nonmass-like enhancements are a common but diagnostically challenging finding in breast MRI. Nonmass-like lesions can be described as clusters of spat...

Drug Repurposing Prediction for Immune-Mediated Cutaneous Diseases using a Word-Embedding-Based Machine Learning Approach.

Immune-mediated diseases affect more than 20% of the population, and many autoimmune diseases affect the skin. Drug repurposing (or repositioning) is ...

Skin Cancer Classification Using Convolutional Neural Networks: Systematic Review.

BACKGROUND: State-of-the-art classifiers based on convolutional neural networks (CNNs) were shown to classify images of skin cancer on par with dermat...

Automatic segmentation of the spinal cord and intramedullary multiple sclerosis lesions with convolutional neural networks.

The spinal cord is frequently affected by atrophy and/or lesions in multiple sclerosis (MS) patients. Segmentation of the spinal cord and lesions from...

[An unusual cause of jaundice].

Hepatic impairment is common during hyperthyroidism. It is most often asymptomatic. Hyperthyroidism revealed by jaundice has been rarely described in ...

Machine learning based framework to predict cardiac arrests in a paediatric intensive care unit : Prediction of cardiac arrests.

A cardiac arrest is a life-threatening event, often fatal. Whilst clinicians classify some of the cardiac arrests as potentially predictable, the majo...

Multimodal skin lesion classification using deep learning.

While convolutional neural networks (CNNs) have successfully been applied for skin lesion classification, previous studies have generally considered o...

Automatic Lacunae Localization in Placental Ultrasound Images via Layer Aggregation.

Accurate localization of structural abnormalities is a precursor for image-based prenatal assessment of adverse conditions. For clinical screening and...

Deep-learning-based, computer-aided classifier developed with a small dataset of clinical images surpasses board-certified dermatologists in skin tumour diagnosis.

BACKGROUND: Application of deep-learning technology to skin cancer classification can potentially improve the sensitivity and specificity of skin canc...

Entangled Conditional Adversarial Autoencoder for de Novo Drug Discovery.

Modern computational approaches and machine learning techniques accelerate the invention of new drugs. Generative models can discover novel molecular ...

Performance and clinical impact of machine learning based lung nodule detection using vessel suppression in melanoma patients.

PURPOSE: To evaluate performance and the clinical impact of a novel machine learning based vessel-suppressing computer-aided detection (CAD) software ...

Prediction of postoperative disease-free survival and brain metastasis for HER2-positive breast cancer patients treated with neoadjuvant chemotherapy plus trastuzumab using a machine learning algorithm.

PURPOSE: This study aimed to develop mathematical tools to predict the likelihood of recurrence after neoadjuvant chemotherapy (NAC) plus trastuzumab ...

Machine learning approaches in GIS-based ecological modeling of the sand fly Phlebotomus papatasi, a vector of zoonotic cutaneous leishmaniasis in Golestan province, Iran.

The distribution and abundance of Phlebotomus papatasi, the primary vector of zoonotic cutaneous leishmaniasis in most semi-/arid countries, is a majo...