Dermatology

Computer-Aided Design of Antimicrobial Peptides: Are We Generating Effective Drug Candidates?

Antimicrobial peptides (AMPs), especially antibacterial peptides, have been widely investigated as potential alternatives to antibiotic-based therapies. Indeed, naturally occurring and synthetic AMPs have shown promising results against a series of clinically relevant bacteria. Even so, this class of antimicrobials has continuously failed clinical trials at some point, highlighting the importance ...

Diagnostic performance of a deep learning convolutional neural network in the differentiation of combined naevi and melanomas.

BACKGROUND: Deep learning convolutional neural networks (CNN) may assist physicians in the diagnosis of melanoma. The capacity of a CNN to differentiate melanomas from combined naevi, the latter representing well-known melanoma simulators, has not been investigated.

Melanoma recognition by a deep learning convolutional neural network-Performance in different melanoma subtypes and localisations.

BACKGROUND: Deep learning convolutional neural networks (CNNs) show great potential for melanoma diagnosis. Melanoma thickness at diagnosis among othe...

Automatic lesion segmentation and classification of hepatic echinococcosis using a multiscale-feature convolutional neural network.

Hepatic echinococcosis (HE) is a life-threatening liver disease caused by parasites that requires a precise diagnosis and proper treatments. To assess...

An Adversarial Learning Approach to Medical Image Synthesis for Lesion Detection.

The identification of lesion within medical image data is necessary for diagnosis, treatment and prognosis. Segmentation and classification approaches...

Improved skin lesions detection using color space and artificial intelligence techniques.

Automatic skin lesion image identification is of utmost importance to develop a fully automatized computer-aided skin analysis system. This will be h...

Breast Reconstruction Utilizing Buried Dermato-cutaneous Skin Flaps and Immediate Adipocyte Transfer: A Minimally Invasive Autologous Breast Reconstruction Technique.

UNLABELLED: Autologous breast reconstruction historically required flaps that were invasive, required prolonged operative times and recoveries, and re...

Impact of galactooligosaccharides delivered in ovo on mitigating negative effects of heat stress on performance and welfare of broilers.

Galactooligosaccharides (GOS) delivered in ovo improve intestinal health of broiler chickens. This study aimed to demonstrate the impact of in ovo sti...

A modern approach to identifying and characterizing child asthma and wheeze phenotypes based on clinical data.

'Asthma' is a complex disease that encapsulates a heterogeneous group of phenotypes and endotypes. Research to understand these phenotypes has previou...

Diagnostic Value of Lesion-specific Measurement of Myocardial Blood Flow Using Hybrid PET/CT.

BACKGROUND: We evaluated whether lesion-specific measurement of myocardial blood flow (MBF) and flow reserve (MFR) by hybrid imaging of myocardial per...

A Machine-Learning Approach to Identify a Prognostic Cytokine Signature That Is Associated With Nivolumab Clearance in Patients With Advanced Melanoma.

Lower clearance of immune checkpoint inhibitors is a predictor of improved overall survival (OS) in patients with advanced cancer. We investigated a n...

Utilizing Machine Learning for Image Quality Assessment for Reflectance Confocal Microscopy.

In vivo reflectance confocal microscopy (RCM) enables clinicians to examine lesions' morphological and cytological information in epidermal and dermal...

A multi-path 2.5 dimensional convolutional neural network system for segmenting stroke lesions in brain MRI images.

Automatic identification of brain lesions from magnetic resonance imaging (MRI) scans of stroke survivors would be a useful aid in patient diagnosis a...

Automatic detection of lesion load change in Multiple Sclerosis using convolutional neural networks with segmentation confidence.

The detection of new or enlarged white-matter lesions is a vital task in the monitoring of patients undergoing disease-modifying treatment for multipl...

Predictive models for patients with lung carcinomas to identify EGFR mutation status via an artificial neural network based on multiple clinical information.

PURPOSE: Epidermal growth factor receptor (EGFR) mutation testing has several limitations. Therefore, we built predictive models to determine the EGFR...

Skin lesion segmentation using high-resolution convolutional neural network.

BACKGROUND AND OBJECTIVE: Skin lesion segmentation is an important but challenging task in computer-aided diagnosis of dermoscopy images. Many segment...

Machine Learning Enables High-Throughput Phenotyping for Analyses of the Genetic Architecture of Bulliform Cell Patterning in Maize.

Bulliform cells comprise specialized cell types that develop on the adaxial (upper) surface of grass leaves, and are patterned to form linear rows alo...

Histopathology-guided mass spectrometry differentiates benign nevi from malignant melanoma.

PURPOSE: Distinguishing benign nevi from malignant melanoma using current histopathological criteria may be very challenging and is one the most diffi...

Limited One-time Sampling Irregularity Map (LOTS-IM) for Automatic Unsupervised Assessment of White Matter Hyperintensities and Multiple Sclerosis Lesions in Structural Brain Magnetic Resonance Images.

We present the application of limited one-time sampling irregularity map (LOTS-IM): a fully automatic unsupervised approach to extract brain tissue ir...

Deep Learning for Lesion Detection, Progression, and Prediction of Musculoskeletal Disease.

Deep learning is one of the most exciting new areas in medical imaging. This review article provides a summary of the current clinical applications of...