Dermatology

Gene Identification and Potential Drug Therapy for Drug-Resistant Melanoma with Bioinformatics and Deep Learning Technology.

BACKGROUND: Melanomas are skin malignant tumors that arise from melanocytes which are primarily treated with surgery, chemotherapy, targeted therapy, immunotherapy, radiation therapy, etc. Targeted therapy is a promising approach to treating advanced melanomas, but resistance always occurs. This study is aimed at identifying the potential target genes and candidate drugs for drug-resistant melanom...

An automatic method to quantify trichomes in Arabidopsis thaliana.

Trichomes are unicellular or multicellular hair-like appendages developed on the aerial plant epidermis of most plant species that act as a protective barrier against natural hazards. For this reason, evaluating the density of trichomes is a valuable approach for elucidating plant defence responses to a continuous challenging environment. However, previous methods for trichome counting, although r...

Histologic Screening of Malignant Melanoma, Spitz, Dermal and Junctional Melanocytic Nevi Using a Deep Learning Model.

OBJECTIVE: The integration of an artificial intelligence tool into pathologists' workflow may lead to a more accurate and timely diagnosis of melanocy...

Validating Automatic Concept-Based Explanations for AI-Based Digital Histopathology.

Digital histopathology poses several challenges such as label noise, class imbalance, limited availability of labelled data, and several latent biases...

Multimodal Sensors with Decoupled Sensing Mechanisms.

Highly sensitive and multimodal sensors have recently emerged for a wide range of applications, including epidermal electronics, robotics, health-moni...

Ability to Predict Melanoma Within 5 Years Using Registry Data and a Convolutional Neural Network: A Proof of Concept Study.

Research relating to machine learning algorithms, including convolutional neural networks, has increased during the past 5 years. The aim of this pilo...

Development and clinical applicability of MRI-based 3D prostate models in the planning of nerve-sparing robot-assisted radical prostatectomy.

The interpretation of conventional MRI may be limited by the two-dimensional presentation of the images. To develop patient-specific MRI prostate-base...

DTP-Net: A convolutional neural network model to predict threshold for localizing the lesions on dermatological macro-images.

Highly focused images of skin captured with ordinary cameras, called macro-images, are extensively used in dermatology. Being highly focused views, th...

Artificial intelligence for the automated single-shot assessment of psoriasis severity.

BACKGROUND: PASI score is globally used to assess disease activity of psoriasis. However, it is relatively complicated and time-consuming, and the sco...

Rainfall prediction using multiple inclusive models and large climate indices.

Rainfall prediction is vital for the management of available water resources. Accordingly, this study used large lagged climate indices to predict rai...

Reduction in Acquisition Time and Improvement in Image Quality in T2-Weighted MR Imaging of Musculoskeletal Tumors of the Extremities Using a Novel Deep Learning-Based Reconstruction Technique in a Turbo Spin Echo (TSE) Sequence.

Background: The aim of this study was to assess the technical feasibility and the impact on image quality and acquisition time of a deep learning-acce...

Development of Lymphopenia during Therapy with Immune Checkpoint Inhibitors Is Associated with Poor Outcome in Metastatic Cutaneous Melanoma.

Predictive markers for immune checkpoint inhibitor (ICI) therapy are needed. Thus, baseline blood counts have been investigated as biomarkers, showing...

A Hybrid Deep Transfer Learning of CNN-Based LR-PCA for Breast Lesion Diagnosis via Medical Breast Mammograms.

One of the most promising research areas in the healthcare industry and the scientific community is focusing on the AI-based applications for real med...

MVFStain: Multiple virtual functional stain histopathology images generation based on specific domain mapping.

To the best of our knowledge, artificial intelligence stain generation is an urgent requirement for histopathology images. Pathological examinations u...

An Online Prognostic Application for Melanoma Based on Machine Learning and Statistics.

BACKGROUND: Melanoma is a common cancer that causes a severe socioeconomic burden. Patients usually turn to plastic surgeons to determine their progno...

Size-adaptive mediastinal multilesion detection in chest CT images via deep learning and a benchmark dataset.

PURPOSE: Many deep learning methods have been developed for pulmonary lesion detection in chest computed tomography (CT) images. However, these method...

Diagnostic performance of artificial intelligence approved for adults for the interpretation of pediatric chest radiographs.

Artificial intelligence (AI) applied to pediatric chest radiographs are yet scarce. This study evaluated whether AI-based software developed for adult...

Systematic perturbation of an artificial neural network: A step towards quantifying causal contributions in the brain.

Lesion inference analysis is a fundamental approach for characterizing the causal contributions of neural elements to brain function. This approach ha...

Radiomics-based machine learning models to distinguish between metastatic and healthy bone using lesion-center-based geometric regions of interest.

Radiomics-based machine learning classifiers have shown potential for detecting bone metastases (BM) and for evaluating BM response to radiotherapy (R...

Deep learning for image-based liver analysis - A comprehensive review focusing on malignant lesions.

Deep learning-based methods, in particular, convolutional neural networks and fully convolutional networks are now widely used in the medical image an...