Dermatology

[Treatment of Parastomal Hernia Following Robot-Assisted Radical Cystectomy and Cutaneous Ureterostomy : A Case Report].

The patient was an 82-year-old woman with localized muscle-invasive bladder cancer, who underwent robot-assisted radical cystectomy and cutaneous ureterostomy. On the 3rd day after the operation, she suddenly complained of abdominal pain. Abdominal computed tomographic scans revealed parastomal hernia. The patient underwent manual reduction of hernia, but had repeated recurrences of parastomal her...

Perceptions of Artificial Intelligence Integration into Dermatology Clinical Practice: A Cross-Sectional Survey Study.

BACKGROUND: Artificial intelligence (AI) is a growing field in dermatology and has great potential for integration into clinical practice. Our objective was to assess the perceptions of artificial intelligence in dermatology practice.

Towards gender equity in artificial intelligence and machine learning applications in dermatology.

There has been increased excitement around the use of machine learning (ML) and artificial intelligence (AI) in dermatology for the diagnosis of skin ...

Checklist for Evaluation of Image-Based Artificial Intelligence Reports in Dermatology: CLEAR Derm Consensus Guidelines From the International Skin Imaging Collaboration Artificial Intelligence Working Group.

IMPORTANCE: The use of artificial intelligence (AI) is accelerating in all aspects of medicine and has the potential to transform clinical care and de...

Foundations of Lesion Detection Using Machine Learning in Clinical Neuroimaging.

This chapter describes technical considerations and current and future clinical applications of lesion detection using machine learning in the clinica...

Deep-learning approach for automated thickness measurement of epithelial tissue and scab using optical coherence tomography.

SIGNIFICANCE: In order to elucidate therapeutic treatment to accelerate wound healing, it is crucial to understand the process underlying skin wound h...

Acne detection and severity evaluation with interpretable convolutional neural network models.

BACKGROUND: Acne vulgaris is one of the most prevalent skin conditions, which harms not only the patients' physiological conditions, but also their me...

Identification of Potential Drug Therapy for Dermatofibrosarcoma Protuberans with Bioinformatics and Deep Learning Technology.

BACKGROUND: Dermatofibrosarcoma protuberans (DFSP) is a rare mesenchymal tumor that is primarily treated with surgery. Targeted therapy is a promising...

The Use of Machine Learning in MicroRNA Diagnostics: Current Perspectives.

MicroRNAs constitute small non-coding RNAs that play a pivotal role in regulating the translation and degradation of mRNA and have been associated wit...

Deep learning-based classification of breast cancer cells using transmembrane receptor dynamics.

MOTIVATION: Motions of transmembrane receptors on cancer cell surfaces can reveal biophysical features of the cancer cells, thus providing a method fo...

Novel strategy for applying hierarchical density-based spatial clustering of applications with noise towards spectroscopic analysis and detection of melanocytic lesions.

Advancements in dermoscopy techniques have elucidated identifiable characteristics of melanoma which revolve around the asymmetrical constitution of m...

Machine learning for the identification of decision boundaries during the transition from radial to vertical growth phase superficial spreading melanomas.

The objective of this study was to compute threshold values for the diameter of superficial spreading melanomas (SSMs) at which the radial growth phas...

The potential of using artificial intelligence to improve skin cancer diagnoses in Hawai'i's multiethnic population.

Skin cancer remains the most commonly diagnosed cancer in the USA with more than 1 million new cases each year. Melanomas account for about 1% of all ...

A Learning Framework for Medical Image-Based Intelligent Diagnosis from Imbalanced Datasets.

Medical image classification and diagnosis based on machine learning has made significant achievements and gradually penetrated the healthcare industr...

Optical topometry and machine learning to rapidly phenotype stomatal patterning traits for maize QTL mapping.

Stomata are adjustable pores on leaf surfaces that regulate the tradeoff of CO2 uptake with water vapor loss, thus having critical roles in controllin...

Clinical Artificial Intelligence Applications in Radiology: Chest and Abdomen.

Organ segmentation, chest radiograph classification, and lung and liver nodule detections are some of the popular artificial intelligence (AI) tasks i...

A Machine learning model trained on dual-energy CT radiomics significantly improves immunotherapy response prediction for patients with stage IV melanoma.

BACKGROUND: To assess the additive value of dual-energy CT (DECT) over single-energy CT (SECT) to radiomics-based response prediction in patients with...

A Partial Label-Based Machine Learning Approach For Cervical Whole-Slide Image Classification: The Winning TissueNet Solution.

Cervical cancer is the fourth most common cancer in women worldwide. To determine early treatment for patients, it is critical to accurately classify ...

A new machine learning based user-friendly software platform for automatic radiomics modeling and analysis.

Supervised machine learning methods are usually used to build a custom model for disease diagnosis and auxiliary prognosis in radiomics studies. A cla...

Deformable Dilated Faster R-CNN for Universal Lesion Detection in CT Images.

Cancer is a major public health issue and takes the second-highest toll of deaths caused by non-communicable diseases worldwide. Automatically detecti...