Dermatology

Latest AI and machine learning research in dermatology for healthcare professionals.

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Subcategories: Atopy Psoriasis
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Automated external cervical resorption segmentation in cone-beam CT using local texture features

External cervical resorption (ECR) is a resorptive process affecting teeth. While in some patients, active resorption ceases and gets replaced by osseous tissue, in other cases, the resorption progresses and ultimately results in tooth loss. For proper ECR assessment, cone-beam computed tomography (CBCT) is the recommended imaging modality, enabling a 3-D characterization of these lesions. While...

Quantifying Itch and its Impact on Sleep Using Machine Learning and Radio Signals

Chronic itch affects 13% of the US population, is highly debilitating, and underlies many medical conditions. A major challenge in clinical care and new therapeutics development is the lack of an objective measure for quantifying itch, leading to reliance on subjective measures like patients' self-assessment of itch severity. In this paper, we show that a home radio device paired with artificial...

MedSegDiffNCA: Diffusion Models With Neural Cellular Automata for Skin Lesion Segmentation

Denoising Diffusion Models (DDMs) are widely used for high-quality image generation and medical image segmentation but often rely on Unet-based arch...

ADSI-MIMO: Adaptive stain imputation with multi-input and multi-output learning for multiplex immunofluorescence imaging

Multiplex immunofluorescence (mIF) imaging plays a crucial role in studying multiple biomarkers and their interactions within the tumour microenvironm...

Mapping and reprogramming human tissue microenvironments with MintFlow

Tissue microenvironments reprogram local cellular states in disease, yet current computational spatial methods remain descriptive and do not simulate ...

Pixel-Precise Lesion Localization in WSIs via Weakly Supervised Streaming Convolution with ReLSE and Adaptive Self-Training

A robust artificial intelligence-assisted workflow for tumor assessment in pathology requires not only accurate classification but also precise lesion...

ATOMIC: A graph attention neural network for ATOpic dermatitis prediction on human gut MICrobiome

Atopic dermatitis (AD) is a chronic inflammatory skin disease driven by complex interactions among genetic, environmental, and microbial factors; howe...

Transcriptomic profiling and machine learning uncover gene signatures of psoriasis endotypes and disease severity

Despite increased understanding of psoriasis pathogenesis, molecular classification of clinical phenotypes and disease severity is poorly defined. Kno...

Machine Learning Enables Rapid Assessment of Disease Vulnerability in a Threatened Cetacean Population

Cetaceans (whales and dolphins) are important ecosystem sentinels but face growing threats from major disease-related mortality events expected to int...

Accelerating Insight Discovery in Large Biomedical Text with Scalable Processing Framework

Large language models are increasingly being used by dermatology professionals to support diagnostic investigation, patient education, and medical res...

Scratcher: An automated machine-vision tool for dissecting the neural basis of itch

Itch or pruritus invokes a specific reflexive and repetitive directed nocifensive behavioural response, known as scratching. Recent decades have revea...

De novo single-cell biological analysis of drug resistance in human melanoma through a novel deep learning-powered approach

Elucidating drug response mechanisms in human melanoma is crucial for improving treatment outcomes. Although scRNA-seq captures gene expression at the...

Cerebral Organoids Uncover Mechanisms of Neural Activity Changes in Epileptogenesis

Neurological disorders often originate from progressive brain network dysfunctions that start years before symptoms appear. How these changes emerge i...

Non-segmented unsupervised learning of multispectral whole slide images for robust analysis of tissue repair and regeneration

Analyzing whole tissue architecture remains challenging due to the inherent complexity of multicellular organization, variable morphology, and the lim...

SpaPheno: Linking Spatial Transcriptomics to Clinical Phenotypes with Interpretable Machine Learning

Linking spatial transcriptomic data to clinically relevant phenotypes is essential for advancing spatially informed precision oncology. Here, we prese...

Fourier transform infrared spectroscopy enables rapid species discrimination across Malassezia and strain-level typing in M. pachydermatis

Malassezia pachydermatis is a zoophilic yeast found on the skin and in the outer ear canal of many mammals. It normally maintains a commensal lifestyl...

Scalable and universal prediction of cellular phenotypes enables in silico experiments

Biological systems can be interrogated by perturbing individual components and observing the consequences across molecular, cellular, and phenotypic l...

CryoPhold: CryoEM meets AlphaFold and molecular simulation to reveal protein dynamics

Here we are introducing CryoPhold, a modular workflow that unifies AlphaFold-based ensemble generation, Bayesian reweighting against experimental cryo...

PyCLM: programming-free, closed-loop microscopy for real-time measurement, segmentation, and optogenetic stimulation

In cell biology, optical techniques are increasingly used to measure cells’ internal states (biosensors) and to stimulate cellular responses (optogene...

CNN-based learning of single-cell transcriptomes reveals a blood-detectable multi-cancer signature of brain metastasis

Brain metastasis (BrM) is a serious complication of advanced cancers and remains difficult to predict before clinical symptoms appear. To investigate ...

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