Defining disease severity in atopic dermatitis and psoriasis for the application to biomarker research: an interdisciplinary perspective

British Journal of Dermatology, Год журнала: 2024, Номер 191(1), С. 14 - 23

Опубликована: Фев. 27, 2024

Abstract More severe atopic dermatitis and psoriasis are associated with a higher cumulative impact on quality of life, multimorbidity healthcare costs. Proactive, early intervention in those most at risk disease may reduce this burden modify the trajectory to limit progression. The lack reliable biomarkers for at-risk group represents barrier such paradigm shift practice. To expedite discovery validation, BIOMarkers Atopic Dermatitis Psoriasis (BIOMAP) consortium (a large-scale European, interdisciplinary research initiative) has curated clinical molecular data across diverse study designs sources including cross-sectional cohort studies (small-scale through large multicentre registries), trials, electronic health records population-based biobanks. We map all dataset severity instruments measures three key domains (symptoms, inflammatory activity course), describe important codependencies relationships variables domains. prioritize definitions more reference international consensus, standards and/or expert opinion. Key factors consider when analysing datasets these types include explicit consideration biomarker purpose context, candidate particular point time over how they related, taking stage development into account selecting analyses, validating associations outcomes using both physician- patient-reported outputs from exercise will ensure coherence focus BIOMAP so that mechanistic insights clinically relevant, patient-centric generalizable current future efforts.

Язык: Английский

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Revolutionizing teledermatology: Exploring the integration of artificial intelligence, including Generative Pre-trained Transformer chatbots for artificial intelligence-driven anamnesis, diagnosis, and treatment plans

Clinics in Dermatology, Год журнала: 2024, Номер 42(5), С. 492 - 497

Опубликована: Июнь 27, 2024

Язык: Английский

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Deep learning models across the range of skin disease

npj Digital Medicine, Год журнала: 2024, Номер 7(1)

Опубликована: Фев. 10, 2024

We explore the evolving landscape of diagnostic artificial intelligence (AI) in dermatology, particularly focusing on deep learning models for a wide array skin diseases beyond cancer. critically analyze current state AI its potential enhancing accuracy, and challenges it faces terms bias, applicability, therapeutic recommendations.

Язык: Английский

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Deep Learning Algorithms for Assessment of Post‐Thyroidectomy Scar Subtype

Dermatologic Therapy, Год журнала: 2025, Номер 2025(1)

Опубликована: Янв. 1, 2025

The rising incidence of thyroid cancer globally is increasing the number thyroidectomies, causing visible scars that can greatly affect quality life due to cosmetic, psychological, and social impacts. In this study, we explored application deep learning algorithms objectively assess post‐thyroidectomy scar morphology using computer‐aided diagnosis. This study was approved by Institutional Review Board Yonsei University College Medicine (approval no. 3‐2021‐051). A dataset comprising 7524 clinical photographs from 3565 patients with utilized. We developed a model convolutional neural network (CNN), specifically ResNet 50 introduced multiple photography (MCPL) method. MCPL method aimed enhance model’s understanding considering characteristics images same lesion per patient. primary outcome, measured area under receiver operating characteristic curve (AUROC), demonstrated superior performance in classifying subtypes compared baseline model. Confidence variation analysis showed reduced discrepancies model, emphasizing its robustness. Furthermore, conducted decision involving five physicians evaluate impact on diagnostic accuracy agreement. Results indicated enhanced reliability subtype determination when confidence scores were integrated into decision‐making. Our findings suggest learning, particularly method, an effective reliable tool for subtypes. approach holds promise assisting professionals improving precision, aiding therapeutic planning, ultimately enhancing patient outcomes management scars.

Язык: Английский

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Unlocking Responsive and Unresponsive Signatures: A Transfer Learning Approach for Automated Classification in Cutaneous Leishmaniasis Lesions

Transboundary and Emerging Diseases, Год журнала: 2025, Номер 2025(1)

Опубликована: Янв. 1, 2025

Cutaneous leishmaniasis (CL) remains a significant global public health disease, with the critical distinction and exact detection between responsive unresponsive cases dictating treatment strategies patient outcomes. However, image‐based methods for differentiating these groups are unexplored. This study addresses this gap by developing deep learning (DL) model utilizing transfer to automatically identify responses in CL lesions. A dataset of 102 lesion images (51 per class; equally distributed across train, test, validation sets) is employed. The DenseNet161, VGG16, ResNet18 networks, pretrained on massive image dataset, fine‐tuned our specific task. models achieved an accuracy 76.47%, 73.53%, 55.88% test data, respectively, sensitivity 80%, 75%, 100% specificity 73.68%, 72.22%, 53.12%, individually. Transfer successfully addressed limited sample size challenge, demonstrating models’ potential real‐world application. work underscores significance automated response CL, paving way improved While acknowledging limitations like size, need collaborative efforts emphasized expand datasets further refine model. approach stands as beacon hope contest against illuminating path toward future where data‐driven diagnostics guide effective alleviate suffering countless patients. Moreover, could be turning point eliminating important widespread disease.

Язык: Английский

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A smart facial acne disease monitoring for automate severity assessment using AI-enabled cloud-based internet of things

Deleted Journal, Год журнала: 2025, Номер 28(1)

Опубликована: Фев. 17, 2025

Язык: Английский

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Eczema Severity Scoring in Skin of Color: A Review of Current Best Practice and Need for Future Improvement

Journal of Investigative Dermatology, Год журнала: 2025, Номер unknown

Опубликована: Фев. 1, 2025

Язык: Английский

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Systematic review of artificial intelligence methods for detection and segmentation of unruptured intracranial aneurysms using medical imaging

Medical & Biological Engineering & Computing, Год журнала: 2025, Номер unknown

Опубликована: Март 17, 2025

Язык: Английский

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A Bibliometric Review of Deep Learning Approaches in Skin Cancer Research

Computation, Год журнала: 2025, Номер 13(3), С. 78 - 78

Опубликована: Март 19, 2025

Early detection of skin cancer is crucial for successful treatment and improved patient outcomes. Medical images play a vital role in this process, serving as the primary data source both traditional modern diagnostic approaches. This study aims to provide an overview significant medical highlight developments use deep learning early diagnosis. The scope survey includes in-depth exploration state-of-the-art methods, evaluation public datasets commonly used training validation, bibliometric analysis recent advancements field. focuses on publications Scopus database from 2019 2024. search string find articles by their abstracts, titles, keywords, several datasets, like HAM ISIC, ensuring relevance topic. Filters are applied based year, document type, language. identified 1697 articles, predominantly comprising journal conference proceedings. shows that number has increased over past five years. growth driven not only developed countries but also developing countries. Dermatology departments various hospitals advancing methods. In addition identifying publication trends, reveals underexplored areas encourage new explorations using VOSviewer Bibliometrix applications.

Язык: Английский

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An Effective Algorithm for Skin Disease Segmentation Combining Inter-channel Features and Spatial Feature Enhancement

Lecture notes in computer science, Год журнала: 2025, Номер unknown, С. 109 - 128

Опубликована: Янв. 1, 2025

Язык: Английский

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