Latest AI and machine learning research in dermatology for healthcare professionals.
INTRODUCTION: Numerous studies have shown that the diagnostic performance and reproducibility of visual recognition of endometriosis during laparoscopy are poor. The use of artificial intelligence (AI) seems relevant for exhaustive lesion recognition. Standardization of the visual classification of lesions, in the form of an ontology, is an essential prerequisite to enable medical experts to annot...
The clinical intractability of diabetic foot ulcers stems from a profound uncoupling of cutaneous neurovascular networks, rendering standard metabolic and topical interventions largely palliative. Paradoxically, remote orthopedic trauma robustly accelerates distal skin repair, yet the systemic molecular mediators driving this "bone-skin crosstalk" remain undefined, precluding its translation into ...
Endometrial carcinoma ranks among the most common malignancies of the female reproductive system. Accurate early-stage staging is essential for devisi...
OBJECTIVES: There is evidence to suggest that alterations in skin health often co-occur with type 2 diabetes (T2D). However, the underlying mechanisms...
STATEMENT OF PROBLEM: Periapical lesions in teeth with fixed prostheses often remain undiagnosed in routine panoramic radiographic evaluations, leadin...
Biofilms are a major cause of delayed wound healing, yet current biofilm identification methods are limited by invasiveness, processing times, or spec...
Despite the rising incidence of basal cell carcinoma (BCC), few studies have compared treatment outcomes or predicted recurrences using nationwide rea...
Keratinocyte cancers (KCs) are the most prevalent cancers in white-skinned individuals, yet remain underrepresented in cancer registries because repor...
Cutaneous electrophysiology is a fundamental non-invasive technique for assessing electrically active organs such as the brain, heart, and muscles. St...
This study evaluated whether a vendor-neutral deep learning reconstruction (DLR) can improve image quality in accelerated T2-weighted imaging (T2WI) o...
Large language models (LLMs) with multimodal capabilities may support automated assessment of cutaneous disease activity in dermatomyositis (DM). We e...
OBJECTIVES: We developed a transfer learning-based multimodal fusion deep learning model integrating positron emission tomography/computed tomography ...
BACKGROUND: Major depressive disorder (MDD) and vitiligo often occur together, worsening patient outcomes. However, the shared pathogenic mechanisms r...
Recent advances in dermoscopic imaging have expanded diagnostic capabilities beyond conventional contact devices. Non-contact modalities, including sh...
RATIONALE AND OBJECTIVES: The 70-gene signature (MammaPrint) guides risk assessment and treatment in the hormone receptor-positive/human epidermal gro...
OBJECTIVE: To develop and validate a nomogram integrating artificial intelligence (AI)-extracted ultrasound features with clinic pathologic data for n...
Melanoma is one of the most aggressive forms of skin cancer, and its early detection from dermoscopic images remains a significant clinical challenge....
BACKGROUND: Multiple sclerosis (MS) is a chronic neurological disease affecting both white and gray matter of the central nervous system. Despite the ...
Oral potentially malignant disorders (OPMDs), particularly leukoplakia, represent critical precursors to oral cancer requiring systematic monitoring. ...
The development of machine learning models for CT imaging depends on the availability of large, high-quality, and diverse annotated datasets. Although...