Latest AI and machine learning research in dermatology for healthcare professionals.
Assessment of brain lesions on MRI is crucial for research in multiple sclerosis (MS). Manual segmentation is time consuming and inconsistent. We aimed to develop an automated MS lesion segmentation algorithm for T2-weighted fluid-attenuated inversion recovery (FLAIR) MRI. We developed FLAIR Lesion Analysis in Multiple Sclerosis (FLAMeS), a deep learning-based MS lesion segmentation algorithm base...
Dengue, chikungunya, and Zika are diseases of major human concern. Differential diagnosis is complicated in children and adolescents by their overlapping clinical features (signs, symptoms, and complete blood count results). Few studies have directly compared the three diseases. We aimed to identify distinguishing pediatric characteristics of each disease. Data were derived from laboratory-confirm...
MRI is critical for the detection of subtle cortical pathology in epilepsy surgery assessment. This can be aided by improved MRI quality and resolutio...
Our aim is to demonstrate that multimodal deep learning can enhance the accuracy of classifying skin lesions using both images and textual description...
Gadolinium-based Contrast Agents (GBCAs) are used in brain MRI exams to improve the visualization of pathology and improve the delineation of lesions....
Knee osteoarthritis (OA) is a leading cause of disability worldwide, with early identification of structural changes critical for improving patient ou...
While Deep Learning (DL) models trained on Magnetic Resonance Imaging (MRI) have shown promise for prostate cancer detection, their lack of direct bio...
Molecular and cellular alterations to the normal pseudostratified columnar bronchial epithelium results in the development of bronchial premalignant l...
Recent advances in Artificial Intelligence (AI) have started disrupting the healthcare industry, especially medical imaging, and AI devices are increa...
Post-translational modifications (PTMs) represent a fourth dimension of the genetic code, orchestrated by the Golgi apparatus and central to the biolo...
Spatial transcriptomics technology remains resource-intensive and unlikely to be routinely adopted for patient care soon. This hinders the development...
Vision-language models (VLMs), with their ability to integrate visual and textual information, have enabled unified and interpretable multimodal reaso...
Skin cancer, one of the most prevalent forms of cancer globally, demands early and accurate diagnosis to improve patient outcomes. In this paper, we p...
Biomedical data are inherently multimodal, capturing complementary aspects of a patient condition. Deep learning (DL) algorithms that integrate multip...
Multiple sclerosis (MS) is a chronic inflammatory neurodegenerative disorder of the central nervous system (CNS) and represents the leading cause of n...
Pre-procedural risk prediction of 30-day all-cause mortality after percutaneous coronary intervention (PCI) aids in clinical decision-making and bench...
Retinal breaks are critical lesions that can lead to retinal detachment and vision loss if not detected and treated early. Automated and precise delin...
Post-stroke pain (PSP) affects nearly half of stroke survivors, severely compromising quality of life. The causes of PSP remain underexplored, althoug...
Widespread access to imaging technologies and stronger machine learning (ML) architectures for dermatology tasks such as malignancy prediction have sp...
Breast cancer is a leading malignancy threatening women’s health globally, making early and accurate diagnosis crucial. Ultrasound is a key screening ...