Dermatology

Evaluation of Semiautomatic and Deep Learning-Based Fully Automatic Segmentation Methods on [F]FDG PET/CT Images from Patients with Lymphoma: Influence on Tumor Characterization.

The objective is to assess the performance of seven semiautomatic and two fully automatic segmentation methods on [F]FDG PET/CT lymphoma images and evaluate their influence on tumor quantification. All lymphoma lesions identified in 65 whole-body [F]FDG PET/CT staging images were segmented by two experienced observers using manual and semiautomatic methods. Semiautomatic segmentation using absolut...

Interactive Explainable Deep Learning Model Informs Prostate Cancer Diagnosis at MRI.

Background Clinically significant prostate cancer (PCa) diagnosis at MRI requires accurate and efficient radiologic interpretation. Although artificial intelligence may assist in this task, lack of transparency has limited clinical translation. Purpose To develop an explainable artificial intelligence (XAI) model for clinically significant PCa diagnosis at biparametric MRI using Prostate Imaging R...

Current developments and perspectives in psoriasis.

The study of psoriasis has yielded fundamental new insights into immunologic regulation and innovative therapies in a way that few other diseases have...

DeepNAPSI multi-reader nail psoriasis prediction using deep learning.

Nail psoriasis occurs in about every second psoriasis patient. Both, finger and toe nails can be affected and also severely destroyed. Furthermore, na...

New Generation of 3D Virtual Models with Perfusional Zones: Perioperative Assistance for the Best Pedicle Management during Robotic Partial Nephrectomy.

Selective clamping during robot-assisted partial nephrectomy (RAPN) may reduce ischemia-related functional impairment. The intraoperative use of 3D-vi...

Improving lesion conspicuity in abdominal dual-energy CT with deep learning image reconstruction: a prospective study with five readers.

OBJECTIVES: To evaluate image quality, diagnostic acceptability, and lesion conspicuity in abdominal dual-energy CT (DECT) using deep learning image r...

Investigation of radiomics and deep convolutional neural networks approaches for glioma grading.

To determine glioma grading by applying radiomic analysis or deep convolutional neural networks (DCNN) and to benchmark both approaches on broader val...

Integrating single-cell analysis and machine learning to create glycosylation-based gene signature for prognostic prediction of uveal melanoma.

BACKGROUND: Increasing evidence suggests a correlation between glycosylation and the onset of cancer. However, the clinical relevance of glycosylation...

Application of Deep Learning-Based Denoising Technique for Radiation Dose Reduction in Dynamic Abdominal CT: Comparison with Standard-Dose CT Using Hybrid Iterative Reconstruction Method.

The purpose is to evaluate whether deep learning-based denoising (DLD) algorithm provides sufficient image quality for abdominal computed tomography (...

Artificial Intelligence-Based Psoriasis Severity Assessment: Real-world Study and Application.

BACKGROUND: Psoriasis is one of the most frequent inflammatory skin conditions and could be treated via tele-dermatology, provided that the current la...

Differentiating malignant and benign eyelid lesions using deep learning.

Artificial intelligence as a screening tool for eyelid lesions will be helpful for early diagnosis of eyelid malignancies and proper decision-making. ...

EG-TransUNet: a transformer-based U-Net with enhanced and guided models for biomedical image segmentation.

Although various methods based on convolutional neural networks have improved the performance of biomedical image segmentation to meet the precision r...

Hyperspectral imaging-based cutaneous wound classification using neighbourhood extraction 3D convolutional neural network.

OBJECTIVES: Hyperspectral imaging is an emerging imaging modality that beginning to gain attention for medical research and has an important potential...

Robot-assisted sacrohysteropexy vs robot-assisted sacrocolpopexy in women with primary advanced apical prolapse: A retrospective cohort study.

BACKGROUND: This study aimed to evaluate the anatomic and clinical outcomes of robot-assisted sacrohysteropexy (RASH) against robot-assisted sacrocolp...

A deep-learning algorithm to classify skin lesions from mpox virus infection.

Undetected infection and delayed isolation of infected individuals are key factors driving the monkeypox virus (now termed mpox virus or MPXV) outbrea...

Predicting survival after radiosurgery in patients with lung cancer brain metastases using deep learning of radiomics and EGFR status.

The early prediction of overall survival (OS) in patients with lung cancer brain metastases (BMs) after Gamma Knife radiosurgery (GKRS) can facilitate...

A review of deep learning-based multiple-lesion recognition from medical images: classification, detection and segmentation.

Deep learning-based methods have become the dominant methodology in medical image processing with the advancement of deep learning in natural image cl...

SAN-Net: Learning generalization to unseen sites for stroke lesion segmentation with self-adaptive normalization.

There are considerable interests in automatic stroke lesion segmentation on magnetic resonance (MR) images in the medical imaging field, as stroke is ...

Evaluation of the area subscore of the Palmoplantar Pustulosis Area and Severity Index using an attention U-net deep learning algorithm.

Palmoplantar pustulosis (PPP) is a variant of pustular psoriasis involving the palms and soles. The severity of PPP is usually evaluated using the Pal...

Strategy toward Kinase-Selective Drug Discovery.

Kinase drug selectivity is the ground challenge in cancer research. Due to the structurally similar kinase drug pockets, off-target inhibitor toxicity...