The art of diagnosing rare skin tumors: Can DL-CNNs enhance dermatologists' diagnostic accuracy?

Importance: Deep learning convolutional neural networks (DL-CNN) achieved diagnostic accuracies comparable to dermatologists in controlled test environments. However, their performance in diagnosing rare skin tumors (RST) remains unclear. This study aimed to evaluate a binary DL-CNN's diagnostic performance in RST and assess the level of support for an international group of dermatologists.

Identifying melanoma among benign simulators - Is there a role for deep learning convolutional neural networks? (MelSim Study).

Importance: Early detection of cutaneous melanoma (CM) is crucial for patient survival, yet avoiding overdiagnosis remains essential. Differentiating CM from benign melanoma simulators (MelSim) is challenging due to overlapping features. Deep learning convolutional neural networks (DL-CNNs) have demonstrated dermatologist-level accuracy in identifying CM.

Optical super-high magnification dermoscopy of benign and malignant melanocytic lesions in correlation with histopathology.

Background And Objectives: Technical advances have allowed for significant improvements in imaging techniques in recent years. Specifically, lesions can now be depicted at a much higher magnification - up to 400 x - using optical super-high magnification dermoscopy (OSHMD).

Patients And Methods: This is a retrospective, observational study assessing 99 melanocytic lesions in patients from the University Hospital Heidelberg.