Background: Studies have demonstrated differences in colors and dermoscopic structures observed with polarized dermoscopes (PDs) and nonpolarized dermoscopes (NPDs).

Objective: The objective was to evaluate whether diagnosis and diagnostic confidence changes when viewing dermoscopic images from NPDs and PDs.

Methods: A total of 100 dermatologists participated in the study. Twenty-five pigmented lesions were shown in the study, consisting of 7 seborrheic keratoses (SK), 3 basal cell carcinomas, 2 atypical nevi, 5 malignant melanomas (MM), 3 dermatofibromas, 3 blue nevi, and 2 hemangiomas. Two images of each lesion (one NPD and one PD) were included. The McNemar test and paired t-test were used for the statistical analysis.

Results: Ninety-one participants completed the study. Significant differences in the diagnoses were observed for the SK, atypical nevus, and MM images. Seventy-five percent and 59% of the final participants correctly diagnosed SK when presented with the NPD and PD images, respectively. For MM, 23 and 34% made the correct diagnoses with the NPD and PD images, respectively.

Conclusions: Viewing lesions with NPD versus PD can affect the diagnosis and diagnostic confidence of physicians that are novices with dermoscopy. Further studies including physicians at different expertise levels and a larger sample of lesions are needed to further explore the differences.

Download full-text PDF

Source
http://dx.doi.org/10.1111/j.1524-4725.2008.34293.xDOI Listing

Publication Analysis

Top Keywords

dermoscopic images
8
diagnosis diagnostic
8
diagnostic confidence
8
npd images
8
images
6
differences
4
differences dermoscopic
4
images nonpolarized
4
nonpolarized dermoscope
4
dermoscope polarized
4

Similar Publications

Introduction: Psoriasis is a chronic inflammatory skin disorder affecting millions worldwide. Dermoscopy and proximal nailfold capillaroscopy have emerged as valuable tools for understanding the pathophysiology and treatment response of psoriasis lesions.

Objectives: This study aimed to contribute to the limited literature on using dermoscopic findings to detect treatment effectiveness in patients with psoriasis vulgaris.

View Article and Find Full Text PDF

Background: Skin cancer is the most common cancer worldwide, with melanoma being the deadliest type, though it accounts for less than 5% of cases. Traditional skin cancer detection methods are effective but are often costly and time-consuming. Recent advances in artificial intelligence have improved skin cancer diagnosis by helping dermatologists identify suspicious lesions.

View Article and Find Full Text PDF

: Melanoma, an aggressive form of skin cancer, accounts for a significant proportion of skin-cancer-related deaths worldwide. Early and accurate differentiation between melanoma and benign melanocytic nevi is critical for improving survival rates but remains challenging because of diagnostic variability. Convolutional neural networks (CNNs) have shown promise in automating melanoma detection with accuracy comparable to expert dermatologists.

View Article and Find Full Text PDF

We present an interesting image of eruptive syringoma confirmed by histopathological assessment in a 37-year-old male who was consulted due to numerous brownish small macules and papules resembling maculopapular cutaneous mastocytosis (MPCM). We show difficulties in diagnosing ES, given its rare occurrence and resemblance to other dermatological disorders. Moreover, we discuss the role of dermoscopy and reflectance confocal microscopy in the differential diagnosis of syringoma.

View Article and Find Full Text PDF

Skin Cancer Detection Using Transfer Learning and Deep Attention Mechanisms.

Diagnostics (Basel)

January 2025

College of Computer and Information Sciences, King Saud University, Riyadh 11451, Saudi Arabia.

Early and accurate diagnosis of skin cancer improves survival rates; however, dermatologists often struggle with lesion detection due to similar pigmentation. Deep learning and transfer learning models have shown promise in diagnosing skin cancers through image processing. Integrating attention mechanisms (AMs) with deep learning has further enhanced the accuracy of medical image classification.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!