Convolutional neural network assistance significantly improves dermatologists' diagnosis of cutaneous tumours using clinical images.

BACKGROUND: Convolutional neural networks (CNNs) have demonstrated expert-level performance in cutaneous tumour classification using clinical images, but most previous studies have focused on dermatologist-versus-CNN comparisons rather than their combination. The objective of our study was to evaluate the potential impact of CNN assistance on dermatologists for clinical image interpretation. METHODS: A multi-class CNN was trained and validated using a dataset of 25,773 clinical images comprising 10 categories of cutaneous tumours. The CNN's performance was tested on an independent dataset of 2107 images. A total of 400 images (40 per category) were randomly selected from the test dataset. A fully crossed, self-control, multi-reader multi-case (MRMC) study was conducted to compare the performance of 18 board-certified dermatologists (experience: 13/18 ≤ 10 years; 5/18＞10 years) in interpreting the 400 clinical images with or without CNN assistance. RESULTS: The CNN achieved an overall accuracy of 78.45% and kappa of 0.73 in the classification of 10 types of cutaneous tumours on 2107 images. CNN-assisted dermatologists achieved a higher accuracy (76.60% vs. 62.78%, P < 0.001) and kappa (0.74 vs. 0.59, P < 0.001) than unassisted dermatologists in interpreting the 400 clinical images. Dermatologists with less experience benefited more from CNN assistance. At the binary classification level (malignant or benign), the sensitivity (89.56% vs. 83.21%, P < 0.001) and specificity (87.90% vs. 80.92%, P < 0.001) of dermatologists with CNN assistance were also significantly improved than those without. CONCLUSIONS: CNN assistance improved dermatologist accuracy in interpreting cutaneous tumours and could further boost the acceptance of this new technique.

Reductive transformation of 2,4-dichlorophenoxyacetic acid by nanoscale and microscale Fe3O4 particles.

Reductive transformation of 2,4-dichlorophenoxyacetic acid (2,4-D) by nanoscale and microscale Fe(3)O(4) was investigated and compared. Disappearance of the parent species and formation of reaction intermediates and products were kinetically analyzed. Results suggest that the transformation of 2,4-D followed a primary pathway of its complete reduction to phenol and a secondary pathway of sequential reductive hydrogenolysis to 2,4-dichlorophenol (2,4-DCP), chlorophenol (2-CP, 4-CP) and phenol. About 65% of 2,4-D with initial concentration of 50 micro M was transformed within 48 h in the presence of 300 mg L(-1) nanoscale Fe(3)O(4), and the reaction rates increased with increasing dosage of nanoscale Fe(3)O(4). The decomposition of 2,4-D proceeded rapidly at optimum pH 3.0. Chloride was identified as a reduction product for 2,4-D in the magnetite-water system. Reductive transformation of 2,4-D by microscale Fe(3)O(4) was slower than that by nanoscale Fe(3)O(4). The reactions apparently followed pseudo-first-order kinetics with respect to the 2,4-D transformation. The degradation rate of 2,4-D decreased with the increase of initial 2,4-D concentration. In addition, anions had a significant adverse impact on the degradation efficiency of 2,4-D.