Quantitative analysis of lesion images to evaluate the efficiency of vascular-targeted photodynamic therapy for port-wine stains: Four case reports.

SIGNIFICANCE: Vascular-targeted photodynamic therapy (V-PDT) is a clinically approved therapeutic approach for treating vascular-related diseases, such as port-wine stains (PWS). For accurate treatment, varying light irradiance is required for different lesions due to the irregularity of vascular size, shape and degree of disease, which commonly alters during different stages of V-PDT. This makes quantitative analysis of the treatment efficiency urgently needed. APPROACH: Lesion images pre- and post- V-PDT treatment of patients with PWS were used to construct a quantitative method to evaluate the differences among lesions. Image analysis techniques were applied to evaluate the V-PDT efficiency for PWS by determining the Euclidean distances and two-dimensional correlation coefficients. RESULTS: According to the image analysis, V-PDT with good treatment efficiency resulted in a larger Euclidean distance and a smaller correlation coefficient compared with the case having lower V-PDT efficiency. CONCLUSIONS: A new method to quantify the Euclidean distances and correlation coefficients has been proposed, which is promising for the quantitative analysis of V-PDT efficiency for PWS.

Association Mining of Near Misses in Hydropower Engineering Construction Based on Convolutional Neural Network Text Classification.

Accidents of various types in the construction of hydropower engineering projects occur frequently, which leads to significant numbers of casualties and economic losses. Identifying and eliminating near misses are a significant means of preventing accidents. Mining near-miss data can provide valuable information on how to mitigate and control hazards. However, most of the data generated in the construction of hydropower engineering projects are semi-structured text data without unified standard expression, so data association analysis is time-consuming and labor-intensive. Thus, an artificial intelligence (AI) automatic classification method based on a convolutional neural network (CNN) is adopted to obtain structured data on near-miss locations and near-miss types from safety records. The apriori algorithm is used to further mine the associations between "locations" and "types" by scanning structured data. The association results are visualized using a network diagram. A Sankey diagram is used to reveal the information flow of near-miss specific objects using the "location ⟶ type" strong association rule. The proposed method combines text classification, association rules, and the Sankey diagrams and provides a novel approach for mining semi-structured text. Moreover, the method is proven to be useful and efficient for exploring near-miss distribution laws in hydropower engineering construction to reduce the possibility of accidents and efficiently improve the safety level of hydropower engineering construction sites.