Robust automated reading of the skin prick test via 3D imaging and parametric surface fitting

Lesion segmentation using 3D scan and deep learning for the evaluation of facial portwine stain birthmarks

BACKGROUND

Portwine stain (PWS) birthmarks are congenital vascular malformations. The quantification of PWS area is an important step in lesion classification and treatment evaluation.

AIMS

The aim of this study was to evaluate the combination of 3D scan with deep learning for automated PWS area quantization.

MATERIALS AND METHODS

PWS color was measured using a portable spectrophotometer. PWS patches (29.26-45.82 cm2) of different color and shape were generated for 2D and 3D PWS model. 3D images were acquired by a handheld 3D scanner to create texture maps. For semantic segmentation, an improved DeepLabV3+ network was developed for PWS lesion extraction from texture mapping of 3D images. In order to achieve accurate extraction of lesion regions, the convolutional block attention module (CBAM) and DENSE were introduced and the network was trained under Ranger optimizer. The performance of different backbone networks for PWS lesion extraction were also compared.

RESULTS

IDeepLabV3+ (Xception) showed the best results in PWS lesion extraction and area quantification. Its mean Intersection over Union (MIou) was 0.9797, Mean Pixel Accuracy (MPA) 0.9908, Accuracy 0.9989, Recall 0.9886 and F1-score 0.9897, respectively. In PWS area quantization, the mean value of the area error rate of this scheme was 2.61 ± 2.33.

CONCLUSIONS

The new 3D method developed in this study was able to achieve accurate quantification of PWS lesion area and has potentials for clinical applications.

Objective evaluation of immediate reading skin prick test applying image planimetric and reaction thermometry analyses

The skin prick test is used to diagnose patients' sensitization to antigens through a mediated IgE response. It is a practical and quick exam, but its diagnosis depends on instruments for measuring the allergic response and observer's interpretation. The conventional method for inferring about the allergic reaction is performed from the dimensions of the wheals, which are measured using a ruler or a caliper. To make this diagnosis less dependent on human interpretation, the present study proposes two alternative methods to infer about the allergic reaction: computational determination of the wheal area and a study of the temperature variation of the patient's skin in the puncture region. For this purpose, prick test using histamine was performed on 20 patients randomly selected. The areas were determined by the conventional method using the dimensions of the wheals measured with a digital caliper 30 min after the puncture. The wheal areas were also determined by a Python algorithm using photographs of the puncture region obtained with a smartphone. A variable named circularity deviation was also determined for each analyzed wheal. The temperature variation was monitored using an infrared temperature sensor, which collected temperature data for 30 min. All results were statistically compared or correlated. The results showed that the computational method to infer the wheal areas did not differ significantly from the areas determined by the conventional method (p-value = 0.07585). Temperature monitoring revealed that there was a consistent temperature increase in the first minutes after the puncture, followed by stabilization, so that the data could be adjusted by a logistic equation (R² = 0.96). This adjustment showed that the optimal time to measure the temperature is 800 s after the puncture, when the temperature stabilization occurs. The results have also shown that this temperature stabilization has a significant positive correlation with wheal area (p-value = 0.0015). Thus, we concluded that the proposed computational method is more accurate to infer the wheal area when compared to the traditional method, and that the temperature may be used as an alternative parameter to infer about the allergic reaction.