Objective hand eczema severity assessment with automated lesion anatomical stratification.

Hand eczema (HE) is one of the most frequent dermatoses, known to be both relapsing and remitting. Regular and precise evaluation of the disease severity is key for treatment management. Current scoring systems such as the hand eczema severity index (HECSI) suffer from intra- and inter-observer variance. We propose an automated system based on deep learning models (DLM) to quantify HE lesions' surface and determine their anatomical stratification. In this retrospective study, a team of 11 experienced dermatologists annotated eczema lesions in 312 HE pictures, and a medical student created anatomical maps of 215 hands pictures based on 37 anatomical subregions. Each data set was split into training and test pictures and used to train and evaluate two DLMs, one for anatomical mapping, the other for HE lesions segmentation. On the respective test sets, the anatomy DLM achieved average precision and sensitivity of 83% (95% confidence interval [CI] 80-85) and 85% (CI 82-88), while the HE DLM achieved precision and sensitivity of 75% (CI 64-82) and 69% (CI 55-81). The intraclass correlation of the predicted HE surface with dermatologists' estimated surface was 0.94 (CI 0.90-0.96). The proposed method automatically predicts the anatomical stratification of HE lesions' surface and can serve as support to evaluate hand eczema severity, improving reliability, precision and efficiency over manual assessment. Furthermore, the anatomical DLM is not limited to HE and can be applied to any other skin disease occurring on the hands such as lentigo or psoriasis.

Detecting racism and xenophobia using deep learning models on Twitter data: CNN, LSTM and BERT.

With the growth that social networks have experienced in recent years, it is entirely impossible to moderate content manually. Thanks to the different existing techniques in natural language processing, it is possible to generate predictive models that automatically classify texts into different categories. However, a weakness has been detected concerning the language used to train such models. This work aimed to develop a predictive model based on BERT, capable of detecting racist and xenophobic messages in tweets written in Spanish. A comparison was made with different Deep Learning models. A total of five predictive models were developed, two based on BERT and three using other deep learning techniques, CNN, LSTM and a model combining CNN + LSTM techniques. After exhaustively analyzing the results obtained by the different models, it was found that the one that got the best metrics was BETO, a BERT-based model trained only with texts written in Spanish. The results of our study show that the BETO model achieves a precision of 85.22% compared to the 82.00% precision of the mBERT model. The rest of the models obtained between 79.34% and 80.48% precision. On this basis, it has been possible to justify the vital importance of developing native transfer learning models for solving Natural Language Processing (NLP) problems in Spanish. Our main contribution is the achievement of promising results in the field of racism and hate speech in Spanish by applying different deep learning techniques.