Anales de la RANM

266 A N A L E S R A N M R E V I S T A F U N D A D A E N 1 8 7 9 DEEP LEARNING GENITAL LESIONS IMAGE CLASSIFICATION González-Alday R, et al. An RANM. 2022;139(03): 266 - 273 DE E P L E ARN I NG F OR C L I N I C A L IMAG E C L A S S I F I C AT I ON O F G E N I TA L L E S I ON S C AU S E D B Y S E XUA L LY T RANSMI T T ED D I S E A S E S APREND I ZA J E PROFUNDO PARA LA CLAS I F I C AC I ÓN DE IMÁGENES CL ÍNI C AS DE LES I ONES GENI TALES C AUSADAS POR ENFERMEDADES DE TRANSMI S I ÓN SEXUAL Raquel González-Alday 1 ; Francois Peinado 2 ; Daniel Carrillo 1 ; Víctor Maojo 1 1. Biomedical Informatics Group, Dpt of Artificial Intelligence, Universidad Politécnica de Madrid 2. Jefe de Urología. Hospital Ruber Juan Bravo 39, Madrid Abstract Sexually transmitted diseases (STDs) are one of the world's major health emergen- cies. Given its incidence and prevalence, particularly in developing countries, it is necessary to find new methods for early diagnosis and treatment. However, this can be complicated in geographical areas where medical care is limited. In this article, we present the basis of a deep learning-based system for image classification of genital lesions caused by these diseases, built using a convolutional neural network model and methods such as transfer learning and data augmentation. In addition, an explaina- bility method (GradCam) is employed to enhance the interpretability of the obtained results. Finally, we developed a web framework to facilitate additional data collec- tion and annotation. This work aims to be a starting point, a “proof of concept” to test various different approaches, for the development of more robust and trustworthy Artificial Intelligence approaches for medical care in STDs, which could substantially improve medical assistance in the near future, particularly in developing regions. Resumen Las enfermedades de transmisión sexual (ETS) son una de las mayores emergen- cias de salud a nivel mundial. Debido a su incidencia y prevalencia, particular- mente en países en desarrollo, es necesario encontrar nuevos métodos para diagnós- tico y tratamiento precoz. Sin embargo, esto puede ser complicado en áreas geográ- ficas en las que la asistencia médica es limitada. En este artículo, presentamos una prueba de concepto de un sistema basado en aprendizaje profundo para la clasifica- ción de imágenes de lesiones genitales causadas por estas enfermedades, construido utilizando un modelo de red neuronal convolucional y métodos como transfer learning y data augmentation . Además, incorpora un método de explicabilidad (GradCam) para mejorar la interpretabilidad de los resultados obtenidos, y se ha desarrollado un servicio web para facilitar la recogida de datos adicionales y su anotación. Este trabajo pretende ser un punto de partida y una prueba de concepto para valorar diferentes enfoques en el desarrollo de modelos de Inteligencia Artificial más robustos y fiables para la asistencia médica en ETS, que podría mejorar sustancialmente la asistencia médica en un futuro próximo, particularmente en regiones en desarrollo. Keywords: Artificial Intelligence; Sexually Transmitted Diseases; Clinical Image Classification. Palabras clave: Inteligencia Artificial; Enfermedades de Transmisión Sexual; Clasificación de Imágenes Clínicas. Autor para la correspondencia Víctor Maojo Universidad Politécnica de Madrid Escuela Técnica Superior de Ingenieros Informáticos. Campus de Montegancedo UPM Tlf. :+34 910 672 898 | E-Mail: vmaojo@gmail.com DOI: 10.32440/ar.2022.139.03. rev07 Enviado: 20.07.22 | Revisado: 28.07.22 | Aceptado: 16.08.22 R E V I S I Ó N 1. INTRODUCTION 1.1. Sexually transmitted diseases: prevalence and lesions Sexually transmitted diseases (STDs), also refered as sexually transmitted infections (STIs), are one of the biggest health issues worlwide, with nearly 1 million estimated people becoming infected every day, according to the WHO (1). This is a major problem in many countries. At the Universidad Politécnica de Madrid, we addressed this problem in a European Commission-funded project, called Africa Build (2010-2013), which we coordinated with partners such as WHO, various European institutions and colleagues from Ghana, Mali, Egypt and Cameroon. We carried out various initiatives such as teaching how to use medical informatics methods and tools, as well as distance learning in various topics, which included STDs.