App for AI-assisted detection of monkeypox skin lesions

The paper provides the scientific basis for the project's PoxApp (https://poxapp.charite.de and https://poxapp.stanford.edu), an AI-based app that can determine a risk score for monkeypox infection and thus help contain the virus. The DAKI-FWS consortium project strives to use AI technologies to link and evaluate data and, on this basis, develop an early warning system for future crisis situations - for example, infectious diseases.

The German Federal Ministry for Economic Affairs and Climate Action (BMWK) is funding DAKI-FWS as part of the "AI Innovation Competition” with approximately 12 million euros. The Fraunhofer Heinrich-Hertz-Institut (HHI) and its "Artificial Intelligence" department are acting as consortium leaders in the project. DAKI-FWS launched at the beginning of December 2021 and is scheduled to run for three years. Fraunhofer HHI contributes to this project its research expertise in the field of multimodal machine learning. As a leading position of the ITU/WMO/UNEP focus group "AI for Natural Disaster Management", it also offers its experience in the field of AI-based prediction and classification in the context of environmental and natural disasters.

Monkeypox virus (also known as mpox virus or MPXV) has triggered an ongoing outbreak with more than 86,000 confirmed cases in more than 100 countries. It has been declared an international health emergency by the World Health Organization (WHO). While the number of new infections has currently declined, the outbreak is not considered to be over. Although transmission of this zoonotic infection has historically been mostly animal-to-human, the current outbreak marks the first time that sustained human-to-human transmission has occurred. Modeling by the European Center for Disease Prevention and Control has shown that MPXV outbreaks result primarily from undetected infections and delayed isolation. That's why it's important to detect cases as soon as possible. The DAKI-FWS team is addressing this by developing an AI-based case definition for detecting MPXV infections.

The majority of MPXV infections are associated with skin lesions that occur at various stages during the course of the disease. In the DAKI-FWS study, the researchers developed a novel neural network (Deep Convolutional Neural Networks, CNN) with more than 90% accuracy in detecting MPXV skin lesions. Using the PoxApp, individuals who are concerned about MPXV infection can upload images of skin lesions and receive an initial assessment. The goal is for patients with MPXV infection to receive appropriate medical treatment more quickly and isolate themselves early.

MEDICA-tradefair.com; Source: Fraunhofer-Institut für Nachrichtentechnik, Heinrich-Hertz-Institut, HHI