An early and rapid AI-diagnostic tool based on clinical features for the identification of new potential COVID-cases
During this period of global emergency due to COVID-19 epidemic, the development of technological solutions is crucial to help and improve the efficiency of the overload healthcare system. The proposed project LearnFromCOVID-19 answers to this need by providing a machine learning algorithm that identifies new potential COVID-cases thanks to the smart learning of the clinical status of already established patients.
The aim of the project is to develop a clinical diagnostic tool, based on artificial intelligence, to provide a rapid and early diagnosis of the disease to identify and remotely monitor the state of COVID-19 patients. In this way, the system overcomes the problem of the high number of patients to be managed, the lack of swabs and the urgent need to reduce the spread of infection.
In order to do that, this project consists of three key points :
- Creation of a database of clinical features of suspected cases previously recorded in hospitals;
- Development of a machine learning tool to predict the swab test results;
- Creation of a user-interface for new subject with suspicion of COVID-19.
The database will contain the following clinical features: age, gender, epidemiological factors, comorbidities, clinical assessed symptoms and vital signs. These data will be gather from the medical records of COVID-19 suspected cases who underwent swab test at the hospital, with the utmost respect for privacy.
The data collected will be used to train the machine learning algorithm and to evaluate its performance.
Once found the best classification rule, an user-friendly interface will allow to use the algorithm as follow: the user uploads independently, from his home, the clinical features reported above, in particular the vital signs will be easily recorded using simple tools available in pharmacy (e.g. thermometer, spO2 sensor, blood pressure sensor,...). The user’s datasheet will be downloadable and able to be sent to an health caregiver for the final evaluation. This tool will be widely distributed to the population and hospitals as a web app in order to predict the potential new cases and hence report the subjects who need to undergo the swab test.
In this way, the population under clinical control will strongly increase and the number of swabs will be targeted only to the potential positive cases predicted by the algorithm.
Moreover, this innovative tool will help to stop the spreading of the disease, indeed a rapid and early diagnosis of a potential COVID case will allow him to take the right measures even before undergoing the swab test.
Last but not least, thanks to this system the work of all the medical personnel which fight everyday on the front-line will be lightened.