Project description DEENESFRITPL Machine-learning algorithm for early prediction of postoperative infections Around 25 % of surgical patients in Europe experience some type of postoperative complication in the form of an infection. Dutch company Healthplus.ai is developing an advanced machine-learning algorithm to predict postoperative infections five days before the medical team diagnosis, achieving an accuracy of 80 %. The EU-funded PERISCOPE project aims to increase the data set and achieve > 90 % accuracy at five days before the onset of potential infection symptoms in the patient. The project is also focussing on a sustainable business model and plan, partnerships as well as legal and regulatory issues. Show the project objective Hide the project objective Objective Some 50M people need to undergo inpatient surgery per year in Europe Yet, despite intensive research efforts, around 25% of surgical patients will experience a complicated recovery with some type of infection on their path: pneumonia, urinary tract infections, wound infections, abdominal infections and bacteremia. Of course, the risk varies depending on the patient and the procedure, but despite efforts like biomarkers, risk scores or devices for early warning/detection, and preventive methods like ‘Enhanced Recovery After Surgery’ (ERAS), the overall risk still leaves almost one in four patients to get an infection within 30-days after surgery. If we look beyond the personal suffering of patients and relatives, the cost of an infectious complication is estimated at around €10,000 per patient. Hence, total cost in only the Netherlands alone can be calculated at up to €3.5B per year, as approximately 350,000 Dutch patients go through an infection after surgery yearly. Total EU costs are even more shocking at an estimated €125B per year. Healthplus.ai R&D BV is currently developing an advanced machine-learning (ML) algorithm (TRL level 4) to predict post-operative infections 5 days prior to the average medical team diagnosis, currently already achieving an accuracy of 80%. Through increasing the dataset and taking on more types of data, the ultimate goal is to ultimately achieve >90% accuracy at 5 days before the infectious symptoms on average are actually detected within the patient. In this proposal, we seek how to go beyond the proven technical feasibility and assess channels to deliver the tool in a safe, affordable and scalable way through third-party vendors with a sustainable business model and plan with the right partnerships and intensify these relationships. Also, potential legal and regulatory issues with EU expansion will be identified, investigated and suitable measures outlined. Fields of science medical and health sciencesclinical medicinepneumologymedical and health sciencesclinical medicinesurgerymedical and health sciencesclinical medicineurologymedical and health scienceshealth sciencesinfectious diseasessocial scienceseconomics and businesseconomicssustainable economy Programme(s) H2020-EU.2.3. - INDUSTRIAL LEADERSHIP - Innovation In SMEs Main Programme H2020-EU.3. - PRIORITY 'Societal challenges H2020-EU.2.1. - INDUSTRIAL LEADERSHIP - Leadership in enabling and industrial technologies Topic(s) EIC-SMEInst-2018-2020 - SME instrument Call for proposal H2020-EIC-SMEInst-2018-2020 See other projects for this call Sub call H2020-SMEInst-2018-2020-1 Funding Scheme SME-1 - SME instrument phase 1 Coordinator HEALTHPLUS.AI-IP BV Net EU contribution € 50 000,00 Address Singel 542 1017 AZ Amsterdam Netherlands See on map Region West-Nederland Noord-Holland Groot-Amsterdam Activity type Private for-profit entities (excluding Higher or Secondary Education Establishments) Links Contact the organisation Opens in new window Participation in EU R&I programmes Opens in new window HORIZON collaboration network Opens in new window Other funding € 21 429,00
