ARTIFICIAL INTELLIGENCE ENHANCEMENT OF SURGICAL TECHNOLOGY FOR THE REDUCTION OF HUMAN BEHAVIOUR-RELATED SURGICAL ACCIDENTS

Información del proyecto

BESAFE

Identificador del acuerdo de subvención: 847378

DOI

10.3030/847378

Proyecto cerrado

Fecha de la firma de la CE 16 Abril 2019

Fecha de inicio 1 Mayo 2019

Fecha de finalización 30 Abril 2020

Financiado con arreglo a

• EXCELLENT SCIENCE - Future and Emerging Technologies (FET)

Coste total

€ 100 000,00

Aportación de la UE

€ 100 000,00

Coordinado por

AFFERENT TECHNOLOGIES SL
Spain

Periodic Reporting for period 1 - BESAFE (ARTIFICIAL INTELLIGENCE ENHANCEMENT OF SURGICAL TECHNOLOGY FOR THE REDUCTION OF HUMAN BEHAVIOUR-RELATED SURGICAL ACCIDENTS)

Período documentado: 2019-05-01 hasta 2020-04-30

Resumen del contexto y de los objetivos generales del proyecto

BESAFE explores the use of Artificial Intelligence to minimise the risk inherent to surgical procedures.

The underlying assumption (backed by data) is that the human factor is a major contributor to surgical risk, impacting on the probability of adequate resolution of intra-operative events. In particular the level of training of staff and stress are associated with surgical outcome.

BESAFE builds on FET-Open project IBSEN to use machine learning to enhance existing instra-operative instruments and devices, in order to detect inadequate training or stree-prone behaviour before a surgical accident occurs.

The project included a demonstration task to assess the practicalities of the concept and the development of a business plan including financial forecasts and product development strategy.

A key conclusion of the project is that quantitative automated analysis of the interaction of users with computer or instrument interfaces can indeed infer the level of training and cognitive pressure.
An additional conclusion is that this core concept could lead to a financially viable startup with substantial return for investors, health impact for patients and a time to market of 4 years.

Trabajo realizado desde el comienzo del proyecto hasta el final del período abarcado por el informe y los principales resultados hasta la fecha

Within BESAFE we performed two core activities.

First we demonstrated with over 100 users that machine learning (real-time or offline automated data analysis) can extract the level of training and user stress from the pattern of use of a computer interface.
This result in itself is extremely interesting in that it can be used in multiple fields. Specifically in surgery, the approach developed in IBSEN/BESAFE can anticipate the outcome of time-constrained tasks, and potential decrease surgical risk.

A second key activity in BESAFE was the development of a business plan.

Avances que van más allá del estado de la técnica e impacto potencial esperado (incluida la repercusión socioeconómica y las implicaciones sociales más amplias del proyecto hasta la fecha)

BESAFE is focused on exploring the market opportunity and on demonstrating technical viability, rather than performing new R&D.

However, modifying how clinicians interact with instrumentation in the oeprating room can have a tremendous socio-economic impact and more widely impact surgical outcomes.

The project's focus has been on planning towards achieving these outcomes at the technical and financial levels.

Heatmap showing correlations amongst variables, as used for machine learning in BESAFE

logo of the project