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IKTPLUSS-IKT og digital innovasjon

Artificial Intelligence guided Point-Of-Care UltraSound in remote areas on Abdominal Aortic Aneurysm

Alternative title: Veiledet pasientnær ultralyd i rurale områder på abdominale aorta aneurismer ved hjelp av kunstig intelligens

Awarded: NOK 12.0 mill.

One important drawback of today’s clinical care is the transport of patients, often over long distances to receive clinical care at specialized units. Medical imaging is a cornerstone of modern medical diagnostics but is often limited in remote areas. Today, newly developed handheld ultrasound (US) devices that can be put in use at remote areas clinical care exists, but a lack of familiarity with US and its non-intuitive nature due to noise and artefacts limit today use. In AI POCUS AAA we will increase the use of US diagnostics in remote areas by using artificial intelligence (AI) to guide US imaging. Thereby improving remote health care services and its environmental and economic sustainability. Guided US will allow new and occasional users to confidently use US where the patient is at the general practitioner’s office, the nursing home, the site of the accident or the ambulance. As a test case we will develop solutions for detecting bulges in the aorta (abdominal aortic aneurism (AAA)). AAA is a gradual dilation of the abdominal aorta, and if left untreated, it may rupture with a high risk of fatal consequences. With AI based US guidance, general practitioners in remote areas can supplement regular AAA management at centralized units, including acute detection of symptomatic aneurysms, screening for asymptomatic AAAs in defined risk-groups, monitoring growth of identified AAA until treatment and post-operative follow-up. The project will 1) Create knowledge on users' needs and possibilities of point-of-care US in remote areas primary care; 2) Create datasets for machine learning purposes; 3) Develop intelligent US image-interpretation guidance by machine learning-based segmentation of key anatomical structures; 4) Develop user-interface and real-time functionality and 5) Test the prototypes at the three participating municipalities to collect real-life experiences creating an understanding of actual changes in practices as they are uncovered in clinical use.

One important drawback of today’s clinical care is the transport of patients, often over long distances to receive clinical care at specialized units. Medical imaging is a cornerstone of modern medical diagnostics but is often limited in remote areas. Today, newly developed handheld ultrasound devices exist that can be put in use at remote areas clinical care. But a lack of familiarity with ultrasound and its non-intuitive nature due to noise and artefacts limits today use. We suggest to drastically improve ultrasound user-friendliness using artificial intelligence to guide ultrasound imaging with the objective that new and occasional users confidently can use ultrasound where the patient is at the general practitioner’s office, with the midwife, the nursing home, the site of the accident or the ambulance. As a test case we will develop solutions for abdominal aortic aneurism. As a multidisciplinary and user-centred project we envisage to address the following R&D challenges: - Create knowledge on users' needs and possibilities to point-of-care ultrasound in remote areas primary care and how artificial intelligence can increase the use of point-of-care ultrasound there - Create annotated datasets for machine learning purposes for the abdominal aortic aneurism. - Develop intelligent ultrasound image-interpretation guidance and computation of clinical measures by machine learning based segmentation of key anatomical structures. - Develop machine learning based real-time image quality estimation and probe guidance. - Develop user-interface and real-time functionality of the above mentioned. - Develop training tutorials based on AI automated image segmentation and annotated datasets. - Test the prototypes at the three participating municipalities and collect real-life experiences creating an understanding of actual changes in practices as they uncover in clinical use.

Funding scheme:

IKTPLUSS-IKT og digital innovasjon

Thematic Areas and Topics