Multimodal deep learning for diagnosing sub-aneurysmal aortic dilatation

dc.contributor.authorFINATI, SARA
dc.contributor.authorLJUNGGREN, ELIN
dc.contributor.departmentChalmers tekniska högskola / Institutionen för data och informationstekniksv
dc.contributor.examinerTorkar, Richard
dc.contributor.supervisorAguirre Nilsson, Charlotta
dc.contributor.supervisorVelic, Medina
dc.date.accessioned2019-07-11T07:30:33Z
dc.date.available2019-07-11T07:30:33Z
dc.date.issued2019sv
dc.date.submitted2019
dc.description.abstractAbdominal Aortic Aneurysm (AAA) is a localized enlargement of the abdominal aorta that can progress to a rupture, which will cause an internal bleeding that is fatal in the majority of the cases. To save more lives, a nationwide screening program invites all men at 65 to measure their largest aortic diameter during an ultrasound examination. The diagnosis is based solely on this diameter and if it is below 30 mm the patient is declared healthy and discarded from any follow-up monitoring. However, recent studies have shown that patients with a diameter within 25–29 mm, a sub-aneurysm, run an elevated risk of developing a full-size aneurysm and hence might need further surveillance. This thesis is a collaboration between the product development company QRTECH AB and Västra Götalandsregionen (VGR). It proposes a novel solution for predicting which sub-aneurysms that might grow into a full-size aneurysm with the help of an ultrasound image complemented by patient data including aortic diameter, number of years of smoking and snus, blood pressure and medications. The solution consists of a multimodal deep learning algorithm that classifies the sub-aneurysms as either healthy or sick and thereby suggests patients that should be kept under surveillance. Due to lack of any follow-up data for the men with sub-aneurysms a comparison with meta-studies, examining how many sub-aneurysms that progressed into a fullsize aneurysm, was carried out. The results from those studies did not agree with the results obtained from classifying the sub-group in this project. A feasible explanation is the limited data set which most likely affected the learning. However, the evaluation of the model’s performance was still promising and indicates the potential of using neural networks for diagnosing AAA.sv
dc.identifier.coursecodeDATX05sv
dc.identifier.urihttps://hdl.handle.net/20.500.12380/300040
dc.language.isoengsv
dc.setspec.uppsokTechnology
dc.subjectmultimodal deep learningsv
dc.subjectabdominal aortic aneurysm (AAA)sv
dc.subjectsubaneurysmal aortic dilatationsv
dc.subjectVGG19sv
dc.subjectKerassv
dc.subjectheatmapssv
dc.subjectpermutation importancesv
dc.titleMultimodal deep learning for diagnosing sub-aneurysmal aortic dilatationsv
dc.type.degreeExamensarbete för masterexamensv
dc.type.uppsokH
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