Key result
Autonomous robotic ultrasound with deep learning proves feasible for screening the abdominal aorta.
Why the study?
Abdominal aortic aneurysms are often asymptomatic but critical if complicated by rupture, embolization, or occlusion, and standard ultrasound screening typically relies on expert sonographers.
An autonomous robotic system combining an impedance-controlled collaborative robot and deep learning segmentation demonstrates feasibility for abdominal aorta ultrasound screening.
Supports preclinical advancement of robotic ultrasound; leaves open clinical utility for abdominal aortic screening.
Abdominal aortic aneurysm consists in the enlargement of the abdominal aorta involving a local diameter greater than 3 cm. Despite the fact that most aneurysms are asymptomatic, such a pathology becomes critical in case of complications as embolization, occlusion and rupture. The diagnosis is commonly achieved by means of an ultrasound examination carried out by expert sonographers. We designed a robotic system that can autonomously perform the ultrasound screening of the abdominal aorta, measuring its diameter and therefore providing the early diagnosis of the aneurysm. We use an impedance-controlled collaborative robot to move the ultrasound probe on the patient’s abdomen while a deep learning neural network segments the aorta in the ultrasound image and estimates the diameter. Our motion planning algorithm makes use of an artificial potential field that guides the robot to move the probe toward the generation of a good aorta view. Finally, we conducted several experiments to validate the feasibility of the proposed approach.
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Farsoni et al. (2025) studied Abdominal aortic aneurysm. Autonomous robotic system with deep learning segmentation was evaluated on Feasibility of the proposed approach. An autonomous robotic system using deep learning segmentation was successfully designed and experimentally validated for the feasibility of ultrasound screening of the abdominal aorta.
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