Intro

💼 Ph.D. in Computer Science from NTNU, specializing in Computerized AI for Cardiac Ultrasound
  • - Developed robust AI system for automatic cardiac function monitoring
  • - Utilized state-of-the-art neural network architectures
🌍 Research stay at CREATIS, Insa Lyon
  • - Generated realistic synthetic ultrasound data for validation
  • - Trained machine learning-based segmentation and motion tracking algorithms
🎓 M.Sc. in Cybernetics and Robotics, NTNU
  • - Expertise in dynamic systems control and monitoring
👏 Internships:
  • - Hy5 Pro (Medtech): Developed mobile platform for MyHand prosthetic
  • - Computas AS: Created emergency preparedness platform using native apps and Google Cloud
  • - Nordic Semiconductor ASA: Developed of a glove to controll drones, with an iOS app, DJI drone support through the DJI API and a microcontroller handling sensor data from the glove
🔬 Collaboration with The Operating Room of the Future (FOR)
  • - Visualized radiological imaging on Microsoft HoloLens using augmented reality
💡 Senior Research Developer at Ntention
  • - Developed advanced glove with sensors for drone control
🌐 Exchange at University of Trento, Italy
  • - Enriched academic journey as an Erasmus Student

Publications

Education

Ph.D., Computer Science, NTNU

Computerized Artificial Intelligence for Automated Monitoring of Left Ventricular Function by Ultrasound. Completed 2024.

M.Sc., Cybernetics and Robotics, NTNU

Control and supervision of complex dynamic systems by computer science, physics, mathematical calculations, and advanced regulation.

Experience

Researcher, Department of Circulation and Medical Imaging, NTNU

Adjunct appointment 20%, temporary position during Ph.D. studies.

Researcher, St. Olavs hospital, NTNU

Adjunct appointment 20%, temporary position during Ph.D. studies.

Senior Research Developer, Ntention

Developed a highly technological glove equipped with sensors to capture the motion of the human hand (capable of controlling drones).

Internships

Summer Intern, Hy5 Pro AS

Developed the mobile platform for a myoelectric hand prosthetic. Using Firebase and Bluetooth technology, the platform connected to the prosthetic, performed diagnostic tests, and configured the settings. The platform also enabled users to train in the use of the prosthetic, in order to improve the performance and usability.

Summer Intern, Computas AS

An emergency preparedness mobile platform was developed in Swift, using Google Cloud Platform as the backend solution. The solution made the handling and mapping of emergency situations easier and safer.

Summer Intern, Nordic Semiconductor

Developed an iOS app in relation to Ntention, making it possible to control drones with the Ntention glove. The app handled Bluetooth and WiFi connections, computing and processing of sensor values, and UI and UX.

Projects

Description of projects developed during my Ph.D. research. For further details,
see Projects section in the menu at top right.

  • AutoMAPSE 2D

    An AI-driven method utilizing 2D transesophageal echocardiography (TEE) for assessing left ventricular (LV) function in critical care patients post-cardiac surgery. We used state-of-the-art machine learning (ML) architectures for the detection and tracking of the mitral annulus in 2D TEE sequences, for the estimation of mitral annular plane systolic excursion (MAPSE).

  • AutoMAPSE 3D

    A fully automatic pipeline for the estimation of MAPSE in 3D TEE, including volume alignment, cardiac view classification, and mitral annulus segmentation in 3D. The comprehensive view provided by 3D TEE, coupled with the efficiency of automation, offered a more accurate and holistic assessment of the LV function. The use of 3D TEE reduced the need for manual intervention, thus streamlining clinical workflows and potentially improving patient outcomes in critical care settings.

  • synTEE

    Generation of synthetic 2D TEE data with ground truth motion fields of myocardial contraction. Method was developed by CREATIS, INSA, Lyon, France and adopted to TEE in collaboration with the research group through a research stay. This simulation pipeline generated synthetic images by mimicking ultrasound wave propagation and reflection through a physical simulator named SIMUS, coupled with post- process calculations. The pipeline also simulated various myocardial contraction patterns, including myocardial infarction in different cardiac segments. This was achieved by locally modifying the scatter map deformations to simulate reduced longitudinal contraction in affected areas, ensuring that global contraction remained consistent and that the endocardial and epicardial borders were unaffected.

  • AutoStrain

    Optical flow and point trajectory estimation in 2D TEE for the approximation of regional LV function in critical care patients by segmental longitudinal strain (SLS). AutoStrain held potential to transform the current practice by providing a more objective and automated approach to strain quantification. Accurate prediction of deformation by estimation of myocardial motion was vital for assessing regional LV function and detecting conditions such as ischemic cardiomyopathy.

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