POLITECNICO DI BARI – Department of Electrical and Information Engineering

PoliBA – Dept. of Electrical and Information Engineering — RES4NET
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Politecnico di Bari — Dept. of Electrical and Information Engineering

Measurement Systems
for Healthcare

Electromagnetic tracking for surgical navigation and deep learning–based intravenous infusion monitoring

Health
AI-assisted surgical procedure — Politecnico di Bari measurement systems for healthcare

Research overview

Application field Measurement systems for health
Technology Electromagnetic tracking system, deep learning for computer vision
Activity Surgical navigation for diagnosis and interventions, Intravenous Drip Infusion Monitoring

Keywords

Healthcare EM Tracking System Image-Guided Surgery Reconstruction Algorithm Surgical Navigation Intravenous Infusion Sensors for Therapeutic Treatments Deep Learning

Operation of the proposed solution

Two basic tasks in the perioperative period are surgery and patient monitoring and care. The outcome of diagnosis and surgical interventions can be improved by means of electromagnetic tracking systems (EMTSs), widely used in surgical navigation. They employ very small EM sensors which measure the magnetic field produced by a field generator, thus accurately estimating the pose of the instrument in the operative scenario.

In the pre- and postoperative phase, monitoring the flow rate of the fluid being administered to patients is critical for their safety. The proposed system uses a camera to film the intravenous (IV) drip infusion kit and a deep learning-based algorithm to detect and count drops. The usage of a camera as a sensing element is safe in medical environments and can be easily integrated into current health facilities.

Open challenges

  • 1 Increase the tracking distance of EMTSs beyond 50 cm of current commercial systems
  • 2 Develop new camera-based smart monitoring devices for medical applications

Selected bibliography

1 Attivissimo F., Nisio A.D., Lanzolla A.M.L., Ragolia M.A. “Analysis of Position Estimation Techniques in a Surgical EM Tracking System.” IEEE Sensors Journal, vol. 21, no. 13, pp. 14389–14396 2021
doi.org/10.1109/JSEN.2020.3042647
2 Giaquinto N., Scarpetta M., Spadavecchia M., Andria G. “Deep Learning-Based Computer Vision for Real-Time Intravenous Drip Infusion Monitoring.” IEEE Sensors Journal, vol. 21, no. 13, pp. 14148–14154 2021
doi.org/10.1109/JSEN.2020.3039009

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