5G in Healthcare

2022 COE Engineering Design Project (RP06)

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Faculty Lab Coordinator

Robnier Reyes Perez

Topic Category

Distributed Systems and Networking

Preamble

The next generation of wireless communication technology provides a significant improvement in speed and throughput which can be used to unlock new applications in the healthcare industry. The new healthcare ecosystem that can be enabled with 5G networks will allow for improved telehealth, remote surgery, and the transferring of large medical files. However, in order to realize the full potential of 5G networks in healthcare, security and data privacy must be addressed. A robust cybersecurity strategy must use a “zero-trust” approach from end-to-end for all devices on the network, strong encryption standards for point-to-point traffic, and machine learning methods to identify and mitigate cyber risks.

Objective

The goal of the project is to create a 5G network for the transfer of large medical files (i.e. DICOM file). The primary objective is to implement a secure network that will protect the privacy of the patients’ medical files.

Partial Specifications

- Simulate scenario where a medical scanner is outside the local network of a Picture Archiving and Communication System (PACS) server and needs to transmit an image in Digital Imaging and Communications in Medicine (DICOM) format using the 5G network.
- The team must implement a machine learning algorithm to identify and mitigate risks.

Suggested Approach

- The team needs to find an open-source repository of DICOM files and implement a cloud-based PACS server and client.
- Evaluate the ability of the 5G network to transmit the required data and quality of service (QoS) specifications.

Group Responsibilities

All team members are responsible for performing a literature review of the current state of 5G communication and its applications in healthcare for the transmission of large files (i.e. medical images). This includes but it is not limited to finding peer-reviewed literature, patented solutions, and commercially available solutions. The team is responsible for creating a block level design of the complete system and assigning specific tasks to each of the blocks. Every member is responsible for documenting the design of their subsystem, explaining design choices, and description of the performed tasks.

Student A Responsibilities

This team member will be responsible for finding the open-source medical image repository and implement a cloud-based PACS. Additional responsibilities as assigned by the FLC.

Student B Responsibilities

This student will be responsible for determining the appropriate low cost hardware and implementing the 5G network (i.e. smartphone, Raspberry Pi 5G Hat). Additional responsibilities as assigned by the FLC.

Student C Responsibilities

This team member will be responsible for the machine learning-based cybersecurity protocol. Additional responsibilities as assigned by the FLC.

Student D Responsibilities

This team member will be responsible for the machine learning-based cybersecurity protocol. This student will work in close collaboration with Student C to achieve this goal. Additional responsibilities as assigned by the FLC.

Course Co-requisites

COE318, COE428, COE528

To ALL EDP Students

Due to COVID-19 pandemic, in the event University is not open for in-class/in-lab activities during the Winter term, your EDP topic specifications, requirements, implementations, and assessment methods will be adjusted by your FLCs at their discretion.