Healthcare technologies for low resource settings (session 2)
Challenges and solutions in delivering effective healthcare in low resource settings
About
Hear from our chosen abstract speakers on the key topics that are important to appropriate healthcare technologies in 2024
This session will focus on oxygen, ventilation and devices and includes the following speakers:
Keynote:
James Roberts, CEO/Founder , mOm Incubators
Adriana Velazquez Berumen, Team Lead Medical Devices and In-Vitro Diagnostics, World Health Organization
Topics
- Enhancing Theta Rhythms in Dementia Rehabilitation Through Virtual Reality: A Machine Learning Approach Inspired by Hippocampal Oscillation Studies
- Enhancing Family Planning Services through Digital Health Education: A Collaborative Initiative between DKT Myanmar and Z-waka
- Cascaded Transformer plus Unet in Medical Image Segmentation with saving of computational resources
- Evaluation of the applicability of ECG signals synthesized from PPG signals by open-source technologies to the real-world scenario of detecting obstructive sleep apnoea events
Delivering effective healthcare in environments with low resources faces many challenges. Some factors which may be detrimental to the provision of adequate healthcare in low resource settings include lack of sufficient financial resources, facilities and infrastructure; insufficient and/or inadequately educated and trained staff; lack of test equipment, manuals and spare parts; inappropriate equipment donations; large number of healthcare equipment out of service; unreliable power and water supplies; political instability and war.
At AHT2024, we shall be highlighting and discussing appropriate technological developments to meet these needs, and we will focus on Innovation, Design and Engineering. Case studies from the field, including those which experienced serious challenges, or even failure, can be instructive in helping future projects.
1
Continuing Professional Development
This event can contribute towards your Continuing Professional Development (CPD) hours as part of the IET's CPD monitoring scheme.
19 Nov 2024
12:30pm - 2:30pm
Organiser
- Healthcare Technologies TN
Speakers
Dr. Khine Pwint Nwe
Founder and CEO - Z-waka
Dr. Khine Pwint Nwe is the Founder and CEO of Z-waka, a digital healthcare platform focused on improving healthcare access in developing countries like Myanmar, Vietnam, and Cambodia. With over 10 years of experience in the medical field, Dr. Khine has a strong research background, having worked extensively with organizations such as PSI, UNFPA, DKT International and Marie Stopes, and contributing to healthcare initiatives for Ministries of Health in Myanmar. She holds a Master’s in Health Informatics from the University of Leeds and has collaborated on several research projects. Dr. Khine’s work has centered around digital health innovations, family planning, and reproductive health rights, making her a leader in both healthcare delivery and research.
Thathsara Nanayakkara BSc.Eng.(Hons), CEng, MIET(UK), AEng, AMIE(SL), MIEEE
Researcher at the Future Medical Healthcare Research Lab - Pukyong National University, South Korea
Thathsara Nanayakkara graduated with a BSc in Engineering (Hons) in Electrical and Electronics Engineering from General Sir John Kotelawala Defence University, Sri Lanka. He is a Chartered Engineer and a member of the IET. Currently, he serves as a researcher at the Future Medical Healthcare Research Lab at Pukyong National University, South Korea, while pursuing a Master's degree in Industry 4.0 Bionics Engineering. His research interests include Robotics and Automation, Sensors and Actuators, Control Systems, Embedded Systems, Industrial Automation, Computer Vision Applications, Electrical Machines and Drives, and Digital Healthcare.
Fabian Degen
B. Sc. Computer Science student - Technical University of Munich
B. Sc. Computer Science student at the Technical University of Munich, research that will be presented was conducted at the University of Oxford as part of my time there as visiting student.
Xin Du
PostDoc - adNet data science team at the Cavendish Laboratory
I have joined the RadNet data science team at the Cavendish Laboratory as a postdoc. I was a Ph.D. student at the University of Southampton, with research interests in information theory, Cascade Learning, and transfer learning with applications to problems in computer vision, biology, and human activity monitoring from wearable sensors. My work is aimed at developing new learning algorithms and architectures, and deeper understanding of them in the context of these applied problems. Currently, I am focusing on auto-segmentation of 3D medical images with deep learning and trying to develop a way to combine the information from both text descriptions and medical image contexts. Outside of research, I fancy baking, travelling, knowing new people, and exploring new activities.
James Roberts
CEO/Founder - mOm Incubators
James Roberts trained as a product designer. In 2014 he was awarded the global Sir James Dyson award for innovation for his project, mOm the inflatable incubator. Since then, he has gone on to be named as a MD-DI MedTech rising star and awarded the Princess Royal Silver Medal which recognises an outstanding and demonstrated personal contribution to UK engineering. He is included on the TFL engineering icons tube map at Queensway station.
James has gone on to raise nearly £10M for the company. The mOm Essential Incubator is now regulated, having received a MDR CE mark. The incubator has now impacted an estimated 4,000 baby's lives in the NHS and 5 other countries.
Reasons to attend
CPD
Real world situations and solutions
Unique opportunity to learn directly from active and experienced professionals in their respective fields
Comprehensive overview of subjects with latest industry trends, developments, and challenges
Q&A to allow you to explore specific, related issues
Programme
Speakers
Keynote:
James Roberts, CEO/Founder , mOm Incubators
Adriana Velazquez Berumen, Team Lead Medical Devices and In-Vitro Diagnostics, World Health Organization
Speakers:
Talk 1: Enhancing Family Planning Services through Digital Health Education: A Collaborative Initiative between DKT Myanmar and Z-waka
Speaker Dr. Khine Pwint Nwe, Founder and CEO z-waka
Myanmar faces significant challenges in reproductive health, exacerbated by ongoing political instability and the deterioration of the public healthcare sector. Limited access to family planning services has contributed to high rates of unintended pregnancies and maternal health issues. The need for effective family planning education and services is critical, particularly in low-resource settings where traditional healthcare delivery methods are strained.
The objective is to evaluate the impact of a digital health education program on the knowledge and practices of healthcare providers (HCPs) in family planning, and to assess the feasibility and effectiveness of such interventions in low-resource settings.
Talk 2: Enhancing Theta Rhythms in Dementia Rehabilitation Through Virtual Reality: A Machine Learning Approach Inspired by Hippocampal Oscillation Studies
Speaker: Thathsara Nanayakkara, Scientific Researcher, Pukyong National University
The increasing prevalence of dementia, particularly among the elderly, poses significant challenges to healthcare systems worldwide, especially in low-resource settings. This study addresses the urgent need for innovative and affordable interventions by exploring the potential of Virtual Reality (VR) combined with machine learning to enhance cognitive rehabilitation in individuals with Mild Cognitive Impairment (MCI) and early-stage dementia.
Authors: Prof. Byeong-il Lee
Talk 3: Evaluation of the applicability of ECG signals synthesized from PPG signals by open-source technologies to the real-world scenario of detecting obstructive sleep apnoea events
Speaker: Fabian Degen, B. Sc. Computer Science student at the Technical University of Munich
Talk 4: Cascaded Transformer plus Unet in Medical Image Segmentation with saving of computational resources
Speaker: Xin Du, PDRA at University of Cambridge
Radiotherapy plays a crucial role in modern medicine but requires considerable time for manually contouring radio-sensitive organs at risk, which can delay treatment processing. With the significant success of deep convolutional neural networks, auto-segmentation in medical image analysis has shown substantial improvements in saving time and reducing inter-operator variability. While convolutional neural networks utilise the locality of convolution operations, they lose global and long-range semantic information. To address this, we propose a cascaded transformer U-net for medical image segmentation that compensates for long-range dependencies and mitigates computational requirements without compromising performance.
Authors: Xin Du and Rajesh Jean, The department of physics at the University of Cambridge.
Q&A and Panel discussion