Hybrid Conferencee

International Conference on Biomedical Device Manufacturing and Innovation (ICBDMI - 26)

7th - 8th October 2026 | Jelgava, Latvia

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Call for Papers Extended:
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Certificate of Presentation:
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Abstract Submissions Open:
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Networking with Global Experts:
"Engage with researchers and professionals from around the world at the Jelgava conference. Build collaborations and gain insights from leading experts."
Keynote Speaker Sessions:
"Don’t miss our Keynote Sessions in Jelgava, featuring global leaders and innovators sharing their knowledge."
Best Paper & Best Paper Presentation Award:
"Submit your paper and stand a chance to win the Best Paper Presentation Award. The winner will be recognized at the conference in Jelgava."
SDG-Inspired Conference Focus:
"Our conference will highlight research that addresses global sustainability, inclusive education, and solutions for environmental challenges."

Conference Session Tracks

SDG Wheel

Aligned with

UN Sustainable Development Goals

This conference contributes to global sustainability by aligning its research discussions and academic sessions with key United Nations Sustainable Development Goals. It fosters knowledge exchange, innovation, and collaborative engagement.

SDG 3
SDG 3 Good Health and Well-being
SDG 4
SDG 4 Quality Education
SDG 9
SDG 9 Industry, Innovation and Infrastructure
Track 01

Innovations in Biomedical Device Manufacturing

This track focuses on the latest advancements in the manufacturing processes of biomedical devices. It aims to explore innovative techniques that enhance production efficiency and device functionality.

Track 02

Predictive Modeling in Biomedical Engineering

This session will delve into the application of predictive modeling techniques within biomedical engineering. Participants will discuss methodologies that improve device reliability and patient outcomes.

Track 03

Machine Learning Applications in Biomedical Devices

This track examines the use of supervised and unsupervised learning algorithms in the development of biomedical devices. Emphasis will be placed on their role in enhancing device performance and decision-making.

Track 04

Deep Learning for Medical Instrumentation

This session will highlight the transformative impact of deep learning on medical instrumentation. Discussions will include case studies and practical applications that demonstrate improved diagnostic capabilities.

Track 05

Anomaly Detection in Biomedical Systems

This track will explore advanced techniques for anomaly detection in biomedical systems. Participants will share insights on identifying and mitigating risks to ensure device safety and efficacy.

Track 06

Feature Extraction Techniques in Biomedical Engineering

This session focuses on innovative feature extraction methods that enhance data analysis in biomedical engineering. The goal is to improve the interpretation of complex biomedical signals.

Track 07

Workflow Automation in Biomedical Device Production

This track addresses the integration of workflow automation in the manufacturing of biomedical devices. Participants will discuss strategies for optimizing production processes and reducing human error.

Track 08

System Monitoring and Evaluation in Biomedical Devices

This session will cover the importance of system monitoring and evaluation in ensuring the reliability of biomedical devices. Best practices for continuous assessment and improvement will be highlighted.

Track 09

Industrial IoT in Biomedical Device Innovation

This track explores the role of the Industrial Internet of Things (IoT) in the innovation of biomedical devices. Discussions will focus on connectivity, data sharing, and real-time monitoring.

Track 10

Rapid Prototyping Techniques for Biomedical Devices

This session will showcase rapid prototyping methods that accelerate the development of biomedical devices. Participants will discuss the impact of prototyping on innovation and time-to-market.

Track 11

Digital Twin Technology in Biomedical Engineering

This track examines the application of digital twin technology in the biomedical field. Emphasis will be placed on its potential to optimize device performance and enhance predictive maintenance.