Session: 02-09-01: Design for Healthcare Products and Processes

Paper Number: 144549

144549 - Ai-Driven Design and Fabrication of Customized Prosthetic Limbs Using Additive Manufacturing and Patient-Specific Optimization 

The field of prosthetics has witnessed significant advancements in recent years, driven by the integration of cutting-edge technologies such as additive manufacturing and artificial intelligence (AI). However, the traditional approach to prosthetic limb design and fabrication often results in generic solutions that fail to adequately address the unique needs and anatomical characteristics of individual users. This research aims to bridge this gap by developing an AI-driven framework for the design and fabrication of customized prosthetic limbs using additive manufacturing techniques, coupled with patient-specific optimization algorithms.

The proposed methodology leverages the power of AI and machine learning to revolutionize the design process, ensuring that each prosthetic limb is tailored to the individual's unique requirements. By incorporating advanced algorithms for personalized design optimization, the framework takes into account factors such as anatomical compatibility, biomechanics, aesthetic appeal, and user comfort. This patient-centric approach not only enhances the fit and functionality of the prosthetic limb but also promotes greater acceptance and seamless integration into the user's daily life.

The research methodology involves a multidisciplinary approach, combining expertise from fields such as biomedical engineering, additive manufacturing, computer-aided design, and AI/machine learning. The initial phase focuses on gathering comprehensive data on the user's anatomical characteristics, mobility requirements, and personal preferences through advanced imaging techniques and user input. This data serves as the foundation for the AI-driven design optimization algorithms, which iteratively generate and evaluate potential prosthetic limb designs based on predefined criteria and constraints.

Furthermore, the integration of machine learning techniques in the fabrication process enables real-time monitoring and adaptive control of the additive manufacturing systems. This innovative approach ensures consistent quality, optimizes resource utilization, and facilitates the incorporation of dynamic adjustments based on user feedback or evolving requirements.

Preliminary results from the AI-driven design optimization and additive manufacturing processes have demonstrated significant improvements in terms of fit, comfort, and functionality when compared to traditional prosthetic limb designs. User feedback has been overwhelmingly positive, with reports of enhanced mobility, reduced discomfort, and improved overall quality of life. The research team is currently conducting extensive testing and validation to further refine the framework and prepare for clinical trials and eventual commercialization.

This groundbreaking research represents a significant stride towards personalized healthcare solutions, leveraging the synergies between AI, additive manufacturing, and biomedical engineering. By addressing the limitations of traditional prosthetic limb design and fabrication, this work has the potential to revolutionize the field, offering individuals with limb loss a more personalized, functional, and empowering solution to regain their independence and quality of life.

Presenting Author: Moosa Salim Al Kharusi Sultan Qaboos University

Presenting Author Biography: Dr. Moosa S.M. Al-Kharusi is an Assistant Professor in the Mechanical and Industrial Engineering Department at the College of Engineering, Sultan Qaboos University, Oman. He received his Ph.D. in Mechanical Engineering from Sultan Qaboos University in 2017, with his dissertation focused on the computational modeling of mechanical properties of carbon nanotube-based nanocomposites.

Dr. Al-Kharusi's research interests encompass computational mechanics, carbon nanotubes, nanocomposites, elastomers, swell packers, and renewable energy. He has published several articles in reputed journals, including the Arabian Journal for Science and Engineering, Polymers, Journal of Petroleum Exploration and Production Technology, and ASME Journal of Energy Resources Technology.

Prior to his current position, Dr. Al-Kharusi worked as a faculty member at the Global College of Engineering and Technology (GCET), in partnership with the University of the West of England (UWE), Bristol, UK, from December 2016 to 2020. He has also participated in various continuing education programs, including a Post-Graduate Certificate in Academic Practice (PCAP) from UWE, and specialized training programs in EBSD Applications, AZtechEnergy, and INCA Wave Applications from Oxford Instruments, UK.

Dr. Al-Kharusi's expertise in computational mechanics, nanocomposites, and renewable energy, combined with his academic and industry experience, positions him as a valuable contributor to the field of mechanical engineering and additive manufacturing.

Authors:

Moosa Salim Al Kharusi Sultan Qaboos University
Matasam Al Maawali SQU
Mohammed Suleiman Al Owiemri SQU
Said Al Mushaifri SQU
Sulaiman Al Maimani SQU