Ayodele James Oyejide

Advancements in assistive robots have significantly transformed healthcare procedures in recent years. Clinical continuum robots have enhanced minimally invasive surgeries, offering benefits to patients such as reduced blood loss and a short recovery time. However, controlling these devices is difficult due to their limited accuracy in three-dimensional deflections and challenging localization, particularly in confined spaces like human internal organs. Consequently, there has been growing research interest in employing miniaturized soft growing robots, a promising alternative that provides enhanced flexibility and maneuverability. In this work, we extensively investigated issues concerning their designs and interactions with humans in clinical contexts. We took insights from the open challenges of the generic soft growing robots to examine implications for miniaturization, actuation, and biocompatibility. We proposed technological concepts and provided detailed discussions on leveraging existing technologies, such as smart sensors, haptic feedback, and artificial intelligence, to ensure the safe and efficient deployment of the robots. Finally, we offer an array of opinions from a biomedical engineering perspective that contributes to advancing research in this domain for future research to transition from conceptualization to practical clinical application of miniature soft growing robots.

Coronary artery stenosis, characterized by the narrowing of the lumen, significantly affects blood flow and contributes to the progression of cardiovascular diseases. This study investigates the hemodynamics of coronary artery models with varying stenosis configurations, all maintaining an 80% lumen reduction, to determine how differences in morphology influence flow behavior and mechanical stresses. We employed computational fluid dynamics to analyze five idealized geometries with (10% & 70%), (20% & 60%), (30% & 50%), (40% & 40%), and (0% & 80%) stenosis configurations. Through physiological pulsatile flow conditions, we evaluated key hemodynamic pattern including velocity profiles, wall shear stress, and pressure distribution. Our results reveal that despite the same degree of lumen reduction, each stenosis configuration produced distinct flow patterns and hemodynamic profiles. Asymmetric configurations, such as 10% & 70% and 20% & 60%, exhibited pronounced flow disruptions and higher wall shear stress at the stenosis throats, while symmetric configurations, such as 40% & 40%, demonstrated more uniform flow and reduced vortex. Our findings challenge the practice of generalizing results across stenosis configurations without accounting for morphological variations, which is prevalent in many CFD studies using idealized models. This study emphasizes the importance of considering stenosis-specific morphology in CFD analyses and clinical interpretations to enhance the accuracy of diagnostic tools, improve personalized treatment planning, and guide the design of medical devices such as stents.

Background: Aging is a significant risk factor for a majority of chronic diseases and impairments. Increased medical costs brought about by the increasing aging population in the world increases the strain on families and communities. A positive and qualitative perspective on aging is successful aging (SA). Successful aging refers to the state of being free from diseases or impairments that hinder normal functioning, as observed from a biological perspective. This differs from typical aging, which is associated with a gradual decrease in both physical and cognitive capacities as individuals grow older. Methods: In this study, the geriatric data acquired from the Afe Babalola University Multi-System Hospital, Ado-Ekiti was initially prepared, and three fundamental machine learning (ML) techniques such as artificial neural networks, support vector machines, and Naive Bayes were then constructed using the data from a sample of 2000 individuals. The Rowe and Kahn Model was used to determined that the dataset was SA based on factors such as the absence of fewer than or equivalent to two diseases, quality of life, nutrition, and capacity for everyday activities. Results: According to the experimental findings, the predictive network, Artificial Neural Network (ANN) performed better than other models in predicting SA with a 100% accuracy, 100% sensitivity, and 100% precision. Conclusions: The results show that ML techniques are useful in assisting social and health policymakers in their decisions on SA. The presented ANN-based method surpasses the other ML models when it comes to classifying people into SA and non-SA categories.

In the past five decades, Nigeria has witnessed a range of Biomedical Engineering (BME) and technology activities within private and public hospitals, research institutions, and a limited number of universities. These have mainly centred on the procurement, installation, and maintenance of medical equipment and devices. Trained technologists and technicians, equipped with relevant skills and certification, have primarily spearheaded these efforts. Consequently, the country has made a minimal contribution to the global knowledge base in BME research. However, academic programmes leading to degrees and dedicated research in BME have recently emerged within Nigerian universities. This article assesses the current state of BME education in the country, including the milestones achieved, ongoing challenges, and prospects for future development. It draws on a critical analysis of the existing literature on BME practices and education in Nigeria as well as the author’s informed perspective. The findings highlight that BME education in Nigeria is yet to match international standards. To further develop these programmes, it recommends that attention focus on seven key areas that have proven instrumental in the development of similar university programmes in developed nations. Strategies are also proposed to foster collaboration among universities, researchers, the health sector, and government entities that would promote interdisciplinary BME education, ultimately enhancing the healthcare delivery system, and research and development (R&D) in Nigeria.