Intelligent Digital Twins for Personalized Migraine Care

Abstract

Intelligent digital twins closely resemble their real-life counterparts. In health and medical care, they enable the real-time monitoring of patients, whereby large amounts of data can be collected to produce actionable information. These powerful tools are constructed with the aid of artificial intelligence, machine learning, and deep learning; the Internet of Things; and cloud computing to collect a diverse range of digital data (e.g., from digital patient journals, wearable sensors, and digitized monitoring equipment or processes), which can provide information on the health conditions and therapeutic responses of their physical twins. Intelligent digital twins can enable data-driven clinical decision making and advance the realization of personalized care. Migraines are a highly prevalent and complex neurological disorder affecting people of all ages, genders, and geographical locations. It is ranked among the top disabling diseases, with substantial negative personal and societal impacts, but the current treatment strategies are suboptimal. Personalized care for migraines has been suggested to optimize their treatment. The implementation of intelligent digital twins for migraine care can theoretically be beneficial in supporting patient-centric care management. It is also expected that the implementation of intelligent digital twins will reduce costs in the long run and enhance treatment effectiveness. This study briefly reviews the concept of digital twins and the available literature on digital twins for health disorders such as neurological diseases. Based on these, the potential construction and utility of digital twins for migraines will then be presented. The potential and challenges when implementing intelligent digital twins for the future management of migraines are also discussed.

Incorporation of “Artificial Intelligence” for Objective Pain Assessment: A Comprehensive Review

“…Pain is a complex and subjective experience influenced by various factors such as genetics, environment, and psychological state. By integrating data from different sources, digital twins can provide a holistic view of an individual’s pain experience, enabling healthcare providers to tailor treatment plans accordingly [ 150 – 152 ].…”

“…For example, machine learning algorithms can identify patterns in the data that may indicate the effectiveness of different interventions or predict the likelihood of chronic pain development. These patterns assist healthcare providers in their clinical decision-making process, personalizing care, and improving patient outcomes [ 151 , 152 ].…”

Perspective Chapter: Digital Twins for Health – Opportunities, Barriers and a Path Forward

“…This accessibility has played a crucial role in the development of health DT solutions [13]. Moreover, data gathered through personal digital devices, and patient-generated health data, such as patient-reported function or symptoms, physical markers, and demographic, and lifestyle data over time of an individual contribute to the development of more comprehensive DTs [9,14,15]. The integration of diverse data streams within health DTs allows for a broad understanding of an individual's health.…”

A digital twin study on thoracic endovascular repair for Stanford type B aortic dissection

“…Personalized, accurate and predictive risk assessment with tailored prevention treatments remains imperative after TEVAR. In recent years, the digital twin (DT) has been proposed for personalized caring and healthcare management [11]. The concept of DT originates from professor Michael Grieves, which contains the physical object, the virtual replication of it, and the connection between them [12].…”