Digital Twins in Healthcare: Methodological Challenges and Opportunities.

One of the most promising advancements in healthcare is the application of digital twin technology, offering valuable applications in monitoring, diagnosis, and development of treatment strategies tailored to individual patients. Furthermore, digital twins could also be helpful in finding novel treatment targets and predicting the effects of drugs and other chemical substances in development. In this review article, we consider digital twins as virtual counterparts of real human patients. The primary aim of this narrative review is to give an in-depth look into the various data sources and methodologies that contribute to the construction of digital twins across several healthcare domains. Each data source, including blood glucose levels, heart MRI and CT scans, cardiac electrophysiology, written reports, and multi-omics data, comes with different challenges regarding standardization, integration, and interpretation. We showcase how various datasets and methods are used to overcome these obstacles and generate a digital twin. While digital twin technology has seen significant progress, there are still hurdles in the way to achieving a fully comprehensive patient digital twin. Developments in non-invasive and high-throughput data collection, as well as advancements in modeling and computational power will be crucial to improve digital twin systems. We discuss a few critical developments in light of the current state of digital twin technology. Despite challenges, digital twin research holds great promise for personalized patient care and has the potential to shape the future of healthcare innovation.

Glenoid version: the role of genetic and environmental factors on its variability. An MRI study on asymptomatic elderly twins.

BACKGROUND: Glenoid version is the most variable parameter of the shoulder joint. No authors investigated if intrinsic genetic factors or influences from extrinsic sources are responsible for its variability. AIM: We compared glenoid version between elderly monozygotic and dizygotic twins intending to separate the contributions of genetics from shared and unique environments. METHODS: Glenoid version of the dominant shoulder was assessed by MRI using Friedman's method in 30 pairs of elderly twins (16 monozygotic-14 dizygotic; mean age ± SD: 63.72 ± 3.37, 53-72). Heritability was estimated as twice the difference between the intraclass correlation coefficients for monozygotic and dizygotic pairs. The influence of shared environment was calculated as the difference between monozygotic correlation coefficient and the heritability index. According to job category, one way analysis of variance was used to estimate the differences between groups in the total sample and within zygosity groups. RESULTS: Glenoid version angle in monozygotic and dizygotic twins was -2° (SD: 2°) and -3° (SD: 3°), respectively (p = 0.334). Heritability index was 0.98, while the contributions of shared and unique environment were 0 and 0.02, respectively. According to working classes, no significant differences were found between the groups (p = 0.732, F = 0.31). CONCLUSIONS: Glenoid version is mainly genetically determined and only marginally influenced by environments.Level of evidence: III.