AI in Imaging—New Perspectives, 2nd Edition

Background and Objectives: It is unknown whether the tibial tuberosity–tibial intercondylar midpoint (TT–TIM) distance can be interchangeably measured on axial computed tomography (CT) and magnetic resonance imaging (MRI). The objective of this retrospective cross-sectional comparative study was to evaluate the intermethod agreement of the TT–TIM distance on axial CT and MRI and its bias towards tibial rotation (TR), age, sex, and body side. Materials and Methods: On axial CT and MRI of 15 consecutive knee pairs where each pair belonged to the same patient with no pathology affecting the tibial circumference and tibial tuberosity, TT–TIM distance and TR were measured by two blinded radiologists at 2-week intervals. Upon checking the symmetry of distributions (Shapiro–Wilk test), differences between matched knee pairs (Wilcoxon signed-rank test), intermethod (Bland–Altman plot) and interrater agreement (intraclass correlation coefficient [ICC]), and correlations (Spearman rank correlation) were assessed. Results: The mean intermethod difference in TT–TIM distance was not statistically significant (−0.4 mm [−1.82, 0.96]; p = 0.52). The TT–TIM distance did not differ between knee pairs (p = 0.68), its interrater agreement was almost perfect (ICC > 0.81), and no bias towards TR (p > 0.66), age (p > 0.14), sex (p = 0.66), and body side (p > 0.37) was found. Conclusions: The TT–TIM distance can be interchangeably measured on axial CT and MRI with almost perfect interrater agreement, unbiased towards TR, age, sex, and body side. Full article

Background and Objectives: Accurate evaluation of vulnerable carotid atherosclerotic plaques remains essential for preventing ischemic stroke. Conventional imaging modalities such as ultrasound and computed tomography angiography (CTA) have limited capacity to identify histopathological features of plaque instability, including fibrous cap rupture, lipid-rich necrotic core, and intraplaque hemorrhage. Artificial intelligence (AI) techniques—particularly deep learning (DL) and radiomics—have recently emerged as valuable adjuncts to standard imaging, achieving AUC values of 0.83–0.99 across modalities in identifying vulnerable plaques. This scoping review summarizes the available evidence on the application of AI in the detection and assessment of vulnerable carotid plaques. Methods: A systematic search of the English-language literature was conducted in MEDLINE, SCOPUS, and CENTRAL from 2000 to 30 June 2025, following the PRISMA-ScR framework. Eligible studies applied AI-based approaches (machine learning, deep learning, or radiomics) to evaluate carotid plaque vulnerability using ultrasound, CTA, or MRI. Extracted outcomes included diagnostic performance, correlation with histopathology or neurological events, and predictive modeling for stroke risk. Results: Of 201 records screened, 12 studies met inclusion criteria (ultrasound = 6; CTA = 4; high-resolution MRI = 2; publication years 2021–2025). All reported receiver operating characteristic area-under-the-curve (ROC-AUC) values for endpoints related to plaque vulnerability (symptomatic versus asymptomatic status, presence of intraplaque hemorrhage or lipid-rich necrotic core, fibrous-cap surrogates, and, less frequently, short-term cerebrovascular events). For ultrasound, contrast-enhanced videomics achieved an AUC of 0.87 (10 centers; n = 205), B-mode texture/radiomics reached 0.87 (n = 150), and segmentation-assisted models 0.827 (n = 202); other ultrasound models reported AUCs of 0.88–0.91. For CTA, a symptomatic-plaque machine-learning model yielded AUC 0.89 ( n = 106); a perivascular-adipose-tissue (PVAT) radiomics nomogram achieved AUC 0.836 on external validation; a histology-referenced pilot attained AUC 0.987; and one mild-stenosis TIA model reported ROC performance. For high-resolution MRI (HR-MRI), radiomics-based models showed AUC 0.835–0.864 in single-modality cohorts and up to 0.984 with multi-contrast inputs. Across modalities, AUC ranges were: ultrasound 0.827–0.91, CTA 0.836–0.987 (external 0.836), and HR-MRI 0.835–0.984. Only two out of twelve studies performed external validation; calibration and decision-curve analyses were rarely provided, and most cohorts were single-center, limiting generalizability. Conclusions: AI demonstrates strong potential as a complementary tool for evaluating carotid plaque vulnerability, with high diagnostic performance across imaging modalities. Reported AUCs ranged from 0.83 to 0.99 based primarily on internal or hold-out validation, representing the upper bound of theoretical rather than real-world performance. Nonetheless, large prospective multicenter studies with standardized protocols, histopathological correlation, and external validation are required before clinical integration into stroke prevention pathways. Full article

Technology is increasingly shaping human life, particularly in healthcare, where recent advancements have revolutionized patient care. Despite these advances, the World Health Organization’s (WHO) definition of health remains rooted in traditional notions, raising questions about its adequacy in light of technological progress. This paper explores the conceptual and practical limitations of the current definition and argues for its revision to encompass the role of technology in health. This paper examines the evolving landscape of healthcare technology and its philosophical implications, drawing on theories such as the Extended Health Hypothesis and the Extended Mind Hypothesis. It claims that health extends beyond traditional biological boundaries and includes the influence of technology on well-being. This paper advocates for a re-examination of the WHO definition of health to reflect the integral role of technology in modern healthcare. Recognizing technology as part of health necessitates a broader conceptual framework that acknowledges the interconnectedness of biology, technology, and human well-being. Given technology’s transformative role in healthcare, this paper argues for a revaluation of the WHO’s definition of health to encapsulate the evolving relationship between technology and human well-being. At the end, we propose a new definition recognizing that health is a dynamic state of physical, mental, social, and technological well-being, wherein individuals can achieve optimal quality of life through the harmonious integration of biological, psychological, and technological factors. This state encompasses not only the absence of disease but also the effective utilization of advanced technologies. Full article