Banala, Mallareddy

Systemic sclerosis-associated pulmonary arterial hypertension (SSc-PAH) is a life-threatening vascular complication of SSc, marked by high morbidity and mortality. Early diagnosis remains a major challenge due to nonspecific symptoms and the limitations of conventional tools such as echocardiography (ECHO), pulmonary function tests (PFTs), and serum biomarkers. This review evaluates the emerging role of artificial intelligence (AI), particularly machine learning (ML) and deep learning (DL), in improving the diagnostic landscape of SSc-PAH. A comprehensive literature search was conducted across PubMed, Scopus, IEEE Xplore, Embase and Google Scholar to identify studies involving AI applications in SSc, pulmonary arterial hypertension (PAH), and their intersection. Evidence indicates that AI models can assist interpretation across modalities, including heart sounds, ECGs, chest X-rays (CXRs), ECHOs, CT pulmonary angiography (CTPA), and omics-based biomarkers. While several models show encouraging diagnostic performance, their accuracy varies by dataset and modality, and most require external validation against right heart catheterization (RHC)-confirmed cohorts. Integrating multimodal data through AI frameworks may enhance early recognition and individualized risk stratification; however, these tools remain exploratory. Future work should emphasize harmonized hemodynamic definitions, transparent validation protocols, and SSc-specific datasets to ensure clinical applicability and reproducibility.

Background: Atrial Fibrillation and Atrial Flutter (Afib) ranks 6th in mortality and 7th in disability amongst all cardiovascular disease the G20 nations. Method: We employed 2019 Global Burden of Disease methodology. Afib and Flutter, non-fatal health outcomes, such as incidence and prevalence, were estimated using the Bayesian meta-regression tool (DisMod MR 2.1). Fatal health outcomes were assessed using the Cause of Death Ensemble Model (CODEm), considering factors like age, gender, year, and location throughout the G20 from 1990 to 2019. Results: From 1990 to 2019, prevalence rose from 23.2 million (95%UI: 17.6-29.7) to 48.9 million (37.5-61.6). The annual percentage change (APC) in incidence surged by 102% (1.8-3.8 million), DALYs by 119% (3-6 million), and deaths by 168% (93,864-251,272). Despite these increases, the age-standardized rate per 100,000 person-years declined over the past three decades. In 2019, the U.S. reported the highest age-standardized incidence rate at 109.52 (88.97-131.92) per 100,000, while Sweden had the top mortality rate of 8.63 (4-10.32) per 100,000. The age group 65-69 had the peak incidence count of 700,768 (362,032-1,158,177). Meanwhile, the highest death count was in the 85-89 age bracket with 59,325 (48,618-70,077), and the most DALYs were in the 80-84 age group with 1,083,488 (882,995-1,323,533) in 2019. Males consistently faced a higher burden than females over this period. Conclusion: AFib and AF accounted for 1.88% deaths amongst all cardiovascular casualties in 2019. Clinicians in the G20 countries must prioritize early detection and intervention for Afib and flutter, especially in males and the elderly, given its escalating prevalence and its ranking as a major cause of death and disability. Public health strategies in the G20 nations should focus on targeted awareness and interventions to combat the rising burden of atrial fibrillation, with countries like the United States and Sweden requiring heightened attention due to their notably high incidence and mortality rates.