Evaluation of the Elasticity of the Pancreas Using Acoustic Radiation Force Impulse Elastography in Patients with Acute Pancreatitis: A Systematic Review and Meta-Analysis

Diagnostic performance of acoustic radiation force impulse for acute pancreatitis: A meta-analysis

OBJECTIVE

The objective of this meta-analysis is to evaluate the diagnostic performance of acoustic radiation force impulse (ARFI) in acute pancreatitis (AP) patients.

METHODS

PubMed, Web of Science, Embase, Wanfang, Chinese Biological Medicine databases, and Chinese Biomedical Literature Service System were searched for relevant studies to explore the potential diagnostic performance of ARFI in AP from inception to November 2023. STATA 14.0 was used to analyze the standardized mean difference (SMD) with 95% confidence interval (CI), pooled sensitivity, specificity, area under the curve, meta-regression analysis, sensitivity analysis, and publication bias.

RESULTS

Nine studies, involving 533 AP patients and 585 healthy controls, were included. AP patients had significantly higher ARFI levels than healthy controls (SMD: 3.13, 95% CI: 1.88-4.39, P = .001). The area under the curve of ARFI for diagnosing AP was 0.99 (95% CI: 0.98-1.00), with 98% sensitivity and 94% specificity. Meta-regression identified the study region and study period as the sources of heterogeneity. Sensitivity analysis showed that the exclusion of any single study did not materially alter the overall combined effect. No evidence of publication bias was observed in the included studies.

CONCLUSION

This meta-analysis demonstrated that ARFI exerted satisfactory diagnostic performance in AP.

Exploring the Intersection of Geophysics and Diagnostic Imaging in the Health Sciences

To develop diagnostic imaging approaches, this paper emphasizes the transformational potential of merging geophysics with health sciences. Diagnostic imaging technology improvements have transformed the health sciences by enabling earlier and more precise disease identification, individualized therapy, and improved patient care. This review article examines the connection between geophysics and diagnostic imaging in the field of health sciences. Geophysics, which is typically used to explore Earth's subsurface, has provided new uses of its methodology in the medical field, providing innovative solutions to pressing medical problems. The article examines the different geophysical techniques like electrical imaging, seismic imaging, and geophysics and their corresponding imaging techniques used in health sciences like tomography, magnetic resonance imaging, ultrasound imaging, etc. The examination includes the description, similarities, differences, and challenges associated with these techniques and how modified geophysical techniques can be used in imaging methods in health sciences. Examining the progression of each method from geophysics to medical imaging and its contributions to illness diagnosis, treatment planning, and monitoring are highlighted. Also, the utilization of geophysical data analysis techniques like signal processing and inversion techniques in image processing in health sciences has been briefly explained, along with different mathematical and computational tools in geophysics and how they can be implemented for image processing in health sciences. The key findings include the development of machine learning and artificial intelligence in geophysics-driven medical imaging, demonstrating the revolutionary effects of data-driven methods on precision, speed, and predictive modeling.