Intelligent Oscillometric System for Automatic Detection of Peripheral Arterial Disease

The Role of AI in Hospitals and Clinics: Transforming Healthcare in the 21st Century

As healthcare systems around the world face challenges such as escalating costs, limited access, and growing demand for personalized care, artificial intelligence (AI) is emerging as a key force for transformation. This review is motivated by the urgent need to harness AI's potential to mitigate these issues and aims to critically assess AI's integration in different healthcare domains. We explore how AI empowers clinical decision-making, optimizes hospital operation and management, refines medical image analysis, and revolutionizes patient care and monitoring through AI-powered wearables. Through several case studies, we review how AI has transformed specific healthcare domains and discuss the remaining challenges and possible solutions. Additionally, we will discuss methodologies for assessing AI healthcare solutions, ethical challenges of AI deployment, and the importance of data privacy and bias mitigation for responsible technology use. By presenting a critical assessment of AI's transformative potential, this review equips researchers with a deeper understanding of AI's current and future impact on healthcare. It encourages an interdisciplinary dialogue between researchers, clinicians, and technologists to navigate the complexities of AI implementation, fostering the development of AI-driven solutions that prioritize ethical standards, equity, and a patient-centered approach.

The Accuracy and Reliability of Automated Oscillometric Measurement of Ankle-Brachial Index in the Diagnosis of Peripheral Artery Disease

BACKGROUND

Comparing the accuracy and reliability of ankle-brachial index (ABI) measured by an oscillometric device with the Doppler method in peripheral arterial disease (PAD).

METHODS

122 patients admitted to the department of interventional radiology with PAD were studied. ABI was measured with the 2 methods in random order. After excluding the inconclusive results, Doppler ABIs were compared with the oscillometric ABIs in each limb, the reliability was evaluated by intraclass correlation coefficient (ICC), and the accuracy and consistency were assessed by receiver operating characteristic curves and Bland-Altman method.

RESULTS

In 122 patients (244 legs), 27 legs got inconclusive oscillometric results, 4 legs got inconclusive oscillometric and inconclusive dorsalis pedis artery Doppler results, 2 legs had oscillometric cuff pressure intolerance. Using Doppler ABI <0.9 as a diagnostic threshold, compared with the high Doppler ABI, oscillometric method had a sensitivity of 57.81%, a specificity of 95.18%, the ICC was 0.626 (95% confidence interval [CI]: 0.536-0.701), Bland-Altman method showed 11/211 (5.21%) difference points outside the 95% limits of agreement. Compared with the low Doppler ABI, oscillometric method had a sensitivity of 50.66%, a specificity of 98.31%, the ICC was 0.541(95% CI: 0.483-0.630), Bland-Altman method showed 11/211 (5.21%) difference points outside the 95% limits of agreement. Using 1.00 as the diagnostic threshold and considering error oscillometric results as abnormal ABIs, the sensitivity improved to 88.05% with high Doppler ABI and 81.42% with the low.

CONCLUSIONS

The accuracy and reliability of oscillometric ABI in patients with PAD is unsatisfied, which makes it not suitable as an alternative method in clinic disease assessment. Using 1.0 as the ABI oscillometric threshold for PAD diagnosis can improve the diagnostic value.