Unseen Artificial Intelligence—Deep Learning Paradigm for Segmentation of Low Atherosclerotic Plaque in Carotid Ultrasound: A Multicenter Cardiovascular Study

Abstract

Background: The early detection of carotid wall plaque is recommended in the prevention of cardiovascular disease (CVD) in moderate-risk patients. Previous techniques for B-mode carotid atherosclerotic wall plaque segmentation used artificial intelligence (AI) methods on monoethnic databases, where training and testing are from the “same” ethnic group (“Seen AI”). Therefore, the versatility of the system is questionable. This is the first study of its kind that uses the “Unseen AI” paradigm where training and testing are from “different” ethnic groups. We hypothesized that deep learning (DL) models should perform in 10% proximity between “Unseen AI” and “Seen AI”. Methodology: Two cohorts from multi-ethnic groups (330 Japanese and 300 Hong Kong (HK)) were used for the validation of our hypothesis. We used a four-layered UNet architecture for the segmentation of the atherosclerotic wall with low plaque. “Unseen AI” (training: Japanese, testing: HK or vice versa) and “Seen AI” experiments (single ethnicity or mixed ethnicity) were performed. Evaluation was conducted by measuring the wall plaque area. Statistical tests were conducted for its stability and reliability. Results: When using the UNet DL architecture, the “Unseen AI” pair one (Training: 330 Japanese and Testing: 300 HK), the mean accuracy, dice-similarity, and correlation-coefficient were 98.55, 78.38, and 0.80 (p < 0.0001), respectively, while for “Unseen AI” pair two (Training: 300 HK and Testing: 330 Japanese), these were 98.67, 82.49, and 0.87 (p < 0.0001), respectively. Using “Seen AI”, the same parameters were 99.01, 86.89 and 0.92 (p < 0.0001), respectively. Conclusion: We demonstrated that “Unseen AI” was in close proximity (<10%) to “Seen AI”, validating our DL model for low atherosclerotic wall plaque segmentation. The online system runs < 1 s.

COVLIAS 2.0-cXAI: Cloud-Based Explainable Deep Learning System for COVID-19 Lesion Localization in Computed Tomography Scans

“…From our past experiences, the effect of generalization can be retained in the deep learning framework to a certain degree. In our recent experiments, where we had applied “unseen test data” on our trained AI models, it resulted in encouraging accuracy [ 27 , 132 ], which justifies “superior generalization” in deep learning frameworks, unlike in machine learning frameworks. Since COVLIAS 2.0-cXAI is a deep learning framework, we thus conclude that the cloud-based “COVLIAS 2.0-cXAI” can be adopted for the longitudinal data sets during the monitoring phase.…”

Novel Biomarkers of Atherosclerotic Vascular Disease—Latest Insights in the Research Field

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Brain Tumor Characterization Using Radiogenomics in Artificial Intelligence Framework

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