Applying Radiomics and Deep Learning to Investigations into Seafarers' Health Status (应用人工智能研究, 2025)
This retrospective study on 7232 male seafarers(June 2022 -- Nov 2023), explored the occurrence of fatty liver disease and gallstones and pointed out its related risk elements, moreover examined the worth of using DL-based radiomics in intelligent examination and medical diagnosis of the said hepatobiliary diseases. Clinical data (BMI, blood lipids) and abdominal ultrasound images were analyzed via traditional statistical methods (chi-squared test, correlation analysis, t-test). A DL framework integrating U-Net segmentation and MobileNetV2 classification was developed to automate region-of-interest (ROI) extraction, extract high-dimensional radiomic features, and fuse clinical/radiomic data for dual-disease prediction. Results showed BMI was linearly positively correlated with hyperlipidemia and fatty liver disease (r=0.98, P