Automated lung ultrasound image assessment using artificial intelligence to identify fluid overload in dialysis patients

Abstract

Background Fluid assessment is challenging, and fluid overload poses a significant problem among dialysis patients, with pulmonary oedema being the most serious consequence. Our study aims to develop a simple objective fluid assessment strategy using lung ultrasound (LUS) and artificial intelligence (AI) to assess the fluid status of dialysis patients. Methods This was a single-centre study of 76 hemodialysis and peritoneal dialysis patients. The fluid status of dialysis patients was assessed via a simplified 8-point LUS method using a portable handheld ultrasound device (HHUSD), clinical examination and bioimpedance spectroscopy (BIS). The primary outcome was the performance of 8-point LUS using a portable HHUSD in diagnosing fluid overload compared to physical examination and BIS. The secondary outcome was to develop and validate a novel AI software program to quantify B-line count and assess the fluid status of dialysis patients. Results Our study showed a moderate correlation between LUS B-line count and fluid overload assessed by clinical examination (r=0.475, p<0.001) and BIS (r=0.356. p<0.001). The use of AI to detect B-lines on LUS in our study for dialysis patients was shown to have good agreement with LUS B lines observed by physicians; (r=0.825, p<0.001) for the training dataset and (r=0.844, p<0.001) for the validation dataset. Conclusion Our study confirms that 8-point LUS using HHUSD, with AI-based detection of B lines, can provide clinically useful information on the assessment of hydration status and diagnosis of fluid overload for dialysis patients in a user-friendly and time-efficient way.