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• W4313237534 abstract "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 carried out between July 2020 to May 2022. 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 analysis (BIA). The primary outcome was the performance of 8-point LUS using a portable HHUSD in diagnosing fluid overload compared to physical examination and BIA. 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 BIA ( 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." @default.
• W4313237534 created "2023-01-06" @default.
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• W4313237534 date "2022-12-24" @default.
• W4313237534 modified "2023-10-09" @default.
• W4313237534 title "Automated lung ultrasound image assessment using artificial intelligence to identify fluid overload in dialysis patients" @default.
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• W4313237534 doi "https://doi.org/10.1186/s12882-022-03044-7" @default.
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