Blood purification · 2026
Artificial Intelligence in Peritoneal Dialysis: Applications, Algorithms, and Future Directions.
СтатьяОбзорНазвание и аннотация: Перевод в очереди
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Arriola-Montenegro Jose, Ratishvili Tamar, Kattah Andrea
Аннотация
BACKGROUND: Peritoneal dialysis (PD) naturally lends itself to artificial intelligence (AI) due to its generation of dense, longitudinal, and structured data. PD outcomes are strongly influenced by modifiable factors such as dialysate composition, dwell time, and fluid balance, making prediction directly actionable.
SUMMARY: Recent investigations have demonstrated the potential of machine learning and deep learning approaches across multiple domains of PD care. Applications include pre-dialysis patient stratification, prediction of technique failure, monitoring of dialysis adequacy, and assessment of fluid status. AI models have also been developed for forecasting the causative pathogen in peritonitis and prediction of complications, hospitalizations, and mortality, frequently outperforming conventional statistical methods. In parallel, AI-driven chatbots and digital platforms have shown promise in enhancing patient education, engagement, and adherence. While these findings highlight significant potential, most studies to date are single-center, exploratory, and limited in scale, underscoring the need for rigorous external validation and systematic clinical implementation.
KEY MESSAGES: AI holds considerable promises for improving outcomes in PD by enabling risk identification, individualized therapy, and strengthened patient support. Future directions include integration with electronic health records, remote monitoring, and multi-omics data. Careful validation, ethical safeguards, and equitable deployment will be essential to realize AI's role as a transformative tool in PD care.
SUMMARY: Recent investigations have demonstrated the potential of machine learning and deep learning approaches across multiple domains of PD care. Applications include pre-dialysis patient stratification, prediction of technique failure, monitoring of dialysis adequacy, and assessment of fluid status. AI models have also been developed for forecasting the causative pathogen in peritonitis and prediction of complications, hospitalizations, and mortality, frequently outperforming conventional statistical methods. In parallel, AI-driven chatbots and digital platforms have shown promise in enhancing patient education, engagement, and adherence. While these findings highlight significant potential, most studies to date are single-center, exploratory, and limited in scale, underscoring the need for rigorous external validation and systematic clinical implementation.
KEY MESSAGES: AI holds considerable promises for improving outcomes in PD by enabling risk identification, individualized therapy, and strengthened patient support. Future directions include integration with electronic health records, remote monitoring, and multi-omics data. Careful validation, ethical safeguards, and equitable deployment will be essential to realize AI's role as a transformative tool in PD care.