Artificial intelligence in peritoneal dialysis: general overview

Bai, Qiong; Tang, Wen

doi:10.1080/0886022x.2022.2064304

Cited by 8 publications

(3 citation statements)

References 40 publications

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“…31,32 AI handles huge data using various algorithms, analyzes numerous typological criteria, and combines clinical and patient data at a finer and more powerful level than is now possible. 33,34 Bioinformatics experts, computer scientists, and data engineers will play an increasingly essential role in clinical and research settings in the future, allowing for more precise detection and treatment of spinal illnesses. 3 Robotics has great potential for spinal surgery.…”

Section: Accepted Manuscriptmentioning

confidence: 99%

A Bibliometric Analysis of Artificial Intelligence Applications in Spine Care

Zhang

Zhao

et al. 2023

J Neurol Surg A Cent Eur Neurosurg

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Background With the rapid development of science and technology, artificial intelligence (AI) has been widely used in the diagnosis and prognosis analysis of various spine diseases. It has been proved that AI has a broad prospect in the accurate diagnosis and treatment of spine field. Methods On May 07, 2022, the Web of Science (WOS) Core Collection database was used to identify the documents on the application of AI in spine field. HistCite and VOSviewer softwares were used for citation analysis and visualization mapping. Results A total of 693 documents were included in the final analysis. The most prolific authors were Karhade AV and Schwab JH. The USA was the most productive country. The leading journal was Spine. The most frequently used keyword was spinal. The most prolific institution was Northwestern Univ. Network visualization map showed that USA was the largest network of international cooperation. The keyword “machine learning” had the strongest total link strengths (TLS) and largest number of occurrences. The latest trends suggest that AI for the diagnosis of spine diseases may receive widespread attention in the future. Conclusions AI has a wide range of application prospect in the subject of spine field, and an increasing number of scholars are committed to the research of AI in spine field. Bibliometric analysis in the field of AI and spine provides an overall perspective, and the appreciation and research of these influential publications are useful for future research.

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Section: Accepted Manuscriptmentioning

confidence: 99%

A Bibliometric Analysis of Artificial Intelligence Applications in Spine Care

Zhang

Zhao

et al. 2023

J Neurol Surg A Cent Eur Neurosurg

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“…7,11,12 A meta-analysis of KFRT patients undergoing PD studies using AI with machine learning, mostly for predictions in PD care, such as patient stratification, technique failure, PD peritonitis and mortality, showed AI algorithm can provide better results than traditional statistical methods. 13 A study by developing a predictive model to predict prolonged hospital stay in PD-treated patients. The results demonstrate the feasibility of utilizing prolongation of hospital stay predictive models to aid clinical care and provide optimal patient management.…”

Section: Introductionmentioning

confidence: 99%

Implementation of artificial intelligence Chatbot in peritoneal dialysis nursing care: Experience from a Taiwan medical center

Cheng,

Lin,

Liu

et al. 2023

Nephrology

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PurposeCoronavirus disease pandemic makes high requirements in delivering remote and efficient communication, and patient education. Because of the increases of digital and social media applications in Taiwan, we aim to apply Artificial Intelligence of LINE Chatbot to improve the self‐care ability of patients undergoing peritoneal dialysis.Materials and MethodsWe conducted the project since 1 May 2021 to 30 April 2022. We designed an automatically replied Chatbot system, and divided into six scopes of interaction interfaces, including peritoneal dialysis technique operation video, clinical reminder, home caring, hospital registration service, dietary guideline, and Automatic Peritoneal dialysis guidance. We surveyed patients' satisfaction with the LINE Chatbot 3 months later by the Likert 1–5 score‐based questionnaire and the higher score indicated higher satisfaction.ResultsThere were 440 patients who joined the PD AI Chatbot study and use the Chatbot, but the satisfaction questionnaire recovery rate was only 297 patients. We found that 91.7% of participants agreed ‘Overall satisfaction with patient intelligent Chatbot application’. More than 4 points accounted for 86.6%. We traced every click in each section of the Chatbot and explored the potential scenario patients face. The Chatbot statistically significant reduced infection rate of exit site and tunnel infection before using (p = .049 and .024). Furthermore, peritonitis rate decreased from 0.93 to 0.8/100 patient month after using Artificial Intelligence technique. Peritoneal dialysis Artificial Intelligence Chatbot had similar effect with face‐to‐face education.ConclusionThe innovative Chatbot allowed delivering remote and digital information's to improve patients' self‐care ability. Peritoneal dialysis patients were highly recognized and satisfied with the tools. It deserves to further explore the long‐term impact of Artificial Intelligence Chatbot in peritoneal dialysis care.

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“…Although PD is less physiologically stressful [ 2 ] and economically more feasible [ 3 ] than hemodialysis (HD), the prevalence of PD among maintenance dialysis patients in the United States is much lower than in the rest of the world. Per available data, PD prevalence was 6.9% and 10.1% in 2009 and 2017, respectively, among maintenance dialysis patients in United States [ 4 ].…”

Section: Introductionmentioning

confidence: 99%

A Review on Major Pathways Leading to Peritoneal Fibrosis in Patients Receiving Continuous Peritoneal Dialysis

Taheri¹,

Thiagaraj²,

Shukla³

et al. 2022

Cureus

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Peritoneal fibrosis (PF) is the most important complication of peritoneal dialysis (PD) that may arise among patients receiving continuous ambulatory peritoneal dialysis (CAPD). PF is a complex process, and many factors contribute to the formation of fibrosis. PD solutions with high glucose content, chronic inflammation, inflammatory cytokines, angiogenesis, and mesothelial to mesenchymal transition (MMT) are factors contributing to the fibrosis of the peritoneum. These factors, as well as stress-induced fibrosis, are going to be discussed further in this article.Although most experimental models are promising in preventing or delaying PD-related fibrosis, most of these recommended treatment options require further research. The lack of sufficient data from real PD patients and many inconclusive data make clinicians depend on conservative treatment. New therapeutics are indeed required for the management of patients undergoing PD to prevent the dreaded complication that may arise from continuous PD. Newer PD solutions are needed to improve survival and minimize the complication associated with PD. Recently, newer PD solutions have been shown to improve patient survival and peritoneal viability and reduce this complication that may arise as a result of continuous PD.

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Artificial intelligence in peritoneal dialysis: general overview

Cited by 8 publications

References 40 publications

A Bibliometric Analysis of Artificial Intelligence Applications in Spine Care

A Bibliometric Analysis of Artificial Intelligence Applications in Spine Care

Implementation of artificial intelligence Chatbot in peritoneal dialysis nursing care: Experience from a Taiwan medical center

A Review on Major Pathways Leading to Peritoneal Fibrosis in Patients Receiving Continuous Peritoneal Dialysis

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