Artificial intelligence with kidney disease

Abstract

Background: Artificial intelligence (AI) has had a significant impact on our lives and plays many roles in various fields. By analyzing the past 30 years of AI trends in the field of nephrology, using a bibliography, we wanted to know the areas of interest and future direction of AI in research related to the kidney. Methods: Using the Institute for Scientific Information Web of Knowledge database, we searched for articles published from 1990 to 2019 in January 2020 using the keywords AI; deep learning; machine learning; and kidney (or renal). The selected articles were reviewed manually at the points of citation analysis. Results: From 218 related articles, we selected the top fifty with 1188 citations in total. The most-cited article was cited 84 times and the least-cited one was cited 12 times. These articles were published in 40 journals. Expert Systems with Applications (three articles) and Kidney International (three articles) were the most cited journals. Forty articles were published in the 2010s, and seven articles were published in the 2000s. The top-fifty most cited articles originated from 17 countries; the USA contributed 16 articles, followed by Turkey with four articles. The main topics in the top fifty consisted of tumors (11), acute kidney injury (10), dialysis-related (5), kidney-transplant related (4), nephrotoxicity (4), glomerular disease (4), chronic kidney disease (3), polycystic kidney disease (2), kidney stone (2), kidney image (2), renal pathology (2), and glomerular filtration rate measure (1). Conclusions: After 2010, the interest in AI and its achievements increased enormously. To date, AIs have been investigated using data that are relatively easy to access, for example, radiologic images and laboratory results in the fields of tumor and acute kidney injury. In the near future, a deeper and wider range of information, such as genetic and personalized database, will help enrich nephrology fields with AI technology.

The 100 most cited articles have fewer citations than other bibliometric articles: A pairwise comparison using a temporal bubble graph

“…Additionally, many articles include many Tables and graphs in bibliometrics without applying radar plots [23] and chord diagrams [29] to condense information of interest for readers, as we did in Figures 5, 8, and 9, particularly with the TBG in Figure 7 and forest plots in Figures 6 and 10 to display all 100 articles on a dashboard and save article spaces when compared to those with 100 and 50 articles listed in their studies [50,51] or with 42 Tables and graphs in an article. [52]…”

“…[8,[44][45][46][47][48][49] Some studies [8,[44][45][46][47][48]49] have applied citation prediction to predict article citations based on the mean citations of article keywords, but the themes have not been definitely classified and clearly visualized through Equation 4. [9] Additionally, many articles include many Tables and graphs in bibliometrics without applying radar plots [23] and chord diagrams [29] to condense information of interest for readers, as we did in Figures 5, 8, and 9, particularly with the TBG in Figure 7 and forest plots in Figures 6 and 10 to display all 100 articles on a dashboard and save article spaces when compared to those with 100 and 50 articles listed in their studies [50,51] or with 42 Tables and graphs in an article. [52]…”

Deep Learning Plus Three-Dimensional Printing in the Management of Giant (>15 cm) Sporadic Renal Angiomyolipoma: An Initial Report

“…At present, medical image analysis is the most successful applications of this burgeoning science in medicine. In terms of renal diseases, DL enables to autonomically classify renal mass, differentiate tumor grades, evaluate acute kidney injury, and so on (14). Three main DL methods are commonly applied for medical imaging synthesis including auto-encoder, generative adversarial network, and U-net.…”

“…It has recently shown promising performance in various medical tasks, including image synthesis, disease diagnosis, and predictive analysis (13). Particularly, DL in the field of renal diseases enables to autonomically classify renal mass, differentiate tumor grades, evaluate acute kidney injury, and so on (14). DL is currently revolutionizing and reshaping previous medical care strategies.…”

Classification and citation analysis of the 100 top-cited articles on nurse resilience using chord diagrams: A bibliometric analysis

“…Moreover, many articles include many tables and graphs in bibliometrics without utilizing radar plots and chord diagrams to condense information that is of interest to readers, as we did in Figures 1 and 2, especially using the dot plot in Figure 5 to display all those T100NurseR articles on a dashboard and save space compared to those with 100 and 50 articles listed in their studies [67,68] or 42 tables and graphs in an article. [69] To visualize dynamics related to contraceptive use and to apply data, chord diagrams [36,[38][39][40] were used.…”