Table 1.
Top-fifty most-cited articles with titles, first authors, institutions, nationalities, source titles, publication years, and citation numbers.
| Rank | Title | 1st author | Institute/ Nationality | Source titles | Publi-cation year | No. of citati-ons |
| 1 | Pattern analysis of serum proteome distinguishes renal cell carcinoma from other urologic diseases and healthy persons | Yonggwan Won | Chonnam National University Medical School/ South Korea | Proteomics | 2003 | 84 |
| 2 | Defining cell-type specificity at the transcriptional level in human disease | Wenjun Ju | University of Michigan/ USA | Genome research | 2013 | 83 |
| 3 | Fast neural network learning algorithms for medical applications | Ahmad Taher Azar | Misr University for Science and Technology/ Egypt | Neural computing and applications | 2013 | 61 |
| 4 | Application of irregular and unbalanced data to predict diabetic nephropathy using visualization and feature selection methods | Baek Hwan Cho | Hanyang University/ South Korea | Artificial intelligence in medicine | 2008 | 55 |
| 5 | Prediction of drug-induced nephrotoxicity and injury mechanisms with human induced pluripotent stem cell-derived cells and machine learning methods | Karthikeyan Kandasamy | Institute of Bioengineering and Nanotechnology/ Singapore | Scientific reports | 2015 | 40 |
| 6 | Texture analysis as a radiomic marker for differentiating renal tumors | HeiShun Yu | Boston Medical Center/ USA | Abdominal radiology | 2007 | 34 |
| 7 | Application of Machine Learning Techniques to High-Dimensional Clinical Data to Forecast Postoperative Complications | Paul Thottakkara | University of Florida/ USA | Plos One | 2016 | 32 |
| 8 | Incorporating temporal EHR data in predictive models for risk stratification of renal function deterioration | Anima Singh | Massachusetts Institute of Technology/ USA | Journal Of Biomedical Informatics | 2015 | 31 |
| 8 | Biomarker discovery with SELDI-TOF MS in human urine associated with early renal injury: Evaluation with computational analytical tools | Kurt J.A. Vanhoutte | Radboud University Nijmegen Medical Centre/ Netherlands | Nephrology dialysis transplantation | 2007 | 31 |
| 10 | Machine learning-based quantitative texture analysis of CT images of small renal masses: Differentiation of angiomyolipoma without visible fat from renal cell carcinoma | Zhichao Feng | Central South University/ China | European radiology | 2018 | 28 |
| 11 | The Development of a Machine Learning Inpatient Acute Kidney Injury Prediction Model | Koyner Jay L. | University of Chicago/ USA | Critical care medicine | 2018 | 26 |
| 12 | Prediction and detection models for acute kidney injury in hospitalized older adults | Rohit J. Kate | University of Wisconsin-Milwaukee/ USA | BMC medical informatics and decision making | 2016 | 26 |
| 13 | Constructing a nutrition diagnosis expert system | Yuchuan Chen | Taipei Medical University/ Taiwan | Expert Systems With Applications | 2012 | 25 |
| 14 | The Pattern of Longitudinal Change in Serum Creatinine and 90-Day Mortality After Major Surgery | Dmytro Korenkevych | University of Florida/ USA | Annals Of Surgery | 2016 | 24 |
| 14 | Medical multiparametric time course prognoses applied to kidney function assessments | Rainer Schmidt | University of Rostock/ Germany | International Journal Of Medical Informatics | 1999 | 24 |
| 16 | High-throughput imaging-based nephrotoxicity prediction for xenobiotics with diverse chemical structures | Ran Su | Bioinformatics Institute/ Singapore | Archives of toxicology | 2016 | 23 |
| 16 | Incidence, risk factors and prediction of post-operative acute kidney injury following cardiac surgery for active infective endocarditis: an observational study | Matthieu Legrand | Université Paris Descartes/ France | Critical Care | 2013 | 23 |
| 16 | Evolving connectionist system versus algebraic formulas for prediction of renal function from serum creatinine | Mark Roger Marshall | Auckland University of Technology/ New Zealand | Kidney International | 2005 | 23 |
| 19 | Assessing rejection-related disease in kidney transplant biopsies based on archetypal analysis of molecular phenotypes | Jeff Reeve | University of Alberta/ Canada | JCI insight | 2017 | 22 |
| 20 | Deep Semantic Segmentation of Kidney and Space-Occupying Lesion Area Based on SCNN and ResNet Models Combined with SIFT-Flow Algorithm | Kai-jian Xia | China University of Mining and Technology/ China | Journal Of Medical Systems | 2019 | 21 |
| 20 | An end stage kidney disease predictor based on an artificial neural networks ensemble | Tommaso Di Noia | Polytechnic University of Bari/ Italy | Expert systems with applications | 2013 | 21 |
| 22 | Detecting repeated cancer evolution from multiregion tumor sequencing data | Giulio Caravagna | Institute of Cancer Research/ UK | Nature Methods | 2018 | 20 |
| 22 | Performance of an Artificial Multi-observer Deep Neural Network for Fully Automated Segmentation of Polycystic Kidneys | Timothy L. Kline | Mayo Clinic College of Medicine/ USA | Journal Of Digital Imaging | 2017 | 20 |
| 22 | Automatic Segmentation of Kidneys using Deep Learning for Total Kidney Volume Quantification in Autosomal Dominant Polycystic Kidney Disease | Kanishka Sharma | IRCCS-Istituto di Ricerche Farmacologiche Mario Negri/ Italy | Scientific Reports | 2017 | 20 |
| 25 | Bayesian Modeling of Pretransplant Variables Accurately Predicts Kidney Graft Survival | Brown T.S. | Naval Medical Research Center/ USA | American Journal Of Nephrology | 2012 | 19 |
| 25 | Classification strategies for the grading of renal cell carcinomas, based on nuclear morphometry and densitometry | Christine François | Université Libre de Bruxelles/ Belgium | Journal Of Pathology | 1997 | 18 |
| 25 | ADMET Evaluation in Drug Discovery. 18. Reliable Prediction of Chemical-Induced Urinary Tract Toxicity by Boosting Machine Learning-Approaches | Tailong Lei | Zhejiang University/ China | Molecular Pharmaceutics | 2017 | 18 |
| 28 | A medical decision support system for disease diagnosis under uncertainty | Behnam Malmir | Kansas State University/ USA | Expert Systems With Applications | 2017 | 17 |
| 28 | Diagnosis of Chronic Kidney Disease Based on Support Vector Machine by Feature Selection Methods | Huseyin Polat | Gazi University/ Turkey | Journal of medical systems | 2017 | 17 |
| 28 | Artificial intelligence: A new approach for prescription and monitoring of hemodialysis therapy | Ahmed l. Akl | Mansoura University/ Egypt | American Journal Of Kidney Diseases | 2001 | 17 |
| 31 | A clinically applicable approach to continuous prediction of future acute kidney injury | Nenad Tomašev | DeepMind/ UK | Nature | 2019 | 16 |
| 31 | Quantitative Ultrasound for Measuring Obstructive Severity in Children with Hydronephrosis | Juan J. Cerrolaza | Children's National Health System/ USA | Journal of urology | 2016 | 16 |
| 31 | Prediction of delayed graft function after kidney transplantation: comparison between logistic regression and machine learning methods | Alexander Decruyenaere | Ghent University Hospital/ Belgium | BMC medical informatics and decision making | 2015 | 16 |
| 31 | Supervised prediction of drug-induced nephrotoxicity based on interleukin-6 and-8 expression levels | Ran Su | Bioinfromatics Institute/ Singapore | BMC bioinformatics | 2014 | 16 |
| 31 | A prognostic model for temporal courses that combines temporal abstraction and case-based reasoning | Rainer Schmidt | Universität Rostock/ Germany | International Journal Of Medical Informatics | 2005 | 16 |
| 31 | Cardiac risk stratification in renal transplantation using a form of artificial intelligence | Thomas F Heston | Oregon Health Sciences University/ USA | American Journal Of Cardiology | 1997 | 16 |
| 37 | Computer-aided detection of exophytic renal lesions on non-contrast CT images | Jianfei Liu | National Institutes of Health Clinical Center/ USA | Medical Image Analysis | 2015 | 15 |
| 37 | Optimization of anemia treatment in hemodialysis patients via reinforcement learning | Pablo Escandell-Montero | University of Valencia/ Spain | Artificial intelligence in medicine | 2014 | 15 |
| 37 | A novel approach for accurate prediction of spontaneous passage of ureteral stones: Support vector machines | F Dal Moro | University of Padova/ Italy | Kidney International | 2006 | 15 |
| 40 | Decision tree and random forest models for outcome prediction in antibody incompatible kidney transplantation | Torgyn Shaikhina | University of Warwick/ UK | Biomedical Signal Processing And Control | 2019 | 14 |
| 40 | Radiogenomics in Clear Cell Renal Cell Carcinoma: Machine Learning-Based High-Dimensional Quantitative CT Texture Analysis in Predicting PBRM1 Mutation Status | Burak Kocak | Istanbul Training and Research Hospital/ Turkey | American Journal Of Roentgenology | 2019 | 14 |
| 40 | Clear Cell Renal Cell Carcinoma: Machine Learning-Based Quantitative Computed Tomography Texture Analysis for Prediction of Fuhrman Nuclear Grade | Ceyda Turan Bektas | Istanbul Training and Research Hospital/ Turkey | European Radiology | 2019 | 14 |
| 40 | Development of Biomarker Models to Predict Outcomes in Lupus Nephritis | Bethany J. Wolf | Medical University of South Carolina/ USA | Arthritis & Rheumatology | 2016 | 14 |
| 40 | Ratsnake: A Versatile Image Annotation Tool with Application to Computer-Aided Diagnosis | D.K. Iakovidis | Technological Educational Institute of Lamia/ Greece | Scientific World Journal | 2014 | 14 |
| 45 | Textural differences between renal cell carcinoma subtypes: Machine learning-based quantitative computed tomography texture analysis with independent external validation | Burak Kocak | Istanbul Training and Research Hospital/ Turkey | European Journal Of Radiology | 2018 | 13 |
| 45 | An international observational study suggests that artificial intelligence for clinical decision support optimizes anemia management in hemodialysis patients | Carlo Barbieri | Fresenius Medical Care/ Germany | Kidney international | 2016 | 13 |
| 45 | Efficient Small Blob Detection Based on Local Convexity, Intensity and Shape Information | Min Zhang | Mayo Clinic/ USA | IEEE transactions on medical imaging | 2016 | 13 |
| 48 | Calibration drift in regression and machine learning models for acute kidney injury | Sharon E Davis | Vanderbilt University School of Medicine/ USA | Journal of the American medical informatics association | 2017 | 12 |
| 48 | Differentiation of fat-poor angiomyolipoma from clear cell renal cell carcinoma in contrast-enhanced MDCT images using quantitative feature classification | Han Sang Lee | Korea Advanced Institute of Science and Technology/ South Korea | Medical Physics | 2017 | 12 |
| 48 | Application of rough set classifiers for determining hemodialysis adequacy in ESRD patients | You-Shyang Chen | Hwa Hsia Institute of Technology /Taiwan | Knowledge And Information Systems | 2013 | 12 |
CT = computed tomograpghy, ESRD = End stage renal disease, IEEE = Institute of Electrical and Electronics Engineers, MDCT = Multi Detector Computed Tomography, SCNN = Siamese Convolutional neural network.