Radiomics in Urolithiasis: a Systematic Review of Current Applications, Limitations and Future Directions
- Authors: Pranovich A.A.1, Karmazanovsky G.G.1, Sirota E.S.2, Firsov M.A.3, Simonov P.A.3, Junker A.I.3, Dzhatdoeva M.K.1, Khubiev D.A.4
-
Affiliations:
- A.V. Vishnevsky National Medical Research Center of Surgery
- I.M. Sechenov First Moscow State Medical University (Sechenov University)
- V.F. Voyno-Yasenetsky Krasnoyarsk State Medical University
- Medical Care Center “DocCity”
- Issue: Vol 79, No 5 (2024)
- Pages: 393-405
- Section: INTERNAL DISEASES: CURRENT ISSUES
- Published: 14.01.2025
- URL: https://vestnikramn.spr-journal.ru/jour/article/view/17953
- DOI: https://doi.org/10.15690/vramn17953
- ID: 17953
Cite item
Abstract
Radiomic image analysis of kidney stones has significantly improved the accuracy of kidney stone type prediction. Such advances in medical imaging technologies and machine learning are likely to be more widely used in routine clinical management of KSD in the near future. However, there is still room for further improvement of machine learning algorithms to improve the sensitivity and specificity of automatic classification methods. Creating a network of centralized database for each type of stone, including demographic and general information about patients, urine, blood parameters, other laboratory tests, treatment methods and their results, will allow the development of a more reliable machine learning algorithm for personalized medicine for KSD.
Keywords
Full Text
About the authors
Alexandr A. Pranovich
A.V. Vishnevsky National Medical Research Center of Surgery
Author for correspondence.
Email: alex.pr76@mail.ru
ORCID iD: 0000-0002-6034-9269
SPIN-code: 1096-6331
PhD in Biology, Senior Researcher
Россия, MoscowGrigory G. Karmazanovsky
A.V. Vishnevsky National Medical Research Center of Surgery
Email: karmazanovsky@yandex.ru
ORCID iD: 0000-0002-9357-0998
SPIN-code: 5964-2369
MD, PhD, Professor, Academician of the RAS
Россия, MoscowEvgeniy S. Sirota
I.M. Sechenov First Moscow State Medical University (Sechenov University)
Email: essirota@mail.ru
ORCID iD: 0000-0001-6419-0155
SPIN-code: 6315-7050
MD, PhD
Россия, MoscowMikhail A. Firsov
V.F. Voyno-Yasenetsky Krasnoyarsk State Medical University
Email: Firsma@mail.ru
ORCID iD: 0000-0002-0887-0081
SPIN-code: 6308-6260
PhD, Head of the Department of Urology
Россия, KrasnoyarskPavel A. Simonov
V.F. Voyno-Yasenetsky Krasnoyarsk State Medical University
Email: doctorsimonov@mail.ru
ORCID iD: 0000-0001-6741-5428
SPIN-code: 2339-2848
Assistant of the Department of Urology
Россия, KrasnoyarskAlexander I. Junker
V.F. Voyno-Yasenetsky Krasnoyarsk State Medical University
Email: junkeralex82@gmail.com
Applicant of the Department of Urology
Россия, KrasnoyarskMariam K. Dzhatdoeva
A.V. Vishnevsky National Medical Research Center of Surgery
Email: mari.dzh0709@mail.ru
ORCID iD: 0009-0003-1589-3817
Resident
Россия, MoscowDinislam A. Khubiev
Medical Care Center “DocCity”
Email: Dinislamx@mail.ru
ORCID iD: 0009-0002-0487-8323
MD, Head of the Urology Department
Россия, CherkesskReferences
- Global Burden of Disease Collaborative Network. Global Burden of Disease Study 2019 (GBD 2019) Disease and Injury Burden 1990–2019. Seattle, USA: Institute for Health Metrics and Evaluation (IHME); 2020.
- Wang Z, Zhang Y, Zhang J, et al. Recent advances on the mechanisms of kidney stone formation (Review). Int J Mol Med. 2021;48(2):149. doi: https://doi.org/10.3892/ijmm.2021.4982
- Мorgan MS, Pearle MS. Medical management of renal stones. BMJ. 2016;352:i52. doi: https://doi.org/10.1136/bmj.i52
- Каприн А.Д., Аполихин О.И., Сивков А.В., и др. Заболеваемость мочекаменной болезнью в Российской Федерации с 2005 по 2020 г. // Экспериментальная и клиническая урология. — 2022. — Т. 15. — № 2. — С. 10–17. [Kaprin AD, Apolikhin OI, Sivkov AV, et al. Incidence of urolithiasis in the Russian Federation from 2005 to 2020. Experimental and Clinical Urology. 2022;15(2):10–17. (In Russ).] doi: https://doi.org/10.29188/2222-8543-2022-15-2-10-1
- Wagner MW, Namdar K, Biswas A, et al. Radiomics, machine learning, and artificial intelligence — what the neuroradiologist needs to know. Neuroradiology. 2021;63(12):1957–1967. doi: https://doi.org/10.1007/s00234-021-02813-9
- Пранович А.А., Исмаилов А.К., Карельская Н.А., и др. Искусственный интеллект в диагностике и лечении мочекаменной болезни // Российский журнал телемедицины и электронного здравоохранения. — 2022. — Т. 8. — № 1. — С. 42–57. [Pranovich AA, Ismailov AK, Karelskaya NA, et al. Artificial intelligence in the diagnosis and treatment of urolithiasis. Russian Journal of Telemedicine and Electronic Health. 2022;8(1):42–57. (In Russ).] doi: https://doi.org/10.29188/2712-9217-2022-8-1-42-57
- Gillies RJ, Kinahan PE, Hricak H. Radiomics: Images Are More than Pictures, They Are Data. Radiology. 2016;278(2):563–577. doi: https://doi.org/10.1148/radiol.2015151169
- Rizzo S, Botta F, Raimondi S, et al. Radiomics: the facts and the challenges of image analysis. Eur Radiol Exp. 2018;2(1):36. doi: https://doi.org/10.1186/s41747-018-0068-z
- Cho HН, Lee HY, Kim E, et al. Radiomics-guided deep neural networks stratify lung adenocarcinoma prognosis from CT scans. Commun Biol. 2021;4(1):1286. doi: https://doi.org/10.1038/s42003-021-02814-7
- Zhang X, Zhang Y, Zhang G, et al. Deep Learning with Radiomics for Disease Diagnosis and Treatment: Challenges and Potential. Front Oncol. 2022;12:773840. doi: https://doi.org/10.3389/fonc.2022.773840
- Avanzo M, Stancanello J, El Naqa I. Beyond imaging: The promise of radiomics. Phys Med. 2017;38:122–139. doi: https://doi.org/10.1016/j.ejmp.2017.05.071
- Keoghane S, Walmsley B, Hodgson D. The natural history of untreated renal tract calculi. BJU Int. 2010;105(12):1627–1629. doi: https://doi.org/10.1111/j.1464-410X.2010.09389.x
- Straub M, Strohmaier WL, Berg W, et al. Diagnosis and metaphylaxis of stone disease. Consensus concept of the National Working Committee on Stone Disease for the upcoming German Urolithiasis Guideline. World J Urol. 2005;23(5):309–323. doi: https://doi.org/10.1007/s00345-005-0029-z
- Ananthakrishnan L, Duan X, Xi Y, et al. Duallayer spectral detector CT: non-inferiority assessment compared to dual-source dual energy CT in discriminating uric acid from non-uric acid renal stones ex vivo. Abdom Radiol (NY). 2018;43(11):3075–3081. doi: https://doi.org/10.1007/s00261-018-1589-x
- Liden M. A new method for predicting uric acid composition in urinary stones using routine single energy. Urolithiasis. 2018;46(4):325–333. doi: https://doi.org/10.1007/s00240-017-0994-x
- Zheng J, Yu H, Batur J, et al. A multicenter study to develop a non-invasive radiomic model to identify urinary infection stone in vivo using machine-learning. Kidney Int. 2021;100(4):870–880. doi: https://doi.org/10.1016/j.kint.2021.05.031
- Kriegshauser JS, Silva AC, Paden RG, et al. Ex vivo renal stone characterization with single-source dual-energy computed tomography: a multiparametric approach. Acad Radiol. 2016;23(8):969–976. doi: https://doi.org/10.1016/j.acra.2016.03.009
- Grosse Hokamp N, Lennartz S, Salem J, et al. Dose independent characterization of renal stones by means of dual energy computed tomography and machine learning: an ex-vivo study. Eur Radiol. 2020;30(3):1397–1404. doi: https://doi.org/10.1007/s00330-019-06455-7
- Chen HW, Chen YC, Lee JT, et al. Prediction of the uric acid component in nephrolithiasis using simple clinical information about metabolic disorder and obesity: a machine learning-based model. Nutrients. 2022;14(9):1829. doi: https://doi.org/10.3390/nu14091829
- Abraham A, Kavoussi NL, Sui W, et al. Machine learning prediction of kidney stone composition using electronic health record-derived features. J Endourol. 2022;36(2):243–250. doi: https://doi.org/10.1089/end.2021.0211
- Onal EG, Tekgul H. Assessing kidney stone composition using smartphone microscopy and deep neural networks. BJUI Compass. 2022;3(4):310–315. doi: https://doi.org/10.1002/bco2.137
- Estrade V, Daudon M, Richard E, et al. Deep morphological recognition of kidney stones using intra-operative endoscopic digital videos. Phys Med Biol. 2022;67(16). doi: https://doi.org/10.1088/1361-6560/ac8592
- Xiang L, Jin X, Liu Y, et al. Prediction of the occurrence of calcium oxalate kidney stones based on clinical and gut microbiota characteristics. World J Urol. 2022;40(1):221–227. doi: https://doi.org/10.1007/s00345-021-03801-7
- Kazemi Y, Mirroshandel SA. A novel method for predicting kidney stone type using ensemble learning. Artif Intell Med. 2018;84:117–126. doi: https://doi.org/10.1016/j.artmed.2017.12.001
- Sacli B, Aydinalp C, Cansiz G, et al. Microwave dielectric property based classification of renal calculi: Application of a kNN algorithm. Comput Biol Med. 2019;112:103366. doi: https://doi.org/10.1016/j.compbiomed.2019.103366
- Black KM, Law H, Aldoukhi A, et al. Deep learning computer vision algorithm for detecting kidney stone composition. BJU Int. 2020;125(6):920–924. doi: https://doi.org/10.1111/bju.15035
- Luk AC, Cleveland P, Olson L, et al. Pelvic phlebolitis: a trivial exercise for the urologist? J Endurol. 2017;31(4):342–347. doi: https://doi.org/10.1089/end.2016.0861
- Karius BM, Long B. Is this your stone? Distinguishing phleboliths from nephroliths on imaging in the emergency department. J Emerg Med. 2022;62(3):316–323. doi: https://doi.org/10.1016/j.jemermed.2021.10.034
- De Perrot T, Hofmeister J, Burgermeister S, et al. Differentiating kidney stones from phleboliths in unenhanced low-dose computed tomography using radiomics and machine learning. Eur Radiol. 2019;29(9):4776–4782. doi: https://doi.org/10.1007/s00330-019-6004-7
- Parakh A, Lee H, Lee JH, et al. Urinary Stone Detection on CT Images Using Deep Convolutional Neural Networks: Evaluation of Model Performance and Generalization. Radiol Artif Intell. 2019;1(4):e180066. doi: https://doi.org/10.1148/ryai.2019180066
- Homayounieh F, Doda Khera R, Bizzo BC, et al. Prediction of burden and management of renal calculi from whole kidney radiomics: a multicenter study. Abdom Radiol (NY). 2021;46(5):2097–2106. doi: https://doi.org/10.1007/s00261-020-02865-0
- Li D, Xiao C, Liu Y, et al. Deep segmentation networks for segmenting kidneys and detecting kidney stones in unenhanced abdominal CT images. Diagnostics (Basel). 2022;12(8):1788. doi: https://doi.org/10.3390/diagnostics12081788
- Caglayan A, Horsanali MO, Kocadurdu K, et al. Deep learning model-assisted detection of kidney stones on computed tomography. Int Braz J Urol. 2022;48(5):830–839. doi: https://doi.org/10.1590/S1677-5538.IBJU.2022.0132
- Längkvist M, Jendeberg J, Thunberg P, et al. Computer aided detection of ureteral stones in thin slice computed tomography volumes using Convolutional Neural Networks. Comput Biol Med. 2018;97:153–160. doi: https://doi.org/10.1016/j.compbiomed.2018.04.021
- Sudharson S, Kokil P. Computer-aided diagnosis system for the classification of multi-class kidney abnormalities in the noisy ultrasound images. Comput Methods Programs Biomed. 2021;205:106071. doi: https://doi.org/10.1016/j.cmpb.2021.106071
- Yildirim K, Bozdag PG, Talo M, et al. Deep learning model for automated kidney stone detection using coronal CT images. Comput Biol Med. 2021;135:104569. doi: https://doi.org/10.1016/j.compbiomed.2021.104569
- Cui Y, Sun Z, Ma S, et al. automatic detection and scoring of kidney stones on noncontrast CT images using S.T.O.N.E. nephrolithometry: combined deep learning and thresholding methods. Mol Imaging Biol. 2021;23(3):436–445. doi: https://doi.org/10.1007/s11307-020-01554-0
- Elton DC, Turkbey EB, Pickhardt PJ, et al. A deep learning system for automated kidney stone detection and volumetric segmentation on noncontrast CT scans. Med Phys. 2022;49(4):2545–2554. doi: https://doi.org/10.1002/mp.15518
- Babajide R, Lembrikova K, Ziemba J, et al. Automated machine learning segmentation and measurement of urinary stones on CT scan. Urology. 2022;169:41–46. doi: https://doi.org/10.1016/j.urology.2022.07.029
- Kavoussi NL, Floyd C, Abraham A, et al. Machine learning models to predict 24 hour urinary abnormalities for kidney stone disease. Urology. 2022;169:52–57. doi: https://doi.org/10.1016/j.urology.2022.07.008
- Mannil M, von Spiczak J, Hermanns T, et al. Prediction of successful shock wave lithotripsy with CT: a phantom study using texture analysis. Abdom Radio (NY). 2018;l43(6):1432–1438. doi: https://doi.org/10.1007/s00261-017-1309-y
- Mannil M, von Spiczak J, Hermanns T, et al. Three-Dimensional Texture Analysis with Machine Learning Provides Incremental Predictive Information for Successful Shock Wave Lithotripsy in Patients with Kidney Stones. J Urol. 2018;200(4):829–836. doi: https://doi.org/10.1016/j.juro.2018.04.059
- Oktay C, Çoraplı M, Tutuş A. The usefulness of the Hounsfield unit and stone heterogeneity variation in predicting the shockwave lithotripsy outcome. Diagn Interv Radiol. 2022;28(3):187–192. doi: https://doi.org/10.5152/dir.2022.20945
- Aminsharifi A, Irani D, Pooyesh S, et al. Artificial neural network system to predict the postoperative outcome of percutaneous nephrolithotomy. J Endourol. 2017;31(5):461–467. doi: https://doi.org/10.1089/end.2016.0791
- Shabaniyan T, Parsaei H, Aminsharifi A, et al. An artificial intelligence-based clinical decision support system for large kidney stone treatment. Australas Phys Eng Sci Med. 2019;42(3):771–779. doi: https://doi.org/10.1007/s13246-019-00780-3
- Hameed BMZ, Shah M, Naik N, et al. Application of artificial intelligence-based classifiers to predict the outcome measures and stone-free status following percutaneous nephrolithotomy for staghorn calculi: cross-validation of data and estimation of accuracy. J Endourol. 2021;35(9):1307–1313. doi: https://doi.org/10.1089/end.2020.1136
- Michaels EK, Niederberger CS, Golden RM, et al. Use of a neural network to predict stone growth after shock wave lithotripsy. Urology. 1998;51(2):335–338. doi: https://doi.org/10.1016/s0090-4295(97)00611-0
- Poulakis V, Dahm P, Witzsch U, et al. Prediction of lower pole stone clearance after shock wave lithotripsy using an artificial neural network. J Urol. 2003;169(4):1250–1256. doi: https://doi.org/10.1097/01.ju.0000055624.65386.b9
- Gomha MA, Sheir KZ, Showky S, et al. Can we improve the prediction of stone-free status after extracorporeal shock wave lithotripsy for ureteral stones? A neural network or a statistical model? J Urol. 2004;172(1):175–179. doi: https://doi.org/10.1097/01.ju.0000128646.20349.27
- Seckiner I, Seckiner S, Sen H, et al. A neural network — based algorithm for predicting stone — free status after ESWL therapy. Int Braz J Urol. 2017;43(6):1110–1114. doi: https://doi.org/10.1590/S1677-5538.IBJU.2016.0630
- Lee Y, Kim N, Cho KS, et al. Bayesian Classifier for predicting malignant renal cysts on MDCT: early clinical experience. AJR Am J Roentgenol. 2009;193(2):W106–111. doi: https://doi.org/10.2214/AJR.08.1858
- Brown TS, Elster EA, Stevens K, et al. Bayesian modeling of pretransplant variables accurately predicts kidney graft survival. Am J Nephrol. 2012;36(6):561–569. doi: https://doi.org/10.1159/000345552
- Topuz K, Zengul FD, Dag A, et al. Predicting Graft Survival among Kidney Transplant Recipients: A Bayesian Decision Support Model. Decision Support Systems. 2018;106:97–109. doi: https://doi.org/10.1016/j.dss.2017.12.004
- Ibrahim NE, McCarthy CP, Shrestha S, et al. A clinical, proteomics, and artificial intelligence-driven model to predict acute kidney injury in patients undergoing coronary angiography. Clin Cardiol. 2019;42(2):292–298. doi: https://doi.org/10.1002/clc.23143
- Elihimas Júnior UF, Couto JP, Pereira W, et al. Logistic Regression Model in a Machine Learning Application to Predict Elderly Kidney Transplant Recipients with Worse Renal Function One Year after Kidney Transplant: Elderly KTbot. J Aging Res. 2020;2020:7413616. doi: https://doi.org/10.1155/2020/7413616
- Boukenze B, Haqiq A, Mousannif H. Predicting Chronic Kidney Failure Disease Using Data Mining Techniques. In: Advances in Ubiquitous Networking 2. Springer Singapore; 2017. P. 701–712. doi: https://doi.org/10.1007/978-981-10-1627-1_55
- Aldeman NLS, de Sá Urtiga Aita KM, Machado VP, et al. Smartpathk: a platform for teaching glomerulopathies using machine learning. BMC Med Educ. 2021;21(1):248. doi: https://doi.org/10.1186/s12909-021-02680-1
- Greco R, Papalia T, Lofaro D, et al. Decisional trees in renal transplant follow-up. Transplant Proc. 2010;42(4):1134–1136. doi: https://doi.org/10.1016/j.transproceed.2010.03.061
- Aalamifar F, Rivaz H, Cerrolaza JJ, et al. Classification of kidney and liver tissue using ultrasound backscatter data. In: Medical Imaging 2015: Ultrason. Imaging Tomography. 2015. Vol. 9419. P. 192–199. doi: https://doi.org/10.1117/12.2082300
- Salekin A, Stankovic J. Detection of chronic kidney disease and selecting important predictive attributes. In: Proceedings of the 2016 IEEE International Conference on Healthcare Informatics (ICHI), Chicago, IL, USA, 4–7 October 2016. P. 262–270.
- Subasi A, Alickovic E, Kevric J. Diagnosis of Chronic Kidney Disease by Using Random Forest. IFMBE Proceedings Book Series; Springer: Berlin/Heidelberg, Germany; 2017. P. 589–594.
- Sanchez-Pinto LN, Venable LR, Fahrenbach J, et al. Comparison of variable selection methods for clinical predictive modeling. Int J Med Inform. 2018;116:10–17. doi: https://doi.org/10.1016/j.ijmedinf.2018.05.006
- Singh NP, Bapi RS, Vinod PK. Machine learning models to predict the progression from early to late stages of papillary renal cell carcinoma. Comput Biol Med. 2018;100:92–99. doi: https://doi.org/10.1016/j.compbiomed.2018.06.030
- Azuaje F, Kim SY, Perez Hernandez D, et al. Connecting Histopathology Imaging and Proteomics in Kidney Cancer through Machine Learning. J Clin Med. 2019;8(10):1535. doi: https://doi.org/10.3390/jcm8101535
- Shaikhina T, Lowe D, Daga S, et al. Model Decision tree and random forest models for outcome prediction in antibody incompatible kidney transplantation. Biomed Signal Process Control. 2017;52:456–462. doi: https://doi.org/10.1016/j.bspc.2017.01.012
- Erdim C, Yardimci AH, Bektas CT, et al. Prediction of Benign and Malignant Solid Renal Masses: Machine Learning-Based CT Texture Analysis. Acad Radiol. 2020;27(10):1422–1429. doi: https://doi.org/10.1016/j.acra.2019.12.015
- Kocak B, Durmaz ES, Kaya OK, et al. Machine learning-based unenhanced ct texture analysis for predicting BAP1 mutation status of clear cell renal cell carcinomas. Acta Radiol. 2019;61(6):856–864. doi: https://doi.org/10.1177/0284185119881742
- Senan EM, Al-Adhaileh MH, Alsaade FW, et al. Diagnosis of Chronic Kidney Disease Using Effective Classification Algorithms and Recursive Feature Elimination Techniques. J Healthc Eng. 2021;2021:1004767. doi: https://doi.org/10.1155/2021/1004767
- Scanlon LA, O’hara C, Garbett A, et al. Developing an Agnostic Risk Prediction Model for Early Aki Detection in Cancer Patients. Cancers (Basel). 2021;13(16):4182. doi: https://doi.org/10.3390/cancers13164182
- Dagliati A, Marini S, Sacchi L, et al. Machine Learning Methods to Predict Diabetes Complications. J Diabetes Sci Technol. 2018;12(2):295–302. doi: https://doi.org/10.1177/1932296817706375
- Leung RKK, Wang Y, Ma RCW, et al. Using a multi-staged strategy based on machine learning and mathematical modeling to predict genotype-phenotype risk patterns in diabetic kidney disease: a prospective case-control cohort analysis. BMC Nephrol. 2013;14:162. doi: https://doi.org/10.1186/1471-2369-14-162
- Chen CJ, Pai TW, Fujita H, et al. Stage diagnosis for chronic kidney disease based on ultrasonography. In: Proceedings of the International Conference on Fuzzy Systems and Knowledge Discovery (FSKD), Xiamen, China; 2014. P. 525–530.
- Wu B, Mukherjee S. Jain M. A new method using multiphoton imaging and morphometric analysis for differentiating chromophobe renal cell carcinoma and oncocytoma kidney tumors. In: Multiphoton Microscopy in the Biomedical. Sciences XVI; 2016. doi: https://doi.org/10.1117/12.2213681
- Charleonnan A, Fufaung T, Niyomwong T, et al. Predictive analytics for chronic kidney disease using machine learning techniques. In: Proceedings of the Management and Innovation Technology International Conference (MITicon 2016). Bang-Saen, Thailand; 2016. P. MIT80–MIT83. doi: https://doi.org/10.1109/MITICON.2016.8025242
- Konieczny A, Stojanowski J, Krajewska M, et al. Machine Learning in Prediction of IgA Nephropathy Outcome: A Comparative Approach. J Pers Med. 2021;11(4):312. doi: https://doi.org/10.3390/jpm11040312
- Große Hokamp N, Lennartz S, Salem J, et al. Dose independent characterization of renal stones by means of dual energy computed tomography and machine learning: an ex-vivo study. Eur Radiol. 2019;30)3):1397–1404. doi: https://doi.org/10.1007/s00330-019-06455-7
- Niel O, Bastard P, Boussard C, et al. Artificial intelligence outperforms experienced nephrologists to assess dry weight in pediatric patients on chronic hemodialysis. Pediatr Nephrol. 2018;33(10):1799–1803. doi: https://doi.org/10.1007/s00467-018-4015-2
- Kanda E, Epureanu BI, Adachi T, et al. Application of explainable ensemble artificial intelligence model to categorization of hemodialysis-patient and treatment using nationwide-real-world data in Japan. PLoS One. 2020;15(5):e0233491. doi: https://doi.org/10.1371/journal.pone.0233491
- Martínez-Martínez JM, Escandell-Montero P, Barbieri C, et al. Prediction of the hemoglobin level in hemodialysis patients using machine learning techniques. Comput Methods Programs Biomed. 2014;117(2):208–217. doi: https://doi.org/10.1016/j.cmpb.2014.07.001
- Wibawa MS, Maysanjaya IMD, Putra IMAW. Boosted classifier and features selection for enhancing chronic kidney disease diagnose. In: Proceedings of the International Conference on Cyber and IT Service Management (CITSM). Denpasar, Indonesia; 2017. doi: https://doi.org/10.1109/CITSM.2017.8089245
- Tran NK, Sen S, Palmieri TL, et al. Artificial intelligence and machine learning for predicting acute kidney injury in severely burned patients: A proof of concept. Burns. 2019;45(6):1350–1358. doi: https://doi.org/10.1016/j.burns.2019.03.021
- Hayashi Y, Nakajima K, Nakajima K. A rule extraction approach to explore the upper limit of hemoglobin during anemia treatment in patients with predialysis chronic kidney disease. Informatics in Medicine Unlocked. 2019;17:100262. doi: https://doi.org/10.1016/j.imu.2019.100262
- Kocak B, Ates E, Durmaz ES, et al. Influence of Segmentation Margin on Machine Learning-Based High-Dimensional Quantitative CT Texture Analysis: A Reproducibility Study on Renal Clear Cell Carcinomas. Eur Radiol. 2019;29(9):4765–4775. doi: https://doi.org/10.1007/s00330-019-6003-8
- Kunapuli G, Varghese BA, Ganapathy P, et al. A Decision-Support Tool for Renal Mass Classification. J Digit Imaging. 2018;31(6):929–939. doi: https://doi.org/10.1007/s10278-018-0100-0
- Penny-Dimri JC, Bergmeir C, Reid CM, et al. Machine Learning Algorithms for Predicting and Risk Profiling of Cardiac Surgery-Associated Acute Kidney Injury. Semin Thorac Cardiovasc Surg. 2021;33(3):735–745. doi: https://doi.org/10.1053/j.semtcvs.2020.09.028
- Iakovidis DK, Goudas T, Smailis C, et al. Ratsnake: A Versatile Image Annotation Tool with Application to Computer-Aided Diagnosis. ScientificWorldJournal. 2014;2014:286856. doi: https://doi.org/10.1155/2014/286856
- Singh A, Nadkarni G, Gottesman O, et al. Incorporating temporal EHR data in predictive models for risk stratification of renal function deterioration. J Biomed Inform. 2015;53:220–228. doi: https://doi.org/10.1016/j.jbi.2014.11.005
- Lu Y, Jia Z, Zeng X, et al. Renal Biopsy Recommendation Based on Text Understanding. Stud Health Technol Inform. 2019;264:689–693. doi: https://doi.org/10.3233/SHTI190311
- Agar JWM, Webb GI. Application of machine learning to a renal biopsy database. Nephrol Dial Transplant. 1992;7(6):472–478.
- Aljaaf AJ, Al-Jumeily D, Haglan HM, et al. Early prediction of chronic kidney disease using machine learning supported by predictive analytics. In: Proceedings of the 2018 IEEE Congress on Evolutionary Computation (CEC 2018). Rio de Janeiro, Brazil; 2018. doi: https://doi.org/10.1109/CEC.2018.8477876
- Vanaja R, Mukherjee S. Novel Wrapper-Based Feature Selection for Efficient Clinical Decision Support System. Communications in Computer and Information Science; 2018. P. 113–129. doi: https://doi.org/10.1007/978-981-13-3582-2_9
- Rady EHA, Anwar AS. Prediction of Kidney Disease Stages Using Data Mining Algorithms. Informatics in Medicine Unlocked. 2019;15:100178. doi: https://doi.org/10.1016/j.imu.2019.100178