MP09-07 DEVELOPING AN ARTIFICIAL INTELLIGENCE MODEL TO PREDICT DIFFERENTIAL RENAL FUNCTION USING CONTRAST-ENHANCED CT SCANS

Abstract

You have accessJournal of UrologyCME1 Apr 2023MP09-07 DEVELOPING AN ARTIFICIAL INTELLIGENCE MODEL TO PREDICT DIFFERENTIAL RENAL FUNCTION USING CONTRAST-ENHANCED CT SCANS Ramon Correa Medero, Daniel Salevitz, Jiwoong Jeong, Bhavik Patel, Imon Banerjee, and Haidar Abdul-Muhsin Ramon Correa MederoRamon Correa Medero More articles by this author , Daniel SalevitzDaniel Salevitz More articles by this author , Jiwoong JeongJiwoong Jeong More articles by this author , Bhavik PatelBhavik Patel More articles by this author , Imon BanerjeeImon Banerjee More articles by this author , and Haidar Abdul-MuhsinHaidar Abdul-Muhsin More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000003224.07AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: Differential kidney function plays an important role in the management of kidney disorders. We sought to assess if this can be obtained through an automated evaluation of contrast enhanced computed tomography using a combination of deep learning and machine learning algorithms without nuclear medical imaging. METHODS: All patients who underwent kidney nuclear scanning at Mayo Clinic between 2018-2022 were collected. Computerized tomographic (CT) scans of the kidneys that were obtained within a three-month interval before or after the nuclear scans were extracted. Patients who underwent a urological or radiological intervention within this time frame were excluded. A deep learning segmentation model was used to automatically segment kidneys (Figure 1). 2D and 3D radiomics features were extracted and compared between the two kidneys to compute delta radiomics and assess its ability to predict differential kidney function. The performance of these models was reported using receiver operating characteristics, precision, recall, and F1 score. RESULTS: There were 4118 nuclear medical scans. We randomly selected 303 CT scans for review. Following exclusion, there were 203 paired studies (Figure 2). The fine-tuned segmentation model demonstrated a Sorensen-Dice score of 0.7. The best results were obtained when the support vector classifier model was used on 3D delta radiomics features computed from the venous phase of contrast-enhanced CT scans. This model has an AUC of 0.90 with a precision, recall, and F1 score of 0.80, 0.80, and 0.82 respectively, to detect abnormal kidney function. CONCLUSIONS: This proposed automated pipeline can provide important differential kidney function information from contrast-enhanced cross section imaging without the need for dedicated nuclear scans. Source of Funding: None © 2023 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 209Issue Supplement 4April 2023Page: e106 Advertisement Copyright & Permissions© 2023 by American Urological Association Education and Research, Inc.MetricsAuthor Information Ramon Correa Medero More articles by this author Daniel Salevitz More articles by this author Jiwoong Jeong More articles by this author Bhavik Patel More articles by this author Imon Banerjee More articles by this author Haidar Abdul-Muhsin More articles by this author Expand All Advertisement PDF downloadLoading ...