Clinical Implementation of an Artificial Intelligence Algorithm for Magnetic Resonance-Derived Measurement of Total Kidney Volume

• Published in: Mayo Clinic proceedings Vol. 98; no. 5; p. 689
• Main Authors: Potretzke, Theodora A, Korfiatis, Panagiotis, Blezek, Daniel J, Edwards, Marie E, Klug, Jason R, Cook, Cole J, Gregory, Adriana V, Harris, Peter C, Chebib, Fouad T, Hogan, Marie C, Torres, Vicente E, Bolan, Candice W, Sandrasegaran, Kumaresan, Kawashima, Akira, Collins, Jeremy D, Takahashi, Naoki, Hartman, Robert P, Williamson, Eric E, King, Bernard F, Callstrom, Matthew R, Erickson, Bradley J, Kline, Timothy L
• Format: Journal Article
• Language: English
• Published: England 01.05.2023
• Subjects: Adult; Algorithms; Artificial Intelligence; Humans; Kidney - diagnostic imaging; Magnetic Resonance Imaging - methods; Magnetic Resonance Spectroscopy; Polycystic Kidney, Autosomal Dominant - diagnostic imaging
• ISSN: 1942-5546; 0025-6196; 1942-5546
• DOI: 10.1016/j.mayocp.2022.12.019

To evaluate the performance of an internally developed and previously validated artificial intelligence (AI) algorithm for magnetic resonance (MR)-derived total kidney volume (TKV) in autosomal dominant polycystic kidney disease (ADPKD) when implemented in clinical practice. The study included adult patients with ADPKD seen by a nephrologist at our institution between November 2019 and January 2021 and undergoing an MR imaging examination as part of standard clinical care. Thirty-three nephrologists ordered MR imaging, requesting AI-based TKV calculation for 170 cases in these 161 unique patients. We tracked implementation and performance of the algorithm over 1 year. A radiologist and a radiology technologist reviewed all cases (N=170) for quality and accuracy. Manual editing of algorithm output occurred at radiology or radiology technologist discretion. Performance was assessed by comparing AI-based and manually edited segmentations via measures of similarity and dissimilarity to ensure expected performance. We analyzed ADPKD severity class assignment of algorithm-derived vs manually edited TKV to assess impact. Clinical implementation was successful. Artificial intelligence algorithm-based segmentation showed high levels of agreement and was noninferior to interobserver variability and other methods for determining TKV. Of manually edited cases (n=84), the AI-algorithm TKV output showed a small mean volume difference of -3.3%. Agreement for disease class between AI-based and manually edited segmentation was high (five cases differed). Performance of an AI algorithm in real-life clinical practice can be preserved if there is careful development and validation and if the implementation environment closely matches the development conditions.