Research-based clinical deployment of artificial intelligence algorithm for prostate MRI

作者：Stephanie A. Harmon, Jesse Tetreault, Ömer Tarık Esengür, Ming Qin, Enis C. Yılmaz, Victor Chang, Dong Yang, Ziyue Xu, Gregg Cohen, Jeff Plum, Testi Sherif, R.J. Levin, Alexander Schmidt-Richberg, Scott M. Thompson, Samuel Coons, Te Chen, Peter L. Choyke, Daguang Xu, Sandeep Gurram, Bradford J. Wood, Peter A. Pinto, Baris Turkbey · 发表于：Abdominal Radiology · 年份：2025 · DOI：10.1007/s00261-025-05014-7 · 被引用次数：7 · 研究领域：Prostate Cancer Diagnosis and Treatment、Artificial Intelligence in Healthcare and Education、AI in cancer detection

PURPOSE: A critical limitation to deployment and utilization of Artificial Intelligence (AI) algorithms in radiology practice is the actual integration of algorithms directly into the clinical Picture Archiving and Communications Systems (PACS). Here, we sought to integrate an AI-based pipeline for prostate organ and intraprostatic lesion segmentation within a clinical PACS environment to enable point-of-care utilization under a prospective clinical trial scenario. METHODS: A previously trained, publicly available AI model for segmentation of intra-prostatic findings on multiparametric Magnetic Resonance Imaging (mpMRI) was converted into a containerized environment compatible with MONAI Deploy Express. An inference server and dedicated clinical PACS workflow were established within our institution for evaluation of real-time use of the AI algorithm. PACS-based deployment was prospectively evaluated in two phases: first, a consecutive cohort of patients undergoing diagnostic imaging at our institution and second, a consecutive cohort of patients undergoing biopsy based on mpMRI findings. The AI pipeline was executed from within the PACS environment by the radiologist. AI findings were imported into clinical biopsy planning software for target definition. Metrics analyzing deployment success, timing, and detection performance were recorded and summarized. RESULTS: In phase one, clinical PACS deployment was successfully executed in 57/58 cases and were obtained within one minut...