التفاصيل البيبلوغرافية
| العنوان: |
Feasibility of artificial intelligence-based decision supporting system in tolvaptan prescription for autosomal dominant polycystic kidney disease. |
| المؤلفون: |
Jung Hyun Shin, Ye Hyun Kim, Myung Kyu Lee, Hyun-Seok Min, Hyungjoo Cho, Hyunsuk Kim, Yong Chul Kim, Yong Seong Lee, Tae Young Shin |
| المصدر: |
Investigative & Clinical Urology; May2023, Vol. 64 Issue 3, p255-264, 10p |
| مصطلحات موضوعية: |
POLYCYSTIC kidney disease, ARTIFICIAL intelligence, DECISION support systems, IMAGE recognition (Computer vision), MEDICAL communication |
| مستخلص: |
Purpose: Total kidney volume (TKV) measurement is crucial for selecting treatment candidates in autosomal dominant polycystic kidney disease (ADPKD). We developed and investigated the performance of fully-automated 3D-volumetry model and applied it to software as a service (SaaS) for clinical support on tolvaptan prescription in ADPKD patients. Materials and Methods: Computed tomography scans of ADPKD patients taken between January 2000 and June 2022 were acquired from seven institutions. The quality of the images was manually reviewed in advance. The acquired dataset was split into training, validation, and test datasets at a ratio of 8.5:1:0.5. Convolutional, neural network-based automatic segmentation model was trained to obtain 3D segment mask for TKV measurement. The algorithm consisted of three steps: data preprocessing, ADPKD area extraction, and post-processing. After performance validation with the Dice score, 3D-volumetry model was applied to SaaS which is based on Mayo imaging classification for ADPKD. Results: A total of 753 cases with 95,117 slices were included. The differences between the ground-truth ADPKD kidney mask and the predicted ADPKD kidney mask were negligible, with intersection over union >0.95. The post-process filter successfully removed false alarms. The test-set performance was homogeneously equal and the Dice score of the model was 0.971; after post-processing, it improved to 0.979. The SaaS calculated TKV from uploaded Digital Imaging and Communications in Medicine images and classified patients according to height-adjusted TKV for age. Conclusions: Our artificial intelligence-3D volumetry model exhibited effective, feasible, and non-inferior performance compared with that of human experts and successfully predicted the rapid ADPKD progressor. [ABSTRACT FROM AUTHOR] |
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| قاعدة البيانات: |
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