| Autor: |
Kim, Youngwoo, Tao, Cheng, Kim, Hyungchan, Oh, Geum-Yoon, Ko, Jeongbeom, Bae, Kyongtae T. |
| Zdroj: |
Journal of the American Society of Nephrology; August 2022, Vol. 33 Issue: 8 p1581-1589, 9p |
| Abstrakt: |
Total kidney volume (TKV) is the most important biomarker of disease severity and progression for autosomal dominant polycystic kidney disease (ADPKD) but determining volumes of kidney and exophytic cysts from magnetic resonance images is a labor-intensive and complex process involving manual tracing of boundaries of kidneys slice by slice. In patients with prominent exophytic cysts, computation of TKV should exclude such cysts to avoid overestimating the disease progression risk profile. The authors developed and validated a deep learning?based fully automated method of computing TKV that excludes exophytic cyst volumes. Their findings indicate that the automated method?s performance is equivalent to the reference standard of manual tracing. This advanced technique shows promise for rapid and reliable assessment of TKV to help estimate ADPKD disease progression and treatment response. |
| Databáze: |
Supplemental Index |
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