Papers
3
Total Citations
24
H-Index
3
About
Zhiguo Zhao’s research bridges two seemingly distinct worlds: the clinical assessment of prostate cancer treatment outcomes and the agricultural application of computer vision. In urologic oncology, Zhao has made significant contributions to understanding how patient-reported quality-of-life measures translate into real-world function. His work on the EPIC-26 questionnaire, cited 13 times, clarifies the often-murky relationship between domain scores and functional outcomes after prostate cancer treatment, helping patients and clinicians set realistic expectations. Zhao also co-authored a pivotal study from the CEASAR cohort, with 7 citations, that critically evaluated quality measures for prostate cancer surgery, revealing that nationally endorsed metrics often fail to align with actual patient outcomes—a finding with direct implications for surgical quality improvement. Demonstrating remarkable versatility, Zhao’s most recent work (2024, 4 citations) introduces YOLOv7-Branch, a deep learning model designed for agricultural robots to detect jujube leaf branches. This innovation addresses a key bottleneck in intelligent harvesting for jujube leaf tea, showcasing Zhao’s ability to apply cutting-edge AI to precision agriculture. This cross-disciplinary portfolio—from patient-centered outcomes to robotic perception—marks Zhao as a researcher who tackles complex, real-world problems across medicine and engineering.
Research Focus
Key Achievements
Top Papers
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