Janet Zhen

Johnson & Johnson (United States)

Papers

2

Total Citations

8

H-Index

2

About

Dr. Janet Zhen is a rising leader in urologic surgery and medical robotics, whose research focuses on improving the safety and precision of percutaneous nephrolithotomy (PCNL) for large kidney stones. Her most cited work, “Deep Learning for Detection of Clinical Operations in Robot-Assisted Percutaneous Renal Access” (2023, 5 citations), pioneers the use of artificial intelligence to automate and enhance surgical decision-making during robotic renal access, a critical step in stone removal. Dr. Zhen’s contributions extend to reducing patient risk: her cadaveric study, “Robotic-Assisted Electromagnetic Guidance Minimizes Radiation Exposure in Gaining Percutaneous Access for Nephrolithotomy” (2022, 3 citations), demonstrated that novice surgeons using robotic electromagnetic guidance achieve comparable access success with significantly less radiation than traditional fluoroscopic methods. This work, presented at the Journal of Urology and co-authored with leading experts like Mitchell Humphreys and Ben Chew, highlights her commitment to translating engineering solutions into clinical practice. With a growing citation record and a focus on deep learning and image-guided intervention, Dr. Zhen is shaping the next generation of minimally invasive urologic surgery, making procedures safer for patients and more accessible for trainees.

Research Focus

Key Achievements

2
H-Index
2
Papers
8
Total Citations
4
Avg Citations/Paper
🏆 Most Cited Paper
Deep Learning for Detection of Clinical Operations in Robot-Assisted Percutaneous Renal Access
5 citations · 2023
📈 Most Prolific Year: 2023 (1 Papers)
🤝 Key Collaborators: 9
🏛 Institutions: Johnson & Johnson (United States)

Top Papers

  1. 1
  2. 2

Key Collaborators

Contact & Links

Available for collaboration
Content generated · 12 days ago