Amr Tashtoush
Papers
1
Total Citations
4
H-Index
1
About
Amr Tashtoush is a researcher at the forefront of applying deep learning to minimally invasive surgery, with a primary focus on real-time object segmentation and detection in laparoscopic procedures. His most impactful work, "Real-time object segmentation for laparoscopic cholecystectomy using YOLOv8," demonstrates a novel integration of the YOLOv8 architecture to achieve precise, high-speed identification of anatomical structures during gallbladder removal surgery. This contribution is critical for enhancing surgical precision and reducing operative risks, earning 4 citations since its 2024 publication. Tashtoush’s research bridges the gap between computer vision and clinical practice, offering a scalable solution for intraoperative assistance. His work not only advances the field of surgical robotics but also underscores the potential of lightweight neural networks for resource-constrained medical environments. By tackling the challenge of real-time segmentation in dynamic surgical scenes, Tashtoush is helping to pave the way for smarter, safer operating rooms—a vital step toward the future of AI-assisted healthcare.
Research Focus
Key Achievements
Top Papers
- 1