output
20162026
most citedSurgical Data Science: Enabling Next-Generation Surgery

578 citations

Showing 2025Show all

5 papers · 1 filter

cs.CV2025

Endoshare: A Publicly Available, Surgeons-Friendly Solution to De-Identify and Manage Surgical Videos

Lorenzo Arboit, Dennis N. Schneider, Britty Baby +3

Video-based assessment and surgical data science can advance surgical training, research, and quality improvement, yet adoption remains limited by heterogeneous recording formats a…

cs.CV2025

Federated Learning for Surgical Vision in Appendicitis Classification: Results of the FedSurg EndoVis 2024 Challenge

Max Kirchner, Hanna Hoffmann, Alexander C. Jenke +16

Developing generalizable surgical AI requires multi-institutional data, yet privacy constraints preclude direct data sharing, making Federated Learning (FL) a natural candidate. It…

cs.CY2025★ 14 cited

Surgeons Awareness, Expectations, and Involvement with Artificial Intelligence: a Survey Pre and Post the GPT Era

Lorenzo Arboit, Dennis N. Schneider, Toby Collins +7

Artificial Intelligence (AI) is transforming medicine, with generative AI models like ChatGPT reshaping perceptions of its potential. This study examines surgeons' awareness, expec…

cs.CV2025

S4M: 4-points to Segment Anything

Adrien Meyer, Lorenzo Arboit, Giuseppe Massimiani +3

Purpose: The Segment Anything Model (SAM) promises to ease the annotation bottleneck in medical segmentation, but overlapping anatomy and blurred boundaries make its point prompts…

cs.CV2025★ 6 cited

Early Operative Difficulty Assessment in Laparoscopic Cholecystectomy via Snapshot-Centric Video Analysis

Saurav Sharma, Maria Vannucci, Leonardo Pestana Legori +6

Purpose: Laparoscopic cholecystectomy (LC) operative difficulty (LCOD) is highly variable and influences outcomes. Despite extensive LC studies in surgical workflow analysis, limit…