Publications by authors named "A Kisilenko"
Background: Laparoscopic cholecystectomy is a very frequent surgical procedure. However, in an ageing society, less surgical staff will need to perform surgery on patients. Collaborative surgical robots (cobots) could address surgical staff shortages and workload.
View Article and Find Full Text PDFA sensorized modular training platform to reduce vascular damage in endovascular surgery.
Int J Comput Assist Radiol Surg
September 2023
Purpose: Endovascular interventions require intense practice to develop sufficient dexterity in catheter handling within the human body. Therefore, we present a modular training platform, featuring 3D-printed vessel phantoms with patient-specific anatomy and integrated piezoresistive impact force sensing of instrument interaction at clinically relevant locations for feedback-based skill training to detect and reduce damage to the delicate vascular wall.
Methods: The platform was fabricated and then evaluated in a user study by medical ([Formula: see text]) and non-medical ([Formula: see text]) users.
Background: Laparoscopic videos are increasingly being used for surgical artificial intelligence (AI) and big data analysis. The purpose of this study was to ensure data privacy in video recordings of laparoscopic surgery by censoring extraabdominal parts. An inside-outside-discrimination algorithm (IODA) was developed to ensure privacy protection while maximizing the remaining video data.
View Article and Find Full Text PDFPurpose: Surgical workflow and skill analysis are key technologies for the next generation of cognitive surgical assistance systems. These systems could increase the safety of the operation through context-sensitive warnings and semi-autonomous robotic assistance or improve training of surgeons via data-driven feedback. In surgical workflow analysis up to 91% average precision has been reported for phase recognition on an open data single-center video dataset.
View Article and Find Full Text PDFArticle Synopsis
- Image-based tracking of medical instruments is crucial for enhancing surgical data science, but existing methods struggle with difficult images and lack generalizability.
- The Heidelberg Colorectal (HeiCo) dataset is introduced as the first publicly available resource for testing detection and segmentation algorithms, focusing on robustness and adaptability.
- This dataset features 30 laparoscopic videos, sensor data, and detailed annotations for over 10,000 frames, aiding in organizing global competitions like the Endoscopic Vision Challenges.