TY - CHAP AU - Mach, Berta AU - Levendovics, Renáta AU - Haidegger, Tamás ED - Szakál, Anikó TI - Recent Approaches in Laparoscopic Training Phantom Development—a Review T2 - SISY 2023 IEEE 21st International Symposium on Intelligent Systems and Informatics PB - IEEE Hungary Section CY - Budapest SN - 9798350343366 PY - 2023 SP - 295 EP - 301 PG - 7 DO - 10.1109/SISY60376.2023.10417939 UR - https://m2.mtmt.hu/api/publication/34151271 ID - 34151271 LA - English DB - MTMT ER - TY - JOUR AU - Levendovics, Renáta AU - Haidegger, Tamás TI - Next in Surgical Data Science: Autonomous Non-Technical Skill Assessment in Minimally Invasive Surgery Training JF - JOURNAL OF CLINICAL MEDICINE J2 - J CLIN MED VL - 11 PY - 2022 IS - 24 PG - 16 SN - 2077-0383 DO - 10.3390/jcm11247533 UR - https://m2.mtmt.hu/api/publication/33547343 ID - 33547343 AB - Background: It is well understood that surgical skills largely define patient outcomes both in Minimally Invasive Surgery (MIS) and Robot-Assisted MIS (RAMIS). Non-technical surgical skills, including stress and distraction resilience, decision-making and situation awareness also contribute significantly. Autonomous, technologically supported objective skill assessment can be efficient tools to improve patient outcomes without the need to involve expert surgeon reviewers. However, autonomous non-technical skill assessments are unstandardized and open for more research. Recently, Surgical Data Science (SDS) has become able to improve the quality of interventional healthcare with big data and data processing techniques (capture, organization, analysis and modeling of data). SDS techniques can also help to achieve autonomous non-technical surgical skill assessments. Methods: An MIS training experiment is introduced to autonomously assess non-technical skills and to analyse the workload based on sensory data (video image and force) and a self-rating questionnaire (SURG-TLX). A sensorized surgical skill training phantom and adjacent training workflow were designed to simulate a complicated Laparoscopic Cholecystectomy task; the dissection of the cholecyst’s peritonial layer and the safe clip application on the cystic artery in an uncomfortable environment. A total of 20 training sessions were recorded from 7 subjects (3 non-medicals, 2 residents, 1 expert surgeon and 1 expert MIS surgeon). Workload and learning curves were studied via SURG-TLX. For autonomous non-technical skill assessment, video image data with tracked instruments based on Channel and Spatial Reliability Tracker (CSRT) and force data were utilized. An autonomous time series classification was achieved by a Fully Convolutional Neural Network (FCN), where the class labels were provided by SURG-TLX. Results: With unpaired t-tests, significant differences were found between the two groups (medical professionals and control) in certain workload components (mental demands, physical demands, and situational stress, p<0.0001, 95% confidence interval, p<0.05 for task complexity). With paired t-tests, the learning curves of the trials were also studied; the task complexity resulted in a significant difference between the first and the second trials. Autonomous non-technical skill classification was based on the FCN by applying the tool trajectories and force data as input. This resulted in a high accuracy (85%) on temporal demands classification based on the z component of the used forces and 75% accuracy for classifying mental demands/situational stress with the x component of the used forces validated with Leave One Out Cross-Validation. Conclusions: Non-technical skills and workload components can be classified autonomously based on measured training data. SDS can be effective via automated non-technical skill assessment. LA - English DB - MTMT ER - TY - CHAP AU - Levendovics, Renáta AU - Haidegger, Tamás ED - Szakál, Anikó TI - Towards Autonomous Endoscopic Image-Based Surgical Skill Assessment: Articulated Tool Pose Estimation T2 - IEEE 10th Jubilee International Conference on Computational Cybernetics and Cyber-Medical Systems ICCC 2022 PB - IEEE Hungary Section CY - Budapest SN - 9781665481762 PY - 2022 SP - 35 EP - 42 PG - 7 UR - https://m2.mtmt.hu/api/publication/32854225 ID - 32854225 LA - English DB - MTMT ER - TY - CHAP AU - Papp, Dóra AU - Levendovics, Renáta AU - Haidegger, Tamás ED - Szakál, Anikó TI - Surgical Tool Segmentation on the JIGSAWS Dataset for Autonomous Image-Based Skill Assessment T2 - IEEE 10th Jubilee International Conference on Computational Cybernetics and Cyber-Medical Systems ICCC 2022 PB - IEEE Hungary Section CY - Budapest SN - 9781665481762 PY - 2022 SP - 49 EP - 56 PG - 7 UR - https://m2.mtmt.hu/api/publication/32854249 ID - 32854249 LA - English DB - MTMT ER - TY - CHAP AU - Levendovics, Renáta AU - Berta, Mach AU - Móga, Kristóf János AU - Alexander, Ládi AU - Haidegger, Tamás ED - Szakál, Anikó TI - Autonomous Non-Technical Surgical Skill Assessment and Workload Analysis in Laparoscopic Cholecystectomy Training T2 - 2022 IEEE 26th International Conference on Intelligent Engineering Systems (INES 2022) PB - IEEE Hungary Section CY - Budapest SN - 9781665492089 PY - 2022 SP - 33 EP - 39 PG - 7 DO - 10.1109/INES56734.2022.9922657 UR - https://m2.mtmt.hu/api/publication/33051165 ID - 33051165 LA - English DB - MTMT ER - TY - CHAP AU - Takács, Kristóf AU - Levendovics, Renáta AU - Haidegger, Tamás ED - Szakál, Anikó TI - Image Processing-Based Methods to Improve the Robustness of Robotic Gripping T2 - IEEE Joint 22nd International Symposium on COMPUTATIONAL INTELLIGENCE and INFORMATICS and 8th International Conference on Recent Achievements in Mechatronics, Automation, Computer Science and Robotics (CINTI-MACRo 2022) PB - IEEE Hungary Section CY - Budapest SN - 9798350398823 PY - 2022 SP - 345 EP - 350 PG - 6 DO - 10.1109/CINTI-MACRo57952.2022.10029473 UR - https://m2.mtmt.hu/api/publication/33261879 ID - 33261879 N1 - Óbuda Universtiy, Antal Bejczy Center for Intelligent Robotics, Budapest, Hungary Óbuda University, Doctoral School of Applied Informatics and Applied Mathematics, Budapest, Hungary Óbuda University, John von Neumann Faculty of Informatics, Budapest, Hungary University Research and Innovation Center (EKIK), Óbuda University, Budapest, Hungary Austrian Center for Medical Innovation and Technology, Wiener Neustadt, Austria Conference code: 186564 Export Date: 18 January 2024 Correspondence Address: Takacs, K.; Óbuda Universtiy, Hungary Funding details: Horizon 2020 Framework Programme, H2020, 871631 Funding details: Óbudai Egyetem Funding details: Alkalmazott Informatikai és Alkalmazott Matematikai Doktori Iskola, Óbudai Egyetem, AIAMDI Funding text 1: This work has received funding from the European Union s Horizon 2020 research and innovation programme under grant agreement No 871631, RoBUTCHER (A Robust, Flexible and Scalable Cognitive Robotics Platform). Kristof Takacs acknowledges the financial support of O buda University, Budapest, Hungary Doctoral School of Applied Informatics and Applied Mathematics. Funding text 2: Kristóf Takács acknowledges the financial support of Óbuda University, Budapest, Hungary – Doctoral School of Applied Informatics and Applied Mathematics. Funding text 3: V. ACKNOWLEDGMENT This work has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 871631, RoBUTCHER (A Robust, Flexible and Scalable Cognitive Robotics Platform). LA - English DB - MTMT ER - TY - CHAP AU - Károly, István Artúr AU - Levendovics, Renáta AU - Haidegger, Tamás AU - Galambos, Péter ED - IEEE, , TI - Moving Obstacle Segmentation with an Optical Flow-based DNN: an Implementation Case Study T2 - 2021 IEEE 25th International Conference on Intelligent Engineering Systems (INES) PB - IEEE CY - New York, New York SN - 9781665444996 PY - 2021 SP - 000189 EP - 000194 PG - 6 DO - 10.1109/INES52918.2021.9512898 UR - https://m2.mtmt.hu/api/publication/32154559 ID - 32154559 LA - English DB - MTMT ER - TY - JOUR AU - Lajkó, Gábor AU - Levendovics, Renáta AU - Haidegger, Tamás TI - Endoscopic Image-Based Skill Assessment in Robot-Assisted Minimally Invasive Surgery JF - SENSORS J2 - SENSORS-BASEL VL - 21 PY - 2021 IS - 16 PG - 24 SN - 1424-8220 DO - 10.3390/s21165412 UR - https://m2.mtmt.hu/api/publication/32154807 ID - 32154807 N1 - EIT Digital Master School, Autonomous Systems Track, Double Degree Programme at the Technische Universität Berlin, Straße des 17. Juni 135, Berlin, 10623, Germany Computer Science for Autonomous Driving, Eötvös Loránd University, Egyetem tér 1-3, Budapest, 1053, Hungary Antal Bejczy Center for Intelligent Robotics, University Research and Innovation Center, Óbuda University, Budapest, 1034, Hungary Doctoral School of Applied Informatics and Applied Mathematics, Óbuda University, Bécsi út 96/b, Budapest, 1034, Hungary John von Neumann Faculty of Informatics, Óbuda University, Bécsi út 96/b, Budapest, 1034, Hungary Austrian Center for Medical Innovation and Technology, Viktor Kaplan-Straße 2/1, Wiener Neustadt, 2700, Austria Cited By :1 Export Date: 27 May 2022 Correspondence Address: Elek, R.N.; Antal Bejczy Center for Intelligent Robotics, Hungary; email: renata.elek@irob.uni-obuda.hu LA - English DB - MTMT ER - TY - CHAP AU - Lajkó, Gábor AU - Levendovics, Renáta AU - Haidegger, Tamás ED - IEEE, , TI - Surgical Skill Assessment Automation Based on Sparse Optical Flow Data T2 - 2021 IEEE 25th International Conference on Intelligent Engineering Systems (INES) PB - IEEE CY - New York, New York SN - 9781665444996 PY - 2021 SP - 1 PG - 7 DO - 10.1109/INES52918.2021.9512917 UR - https://m2.mtmt.hu/api/publication/32104177 ID - 32104177 N1 - Technische Universität Berlin, Eit Digital Master School Autonomous Systems Track, Berlin, Germany Óbuda University, Antal Bejczy Center for Intelligent Robotics (IROB), Ekik Doctoral School of Applied Informatics John von Neumann Faculty of Informatics, Budapest, Hungary Export Date: 27 May 2022 LA - English DB - MTMT ER - TY - JOUR AU - Levendovics, Renáta AU - Haidegger, Tamás TI - Non-Technical Skill Assessment and Mental Load Evaluation in Robot-Assisted Minimally Invasive Surgery JF - SENSORS J2 - SENSORS-BASEL VL - 21 PY - 2021 IS - 8 PG - 23 SN - 1424-8220 DO - 10.3390/s21082666 UR - https://m2.mtmt.hu/api/publication/32009471 ID - 32009471 N1 - Funding Agency and Grant Number: European UnionEuropean Commission [EFOP-3.6.1-16-2016-00010]; New National Excellence Program of the Ministry of Human Capacities Funding text: Authors thankfully acknowledge the financial support of this work by the Hungarian State and the European Union under the EFOP-3.6.1-16-2016-00010 project. T. Haidegger and R. Nagyne Elek are supported through the New National Excellence Program of the Ministry of Human Capacities. T. Haidegger is a Bolyai Fellow of the Hungarian Academy of Sciences. LA - English DB - MTMT ER -