@inproceedings{MTMT:34151271,
	title = {Recent Approaches in Laparoscopic Training Phantom Development—a Review},
	url = {https://m2.mtmt.hu/api/publication/34151271},
	author = {Mach, Berta and Levendovics, Renáta and Haidegger, Tamás},
	booktitle = {SISY 2023 IEEE 21st International Symposium on Intelligent Systems and Informatics},
	doi = {10.1109/SISY60376.2023.10417939},
	unique-id = {34151271},
	year = {2023},
	pages = {295-301},
	orcid-numbers = {Haidegger, Tamás/0000-0003-1402-1139}
}

@article{MTMT:33547343,
	title = {Next in Surgical Data Science: Autonomous Non-Technical Skill Assessment in Minimally Invasive Surgery Training},
	url = {https://m2.mtmt.hu/api/publication/33547343},
	author = {Levendovics, Renáta and Haidegger, Tamás},
	doi = {10.3390/jcm11247533},
	journal-iso = {J CLIN MED},
	journal = {JOURNAL OF CLINICAL MEDICINE},
	volume = {11},
	unique-id = {33547343},
	abstract = {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.},
	year = {2022},
	eissn = {2077-0383},
	orcid-numbers = {Haidegger, Tamás/0000-0003-1402-1139}
}

@inproceedings{MTMT:32854225,
	title = {Towards Autonomous Endoscopic Image-Based Surgical Skill Assessment: Articulated Tool Pose Estimation},
	url = {https://m2.mtmt.hu/api/publication/32854225},
	author = {Levendovics, Renáta and Haidegger, Tamás},
	booktitle = {IEEE 10th Jubilee International Conference on Computational Cybernetics and Cyber-Medical Systems ICCC 2022},
	unique-id = {32854225},
	year = {2022},
	pages = {35-42},
	orcid-numbers = {Haidegger, Tamás/0000-0003-1402-1139}
}

@inproceedings{MTMT:32854249,
	title = {Surgical Tool Segmentation on the JIGSAWS Dataset for Autonomous Image-Based Skill Assessment},
	url = {https://m2.mtmt.hu/api/publication/32854249},
	author = {Papp, Dóra and Levendovics, Renáta and Haidegger, Tamás},
	booktitle = {IEEE 10th Jubilee International Conference on Computational Cybernetics and Cyber-Medical Systems ICCC 2022},
	unique-id = {32854249},
	year = {2022},
	pages = {49-56},
	orcid-numbers = {Haidegger, Tamás/0000-0003-1402-1139}
}

@inproceedings{MTMT:33051165,
	title = {Autonomous Non-Technical Surgical Skill Assessment and Workload Analysis in Laparoscopic Cholecystectomy Training},
	url = {https://m2.mtmt.hu/api/publication/33051165},
	author = {Levendovics, Renáta and Berta, Mach and Móga, Kristóf János and Alexander, Ládi and Haidegger, Tamás},
	booktitle = {2022 IEEE 26th International Conference on Intelligent Engineering Systems (INES 2022)},
	doi = {10.1109/INES56734.2022.9922657},
	unique-id = {33051165},
	year = {2022},
	pages = {33-39},
	orcid-numbers = {Haidegger, Tamás/0000-0003-1402-1139}
}

@inproceedings{MTMT:33261879,
	title = {Image Processing-Based Methods to Improve the Robustness of Robotic Gripping},
	url = {https://m2.mtmt.hu/api/publication/33261879},
	author = {Takács, Kristóf and Levendovics, Renáta and Haidegger, Tamás},
	booktitle = {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)},
	doi = {10.1109/CINTI-MACRo57952.2022.10029473},
	unique-id = {33261879},
	year = {2022},
	pages = {345-350},
	orcid-numbers = {Haidegger, Tamás/0000-0003-1402-1139}
}

@inproceedings{MTMT:32154559,
	title = {Moving Obstacle Segmentation with an Optical Flow-based DNN: an Implementation Case Study},
	url = {https://m2.mtmt.hu/api/publication/32154559},
	author = {Károly, István Artúr and Levendovics, Renáta and Haidegger, Tamás and Galambos, Péter},
	booktitle = {2021 IEEE 25th International Conference on Intelligent Engineering Systems (INES)},
	doi = {10.1109/INES52918.2021.9512898},
	unique-id = {32154559},
	year = {2021},
	pages = {000189-000194},
	orcid-numbers = {Haidegger, Tamás/0000-0003-1402-1139; Galambos, Péter/0000-0002-2319-0551}
}

@article{MTMT:32154807,
	title = {Endoscopic Image-Based Skill Assessment in Robot-Assisted Minimally Invasive Surgery},
	url = {https://m2.mtmt.hu/api/publication/32154807},
	author = {Lajkó, Gábor and Levendovics, Renáta and Haidegger, Tamás},
	doi = {10.3390/s21165412},
	journal-iso = {SENSORS-BASEL},
	journal = {SENSORS},
	volume = {21},
	unique-id = {32154807},
	year = {2021},
	eissn = {1424-8220},
	orcid-numbers = {Lajkó, Gábor/0000-0002-0003-0408; Haidegger, Tamás/0000-0003-1402-1139}
}

@inproceedings{MTMT:32104177,
	title = {Surgical Skill Assessment Automation Based on Sparse Optical Flow Data},
	url = {https://m2.mtmt.hu/api/publication/32104177},
	author = {Lajkó, Gábor and Levendovics, Renáta and Haidegger, Tamás},
	booktitle = {2021 IEEE 25th International Conference on Intelligent Engineering Systems (INES)},
	doi = {10.1109/INES52918.2021.9512917},
	unique-id = {32104177},
	year = {2021},
	pages = {1-8},
	orcid-numbers = {Haidegger, Tamás/0000-0003-1402-1139}
}

@article{MTMT:32009471,
	title = {Non-Technical Skill Assessment and Mental Load Evaluation in Robot-Assisted Minimally Invasive Surgery},
	url = {https://m2.mtmt.hu/api/publication/32009471},
	author = {Levendovics, Renáta and Haidegger, Tamás},
	doi = {10.3390/s21082666},
	journal-iso = {SENSORS-BASEL},
	journal = {SENSORS},
	volume = {21},
	unique-id = {32009471},
	year = {2021},
	eissn = {1424-8220},
	orcid-numbers = {Levendovics, Renáta/0000-0002-3030-254X; Haidegger, Tamás/0000-0003-1402-1139}
}