Surgical data science, an emerging field of medicine

Nagy, Dénes Ákos; Rudas, Imre J.; Haidegger, Tamás

doi:10.1109/nc.2017.8263251

Cited by 9 publications

(2 citation statements)

References 34 publications

(34 reference statements)

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“…Real-time integration with external data sources (surgical robot, patient monitor etc.) in a longer term could enable the system to automatically recognize workflow steps, and eventually to create decision support systems, and context aware automation [21].…”

Section: Resultsmentioning

confidence: 99%

OntoFlow, a software tool for surgical workflow recording

Nagy

Rudas

Haidegger

2018

2018 IEEE 16th World Symposium on Applied Machine Intelligence and Informatics (SAMI)

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Surgical Process Modeling is a growing field of biomedical data science, aiming to create and support context aware surgical systems. As a part of it, novel research intends to provide standardized, formal description of surgical processes. Surgical workflow recordings based on ontologies can provide objective measurements of surgical skill, thus standardizing surgical performance. Comparing the operational phase to the calculated optimal process could allow for new, context aware surgical training, evaluation and assistant systems. In this paper, we present a new software tool, named OntoFlow, developed to record ontology-based surgical workflow during the clinical practice, with post-event editing and reviewing capabilities. OntoFlow directly accesses the background ontology, therefore it can speed up the process of ontology development. As a surgical workflow reviewing software it can also be used as a training tool for surgical residents.

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Section: Resultsmentioning

confidence: 99%

OntoFlow, a software tool for surgical workflow recording

Nagy

Rudas

Haidegger

2018

2018 IEEE 16th World Symposium on Applied Machine Intelligence and Informatics (SAMI)

Self Cite

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“…Within all these domains, data collection at large scale became possible with the introduction of CIS systems, also as an enabler of Machine Learning methods [10]. This opened the case for a completely new field, dubbed Surgical Data Science [11], which really enables the assessment and benchmarking of CIS systems based on surgical process models [12].…”

Section: Computer-integrated Surgery: An Emerging Fieldmentioning

confidence: 99%

Probabilistic Method to Improve the Accuracy of Computer-Integrated Surgical Systems

2019

APH

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The technological development of the last decades resulted in the rise of entirely new paradigms in healthcare. Computer-Integrated Surgery (CIS) is providing innovative, minimally invasive solutions to heal complex injuries and diseases. It integrates robotic devices to the treatment delivery phase. By now, well over 6 million successful operations have been accomplished with various systems. In certain critical surgical procedures, where spatial accuracy is a must, physicians extensively rely on the help of CIS, and particularly on intra-operative navigation system. For these, the ways of use, including setup, registration and application accuracy metrics are provided by the manufacturers. Depending on the setup, inherent system errors can accumulate, and lead to significant deviation in position measurements. It is crucial to improve the precision of integrated setups, and to determine the overall task execution error. The stochastic approach proposed here offers an easy and straightforward solution to map and scale the error propagation. Applying pre-operative and on-site simulations, the optimal positioning of the navigation system can be achieved. This results in faster task execution and reduction of the probability of surgical errors. Surgical tracking systems have broader applications in endoscopic surgeries, and the method described in the article can be directly applied to these procedures too. It was tested in silico and on a neurosurgical prototype robot system developed at the Johns Hopkins University. The proposed features together can greatly increase the safety and reliability of all procedures where camera systems are involved, and ease the surgeon's task and potentially reduce operating time.

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Emerging Technologies: Data and the Future of Surgery

Hachach‐Haram¹,

Karim²

2022

Health Informatics

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Surgical data science, an emerging field of medicine

Cited by 9 publications

References 34 publications

OntoFlow, a software tool for surgical workflow recording

OntoFlow, a software tool for surgical workflow recording

Probabilistic Method to Improve the Accuracy of Computer-Integrated Surgical Systems

Emerging Technologies: Data and the Future of Surgery

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