Knowledge-based automated planning system for StereoElectroEncephaloGraphy: A center-based scenario.

Journal of Biomedical Informatics
Davide ScorzaFrancesco Cardinale

Abstract

Surgical planning for StereoElectroEncephaloGraphy (SEEG) is a complex and patient specific task, where the experience and medical workflow of each institution may influence the final planning choices. To account for this variability, we developed a data-based Computer Assisted Planning (CAP) solution able to exploit the knowledge extracted by past cases. By the analysis of retrospective patients' data sets, our system proposes a pool of trajectories commonly used by the institution, which can be selected to initialize a new patient plan. An optimization framework adapts those to the patient's anatomy by optimizing clinical requirements (e.g. distance from vessel, gray matter recording and insertion angle), and adapting its strategy based on the trajectory type selected.The system has been customized based on the data of a single institution. Two neurosurgeons, working in a high-volume hospital, have validated it by using 15 retrospective patient data sets, with more than 200 trajectories reviewed. Both surgeons considered ~81% of the optimized trajectories as clinically feasible (75% inter-rater reliability). Quantitative comparison of distance from vessels, insertion angle and gray matter recording index showed that the optim...Continue Reading

References

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Jun 18, 2017·International Journal of Computer Assisted Radiology and Surgery·Rachel SparksSebastien Ourselin
Jul 16, 2017·International Journal of Computer Assisted Radiology and Surgery·Davide ScorzaFrancesco Cardinale
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Citations

Feb 3, 2021·Epilepsia·Xiaojin LiSamden D Lhatoo
Oct 19, 2020·Journal of Biomedical Informatics·Orazio GambinoSalvatore Vitabile

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