Exploration of Interventionists' Technical Manipulation Skills for Robot-Assisted Intravascular PCI Catheterization

Wenjing Du, Olatunji Mumini Omisore, Wenke Duan, Tao Zhou, Xiaodan Lv, Yifa Li, Shipeng Han, Yousef Al-Handarish, Qiuhua Liu, Lei Wang

Research output: Journal PublicationArticlepeer-review

10 Citations (Scopus)


Despite the great proven advantages of imaging techniques in percutaneous coronary interventions (PCIs), the recent rapid increase in the number of PCI procedures exposes both surgical experts and patients to more radiation and orthopedic hazards in the intervention room. While patients are minimally exposed, many appearance of interventionists subject them to frequent exposure of the operational hazards. Despite promoting the use of robot-assisted intravascular PCI in the Cath-Labs, cognitive and technical skills of interventionists' are yet to be explored towards reducing procedure time and minimal exposure of surgeon to operational hazards. In this study, a random forest classification framework is developed for proper identification of technical manipulation skills of surgeons along with underlying motion patterns of the flexible intravascular tools (viz. guidewire and catheter) during PCI catheterization. For this purpose, analysis of interventionists' muscular activities and related hand motion were decoded from physiological signals recorded with sensors. Surface electromyography, electromagnetic, and tactile force signals were acquired concurrently from seven interventionists during specified guidewire movement viz. pull, push, rotate-pull (clockwise rotation with pull), rotate-push (counterclockwise rotation with push), CR (clockwise rotation), and CCR (counterclockwise rotation). While relations were established between force and muscle activities in the surgeon groups using Spearman's Rank-Order statistical method, Wilcoxon test and Kruskal-Wallis one-way ANOVA test were employed to identify intra-group and inter-group differences. From the experimental results obtained, guidewire delivery patterns exhibit stable characteristics with overall classification accuracies of 94.11% based on nineteen features subset from muscle activities and hand motion, followed by 88.01% based on twelve features subset from muscle activities, 71.97% based on seven features subset from hand motion, 84.56% based on ten features subset from part muscle activities and hand motion. Thus, this study shows existence of significant correlation (p < 0.05) between force and muscle activity during intravascular catheterization for PCIs, while comparably high tactile force values were experience in vascular model with plaque or stenosis.

Original languageEnglish
Article number9035494
Pages (from-to)53750-53765
Number of pages16
JournalIEEE Access
Publication statusPublished - 2020
Externally publishedYes


  • Intravascular surgery
  • percutaneous coronary intervention
  • recognition accuracy
  • robotic catheterization
  • surface electromyography

ASJC Scopus subject areas

  • General Computer Science
  • General Materials Science
  • General Engineering


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