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Publisher DOI: 10.1109/TMRB.2022.3146440
arXiv ID: 2201.12168v1
Title: Robotic tissue sampling for safe post-mortem biopsy in infectious corpses
Language: English
Authors: Neidhardt, Maximilian 
Gerlach, Stefan  
Mieling, Till Robin 
Laves, Max-Heinrich 
Weiß, Thorben 
Gromniak, Martin 
Fitzek, Antonia 
Möbius, Dustin 
Kniep, Inga 
Ron, Alexandra 
Schädler, Julia 
Heinemann, Axel 
Püschel, Klaus 
Ondruschka, Benjamin 
Schlaefer, Alexander 
Keywords: Collaborative robot; COVID-19; forensic medicine; medical robotics; path planning; Computer Science - Robotics; Computer Science - Robotics; eess.IV; Quantitative Biology - Tissues and Organs
Issue Date: 26-Jan-2022
Publisher: IEEE
Source: IEEE Transactions on Medical Robotics and Bionics 4 (1): 94-105 (2022-01-28)
Abstract (english): 
In pathology and legal medicine, the histopathological and microbiological analysis of tissue samples from infected deceased is a valuable information for developing treatment strategies during a pandemic such as COVID-19. However, a conventional autopsy carries the risk of disease transmission and may be rejected by relatives. We propose minimally invasive biopsy with robot assistance under CT guidance to minimize the risk of disease transmission during tissue sampling and to improve accuracy. A flexible robotic system for biopsy sampling is presented, which is applied to human corpses placed inside protective body bags. An automatic planning and decision system estimates optimal insertion point. Heat maps projected onto the segmented skin visualize the distance and angle of insertions and estimate the minimum cost of a puncture while avoiding bone collisions. Further, we test multiple insertion paths concerning feasibility and collisions. A custom end effector is designed for inserting needles and extracting tissue samples under robotic guidance. Our robotic post-mortem biopsy (RPMB) system is evaluated in a study during the COVID-19 pandemic on 20 corpses and 10 tissue targets, 5 of them being infected with SARS-CoV-2. The mean planning time including robot path planning is (5.72+-1.67) s. Mean needle placement accuracy is (7.19+-4.22) mm.
DOI: 10.15480/882.4236
ISSN: 2576-3202
Journal: IEEE transactions on medical robotics and bionics 
Institute: Medizintechnische und Intelligente Systeme E-1 
Document Type: Article
More Funding information: Publishing fees supported by Open Access Publishing of Hamburg University of Technology.
License: CC BY 4.0 (Attribution) CC BY 4.0 (Attribution)
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