Achievements and solutions in mechanical engineering II : selected, peer reviewed papers from the 5th International Conference of Mechanical Engineering (ICOME) 2019, October 24-25, 2019, Craiova, Romania. ICOME (Conference) (5th : 2019...最新文献

英文中文

Real Time Prediction of Sclera Force with LSTM Neural Networks in Robot-Assisted Retinal Surgery. 机器人辅助视网膜手术中基于LSTM神经网络的巩膜力实时预测。

Achievements and solutions in mechanical engineering II : selected, peer reviewed papers from the 5th International Conference of Mechanical Engineering (ICOME) 2019, October 24-25, 2019, Craiova, Romania. ICOME (Conference) (5th : 2019...

Pub Date : 2020-02-01 DOI: 10.4028/www.scientific.net/AMM.896.183

Changyan He, Niravkumar Patel, Marin Kobilarov, Lulian Lordachita

Retinal microsurgery is one of the most technically demanding surgeries, during which the surgical tool needs to be inserted into the eyeball and is constantly constrained by the sclerotomy port. During the surgery, any unexpected manipulation could cause extreme tool-sclera contact force leading to sclera damage. Although, a robot assistant could reduce hand tremor and improve the tool positioning accuracy, it cannot prevent or alarm the surgeon about the upcoming danger caused by surgeon's misoperations, i.e., applying excessive force on the sclera. In this paper, we present a new method based on a Long Short Term Memory recurrent neural network for predicting the user behavior, i.e., the contact force between the tool and sclera (sclera force) and the insertion depth of the tool from sclera contact point (insertion depth) in real time (40Hz). The predicted force information is provided to the user through auditory feedback to alarm any unexpected sclera force. The user behavior data is collected in a mock retinal surgical operation on a dry eye phantom with Steady Hand Eye Robot and a novel multi-function sensing tool. The Long Short Term Memory recurrent neural network is trained on the collected time series of sclera force and insertion depth. The network can predict the sclera force and insertion depth 100 milliseconds in the future with 95.29% and 96.57% accuracy, respectively, and can help reduce the fraction of unsafe sclera forces from 40.19% to 15.43%.

视网膜显微手术是技术要求最高的手术之一，手术工具需要插入眼球，并不断受到巩膜切开口的限制。在手术过程中，任何意外的操作都可能造成极大的工具-巩膜接触力，导致巩膜损伤。虽然机器人助手可以减少手部震颤，提高工具的定位精度，但它不能防止或提醒外科医生手术失误所带来的危险，即对巩膜施加过大的力。本文提出了一种基于长短期记忆递归神经网络的预测用户行为的新方法，即工具与巩膜之间的接触力(巩膜力)和工具从巩膜接触点的插入深度(插入深度)实时预测(40Hz)。预测的力信息通过听觉反馈提供给用户，报警任何意外的巩膜力。使用稳态手眼机器人和一种新型多功能传感工具在干眼幻影上模拟视网膜手术中收集用户行为数据。利用采集到的巩膜力和插入深度的时间序列对长短期记忆递归神经网络进行训练。该网络可以在100毫秒内预测巩膜力和插入深度，准确率分别为95.29%和96.57%，并将不安全巩膜力的比例从40.19%降低到15.43%。

{"title":"Real Time Prediction of Sclera Force with LSTM Neural Networks in Robot-Assisted Retinal Surgery.","authors":"Changyan He, Niravkumar Patel, Marin Kobilarov, Lulian Lordachita","doi":"10.4028/www.scientific.net/AMM.896.183","DOIUrl":"https://doi.org/10.4028/www.scientific.net/AMM.896.183","url":null,"abstract":"<p><p>Retinal microsurgery is one of the most technically demanding surgeries, during which the surgical tool needs to be inserted into the eyeball and is constantly constrained by the sclerotomy port. During the surgery, any unexpected manipulation could cause extreme tool-sclera contact force leading to sclera damage. Although, a robot assistant could reduce hand tremor and improve the tool positioning accuracy, it cannot prevent or alarm the surgeon about the upcoming danger caused by surgeon's misoperations, i.e., applying excessive force on the sclera. In this paper, we present a new method based on a Long Short Term Memory recurrent neural network for predicting the user behavior, i.e., the contact force between the tool and sclera (sclera force) and the insertion depth of the tool from sclera contact point (insertion depth) in real time (40Hz). The predicted force information is provided to the user through auditory feedback to alarm any unexpected sclera force. The user behavior data is collected in a mock retinal surgical operation on a dry eye phantom with Steady Hand Eye Robot and a novel multi-function sensing tool. The Long Short Term Memory recurrent neural network is trained on the collected time series of sclera force and insertion depth. The network can predict the sclera force and insertion depth 100 milliseconds in the future with 95.29% and 96.57% accuracy, respectively, and can help reduce the fraction of unsafe sclera forces from 40.19% to 15.43%.</p>","PeriodicalId":93241,"journal":{"name":"Achievements and solutions in mechanical engineering II : selected, peer reviewed papers from the 5th International Conference of Mechanical Engineering (ICOME) 2019, October 24-25, 2019, Craiova, Romania. ICOME (Conference) (5th : 2019...","volume":"896 ","pages":"183-194"},"PeriodicalIF":0.0,"publicationDate":"2020-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.4028/www.scientific.net/AMM.896.183","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39424902","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

类型

全部化学•材料生命科学医学物理工程技术环境•农林材料科学地球科学法学管理学化学环境科学与生态学计算机科学教育学经济学农林科学人文科学生物学数学物理与天体物理心理学综合性期刊其他工业工程理学历史学农学文学信息工程

数据库

全部 ACS Publications Elsevier ieeexplore Springer The Royal Society of Chemistry Wiley

期刊

全部 Acc. Chem. Res. ACS Applied Bio Materials ACS Appl. Electron. Mater. ACS Appl. Energy Mater. ACS Appl. Mater. Interfaces ACS Appl. Nano Mater. ACS Appl. Polym. Mater. ACS BIOMATER-SCI ENG ACS Catal. ACS Cent. Sci. ACS Chem. Biol. ACS Chemical Health & Safety ACS Chem. Neurosci. ACS Comb. Sci. ACS Earth Space Chem. ACS Energy Lett. ACS Infect. Dis. ACS Macro Lett. ACS Mater. Lett. ACS Med. Chem. Lett. ACS Nano ACS Omega ACS Photonics ACS Sens. ACS Sustainable Chem. Eng. ACS Synth. Biol. Anal. Chem. BIOCHEMISTRY-US Bioconjugate Chem. BIOMACROMOLECULES Chem. Res. Toxicol. Chem. Rev. Chem. Mater. CRYST GROWTH DES ENERG FUEL Environ. Sci. Technol. Environ. Sci. Technol. Lett. Eur. J. Inorg. Chem. IND ENG CHEM RES Inorg. Chem. J. Agric. Food. Chem. J. Chem. Eng. Data J. Chem. Educ. J. Chem. Inf. Model. J. Chem. Theory Comput. J. Med. Chem. J. Nat. Prod. J PROTEOME RES J. Am. Chem. Soc. LANGMUIR MACROMOLECULES Mol. Pharmaceutics Nano Lett. Org. Lett. ORG PROCESS RES DEV ORGANOMETALLICS J. Org. Chem. J. Phys. Chem. J. Phys. Chem. A J. Phys. Chem. B J. Phys. Chem. C J. Phys. Chem. Lett. Analyst Anal. Methods Biomater. Sci. Catal. Sci. Technol. Chem. Commun. Chem. Soc. Rev. CHEM EDUC RES PRACT CRYSTENGCOMM Dalton Trans. Energy Environ. Sci. ENVIRON SCI-NANO ENVIRON SCI-PROC IMP ENVIRON SCI-WAT RES Faraday Discuss. Food Funct. Green Chem. Inorg. Chem. Front. Integr. Biol. J. Anal. At. Spectrom. J. Mater. Chem. A J. Mater. Chem. B J. Mater. Chem. C Lab Chip Mater. Chem. Front. Mater. Horiz. MEDCHEMCOMM Metallomics Mol. Biosyst. Mol. Syst. Des. Eng. Nanoscale Nanoscale Horiz. Nat. Prod. Rep. New J. Chem. Org. Biomol. Chem. Org. Chem. Front. PHOTOCH PHOTOBIO SCI PCCP Polym. Chem.

﹀