Clinical Translation of the LevelCheck Decision Support Algorithm for Target Localization in Spine Surgery
Recent work has yielded a method for automatic labeling of vertebrae in intraoperative radiographs as an assistant to manual level counting. The method, called LevelCheck , previously demonstrated promise in phantom studies and retrospective studies. This study aims to: (#1) Analyze the effect of Le...
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| Published in | Annals of biomedical engineering Vol. 46; no. 10; pp. 1548 - 1557 |
|---|---|
| Main Authors | , , , , , , , , , , , , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
New York
Springer US
01.10.2018
Springer Nature B.V |
| Subjects | |
| Online Access | Get full text |
| ISSN | 0090-6964 1573-9686 1573-9686 |
| DOI | 10.1007/s10439-018-2099-2 |
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| Abstract | Recent work has yielded a method for automatic labeling of vertebrae in intraoperative radiographs as an assistant to manual level counting. The method, called
LevelCheck
, previously demonstrated promise in phantom studies and retrospective studies. This study aims to: (#1) Analyze the effect of
LevelCheck
on accuracy and confidence of localization in two modes: (a)
Independent Check
(labels displayed after the surgeon’s decision) and (b)
Active Assistant
(labels presented before the surgeon’s decision). (#2) Assess the feasibility and utility of
LevelCheck
in the operating room. Two studies were conducted: a laboratory study investigating these two workflow implementations in a simulated operating environment with 5 surgeons, reviewing 62 cases selected from a dataset of radiographs exhibiting a challenge to vertebral localization; and a clinical study involving 20 patients undergoing spine surgery. In Study #1, the median localization error without assistance was 30.4% (IQR = 5.2%) due to the challenging nature of the cases.
LevelCheck
reduced the median error to 2.4% for both the
Independent Check
and
Active Assistant
modes (
p
< 0.01). Surgeons found
LevelCheck
to increase confidence in 91% of cases. Study #2 demonstrated accuracy in all cases. The algorithm runtime varied from 17 to 72 s in its current implementation. The algorithm was shown to be feasible, accurate, and to improve confidence during surgery. |
|---|---|
| AbstractList | Recent work has yielded a method for automatic labeling of vertebrae in intraoperative radiographs as an assistant to manual level counting. The method, called LevelCheck, previously demonstrated promise in phantom studies and retrospective studies. This study aims to: (#1) Analyze the effect of LevelCheck on accuracy and confidence of localization in two modes: (a) Independent Check (labels displayed after the surgeon’s decision) and (b) Active Assistant (labels presented before the surgeon’s decision). (#2) Assess the feasibility and utility of LevelCheck in the operating room. Two studies were conducted: a laboratory study investigating these two workflow implementations in a simulated operating environment with 5 surgeons, reviewing 62 cases selected from a dataset of radiographs exhibiting a challenge to vertebral localization; and a clinical study involving 20 patients undergoing spine surgery. In Study #1, the median localization error without assistance was 30.4% (IQR = 5.2%) due to the challenging nature of the cases. LevelCheck reduced the median error to 2.4% for both the Independent Check and Active Assistant modes (p < 0.01). Surgeons found LevelCheck to increase confidence in 91% of cases. Study #2 demonstrated accuracy in all cases. The algorithm runtime varied from 17 to 72 s in its current implementation. The algorithm was shown to be feasible, accurate, and to improve confidence during surgery. Recent work has yielded a method for automatic labeling of vertebrae in intraoperative radiographs as an assistant to manual level counting. The method, called LevelCheck, previously demonstrated promise in phantom studies and retrospective studies. This study aims to: (#1) Analyze the effect of LevelCheck on accuracy and confidence of localization in two modes: (a) Independent Check (labels displayed after the surgeon's decision) and (b) Active Assistant (labels presented before the surgeon's decision). (#2) Assess the feasibility and utility of LevelCheck in the operating room. Two studies were conducted: a laboratory study investigating these two workflow implementations in a simulated operating environment with 5 surgeons, reviewing 62 cases selected from a dataset of radiographs exhibiting a challenge to vertebral localization; and a clinical study involving 20 patients undergoing spine surgery. In Study #1, the median localization error without assistance was 30.4% (IQR = 5.2%) due to the challenging nature of the cases. LevelCheck reduced the median error to 2.4% for both the Independent Check and Active Assistant modes (p < 0.01). Surgeons found LevelCheck to increase confidence in 91% of cases. Study #2 demonstrated accuracy in all cases. The algorithm runtime varied from 17 to 72 s in its current implementation. The algorithm was shown to be feasible, accurate, and to improve confidence during surgery.Recent work has yielded a method for automatic labeling of vertebrae in intraoperative radiographs as an assistant to manual level counting. The method, called LevelCheck, previously demonstrated promise in phantom studies and retrospective studies. This study aims to: (#1) Analyze the effect of LevelCheck on accuracy and confidence of localization in two modes: (a) Independent Check (labels displayed after the surgeon's decision) and (b) Active Assistant (labels presented before the surgeon's decision). (#2) Assess the feasibility and utility of LevelCheck in the operating room. Two studies were conducted: a laboratory study investigating these two workflow implementations in a simulated operating environment with 5 surgeons, reviewing 62 cases selected from a dataset of radiographs exhibiting a challenge to vertebral localization; and a clinical study involving 20 patients undergoing spine surgery. In Study #1, the median localization error without assistance was 30.4% (IQR = 5.2%) due to the challenging nature of the cases. LevelCheck reduced the median error to 2.4% for both the Independent Check and Active Assistant modes (p < 0.01). Surgeons found LevelCheck to increase confidence in 91% of cases. Study #2 demonstrated accuracy in all cases. The algorithm runtime varied from 17 to 72 s in its current implementation. The algorithm was shown to be feasible, accurate, and to improve confidence during surgery. Recent work has yielded a method for automatic labeling of vertebrae in intraoperative radiographs as an assistant to manual level counting. The method, called LevelCheck, previously demonstrated promise in phantom studies and retrospective studies. This study aims to: (#1) Analyze the effect of LevelCheck on accuracy and confidence of localization in two modes: (a) Independent Check (labels displayed after the surgeon's decision) and (b) Active Assistant (labels presented before the surgeon's decision). (#2) Assess the feasibility and utility of LevelCheck in the operating room. Two studies were conducted: a laboratory study investigating these two workflow implementations in a simulated operating environment with 5 surgeons, reviewing 62 cases selected from a dataset of radiographs exhibiting a challenge to vertebral localization; and a clinical study involving 20 patients undergoing spine surgery. In Study #1, the median localization error without assistance was 30.4% (IQR = 5.2%) due to the challenging nature of the cases. LevelCheck reduced the median error to 2.4% for both the Independent Check and Active Assistant modes (p < 0.01). Surgeons found LevelCheck to increase confidence in 91% of cases. Study #2 demonstrated accuracy in all cases. The algorithm runtime varied from 17 to 72 s in its current implementation. The algorithm was shown to be feasible, accurate, and to improve confidence during surgery. Recent work has yielded a method for automatic labeling of vertebrae in intraoperative radiographs as an assistant to manual level counting. The method, called LevelCheck , previously demonstrated promise in phantom studies and retrospective studies. This study aims to: (#1) Analyze the effect of LevelCheck on accuracy and confidence of localization in two modes: (a) Independent Check (labels displayed after the surgeon’s decision) and (b) Active Assistant (labels presented before the surgeon’s decision). (#2) Assess the feasibility and utility of LevelCheck in the operating room. Two studies were conducted: a laboratory study investigating these two workflow implementations in a simulated operating environment with 5 surgeons, reviewing 62 cases selected from a dataset of radiographs exhibiting a challenge to vertebral localization; and a clinical study involving 20 patients undergoing spine surgery. In Study #1, the median localization error without assistance was 30.4% (IQR = 5.2%) due to the challenging nature of the cases. LevelCheck reduced the median error to 2.4% for both the Independent Check and Active Assistant modes ( p < 0.01). Surgeons found LevelCheck to increase confidence in 91% of cases. Study #2 demonstrated accuracy in all cases. The algorithm runtime varied from 17 to 72 s in its current implementation. The algorithm was shown to be feasible, accurate, and to improve confidence during surgery. |
| Author | Groves, Mari Ketcha, Michael Jacobson, Matthew Iyer, Rajiv Wolinsky, Jean-Paul Vogt, Sebastian Garzon-Muvdi, Tomas Uneri, Ali Kleinszig, Gerhard Ye, Xiaobu Siewerdsen, Jeffrey H. Thompson, David Manbachi, Amir De Silva, Tharindu Han, Runze Aygun, Nafi Molina, Camilo Raber, Michael R. Goerres, Joseph |
| AuthorAffiliation | 4 Armstrong Institute for Patient Safety and Quality, Johns Hopkins University School of Medicine, 750 E Pratt St, 15th Floor, Baltimore, MD 21202, USA 1 Department of Biomedical Engineering, Johns Hopkins University, 3400 N. Charles Street, Wyman Park Building, Suite 400 West, Baltimore, MD 21218, USA 2 Department of Neurosurgery, Johns Hopkins University School of Medicine, The Johns Hopkins Hospital, 1800 Orleans Street, Baltimore, MD 21287, USA 3 Russell H. Morgan Department of Radiology, Johns Hopkins University, The Johns Hopkins Hospital, 1800 Orleans Street, Baltimore, MD 21287, USA 5 Siemens Healthineers, Henkestraße 127, 91052 Erlangen, Germany 6 Department of Biomedical Engineering, Johns Hopkins University, Traylor Building, Rm 622, 720 Rutland Avenue, Baltimore, MD 21205, USA |
| AuthorAffiliation_xml | – name: 3 Russell H. Morgan Department of Radiology, Johns Hopkins University, The Johns Hopkins Hospital, 1800 Orleans Street, Baltimore, MD 21287, USA – name: 1 Department of Biomedical Engineering, Johns Hopkins University, 3400 N. Charles Street, Wyman Park Building, Suite 400 West, Baltimore, MD 21218, USA – name: 2 Department of Neurosurgery, Johns Hopkins University School of Medicine, The Johns Hopkins Hospital, 1800 Orleans Street, Baltimore, MD 21287, USA – name: 4 Armstrong Institute for Patient Safety and Quality, Johns Hopkins University School of Medicine, 750 E Pratt St, 15th Floor, Baltimore, MD 21202, USA – name: 6 Department of Biomedical Engineering, Johns Hopkins University, Traylor Building, Rm 622, 720 Rutland Avenue, Baltimore, MD 21205, USA – name: 5 Siemens Healthineers, Henkestraße 127, 91052 Erlangen, Germany |
| Author_xml | – sequence: 1 givenname: Amir surname: Manbachi fullname: Manbachi, Amir organization: Department of Biomedical Engineering, Johns Hopkins University, Department of Neurosurgery, Johns Hopkins University School of Medicine, The Johns Hopkins Hospital – sequence: 2 givenname: Tharindu surname: De Silva fullname: De Silva, Tharindu organization: Department of Biomedical Engineering, Johns Hopkins University – sequence: 3 givenname: Ali surname: Uneri fullname: Uneri, Ali organization: Department of Biomedical Engineering, Johns Hopkins University – sequence: 4 givenname: Matthew surname: Jacobson fullname: Jacobson, Matthew organization: Department of Biomedical Engineering, Johns Hopkins University – sequence: 5 givenname: Joseph surname: Goerres fullname: Goerres, Joseph organization: Department of Biomedical Engineering, Johns Hopkins University – sequence: 6 givenname: Michael surname: Ketcha fullname: Ketcha, Michael organization: Department of Biomedical Engineering, Johns Hopkins University – sequence: 7 givenname: Runze surname: Han fullname: Han, Runze organization: Department of Biomedical Engineering, Johns Hopkins University – sequence: 8 givenname: Nafi surname: Aygun fullname: Aygun, Nafi organization: Russell H. Morgan Department of Radiology, Johns Hopkins University, The Johns Hopkins Hospital – sequence: 9 givenname: David surname: Thompson fullname: Thompson, David organization: Armstrong Institute for Patient Safety and Quality, Johns Hopkins University School of Medicine – sequence: 10 givenname: Xiaobu surname: Ye fullname: Ye, Xiaobu organization: Department of Neurosurgery, Johns Hopkins University School of Medicine, The Johns Hopkins Hospital – sequence: 11 givenname: Sebastian surname: Vogt fullname: Vogt, Sebastian organization: Siemens Healthineers – sequence: 12 givenname: Gerhard surname: Kleinszig fullname: Kleinszig, Gerhard organization: Siemens Healthineers – sequence: 13 givenname: Camilo surname: Molina fullname: Molina, Camilo organization: Department of Neurosurgery, Johns Hopkins University School of Medicine, The Johns Hopkins Hospital – sequence: 14 givenname: Rajiv surname: Iyer fullname: Iyer, Rajiv organization: Department of Neurosurgery, Johns Hopkins University School of Medicine, The Johns Hopkins Hospital – sequence: 15 givenname: Tomas surname: Garzon-Muvdi fullname: Garzon-Muvdi, Tomas organization: Department of Neurosurgery, Johns Hopkins University School of Medicine, The Johns Hopkins Hospital – sequence: 16 givenname: Michael R. surname: Raber fullname: Raber, Michael R. organization: Department of Neurosurgery, Johns Hopkins University School of Medicine, The Johns Hopkins Hospital – sequence: 17 givenname: Mari surname: Groves fullname: Groves, Mari organization: Department of Neurosurgery, Johns Hopkins University School of Medicine, The Johns Hopkins Hospital – sequence: 18 givenname: Jean-Paul surname: Wolinsky fullname: Wolinsky, Jean-Paul organization: Department of Neurosurgery, Johns Hopkins University School of Medicine, The Johns Hopkins Hospital – sequence: 19 givenname: Jeffrey H. surname: Siewerdsen fullname: Siewerdsen, Jeffrey H. email: jeff.siewerdsen@jhu.edu organization: Department of Biomedical Engineering, Johns Hopkins University, Department of Neurosurgery, Johns Hopkins University School of Medicine, The Johns Hopkins Hospital, Russell H. Morgan Department of Radiology, Johns Hopkins University, The Johns Hopkins Hospital, Armstrong Institute for Patient Safety and Quality, Johns Hopkins University School of Medicine, Department of Biomedical Engineering, Johns Hopkins University |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/30051244$$D View this record in MEDLINE/PubMed |
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| CitedBy_id | crossref_primary_10_1007_s10439_018_02127_7 crossref_primary_10_1088_1361_6560_ac07c7 crossref_primary_10_1016_j_cmpb_2022_107222 |
| Cites_doi | 10.1097/BRS.0000000000000814 10.1007/s00330-010-1821-8 10.1088/0031-9155/61/8/3009 10.1088/1361-6560/aa6b3e 10.1088/0031-9155/57/17/5485 10.4103/2152-7806.79769 10.1097/BRS.0000000000001589 10.1097/BRS.0b013e31816043d1 10.1088/0031-9155/60/5/2075 10.1088/0031-9155/58/23/8535 10.1088/1361-6560/62/2/684 |
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| Copyright | Biomedical Engineering Society 2018 Annals of Biomedical Engineering is a copyright of Springer, (2018). All Rights Reserved. |
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Biol.20156052075209010.1088/0031-9155/60/5/2075256748514640192 – reference: De SilvaTUneriAKetchaMDReaungamornratSKleinszigGVogtSAygunNLoSFWolinskyJPSiewerdsenJH3D–2D image registration for target localization in spine surgery: investigation of similarity metrics providing robustness to content mismatchPhys. Med. Biol.20166183009302510.1088/0031-9155/61/8/30091:CAS:528:DC%2BC2sXisVKqtr4%3D269922454915376 – reference: SamulskiMHupseRBoetesCMusRDMden HeetenGJKarssemeijerNUsing computer-aided detection in mammography as a decision supportEur. Radiol.201020102323233010.1007/s00330-010-1821-8205328902940044 – reference: De SilvaTUneriAKetchaMDReaungamornratSGoerresJJacobsonMVogtSKleinszigGKhannaAJWolinskyJPSiewerdsenJHRegistration of MRI to intraoperative radiographs for target localization in spinal interventionsPhys. Med. Biol.201762268470110.1088/1361-6560/62/2/684280509725321067 – reference: KetchaMDDe SilvaTUneriAJacobsonMWGoerresJKleinszigGVogtSWolinskyJPSiewerdsenJHMulti-stage 3D-2D registration for correction of anatomical deformation in image-guided spine surgeryPhys. Med. Biol.201710.1088/1361-6560/aa6b3e290586875868367 – reference: OtakeYSchaferSStaymanJWZbijewskiWKleinszigGGraumannRKhannaAJSiewerdsenJHAutomatic localization of vertebral levels in x-ray fluoroscopy using 3D-2D registration: a tool to reduce wrong-site surgeryPhys. Med. Biol.201257175485550810.1088/0031-9155/57/17/54851:STN:280:DC%2BC38flsFOrtA%3D%3D228643663429949 – reference: OtakeYWangASStaymanJWUneriAKleinszigGVogtSKhannaAJGokaslanZLSiewerdsenJHRobust 3D–2D image registration: application to spine interventions and vertebral labeling in the presence of anatomical deformationPhys. Med. Biol.201358238535855310.1088/0031-9155/58/23/8535242463864915373 – reference: Adverse Events, Near Misses, and Errors. Patient Safety Primer. Patient Safety Network, Agency for Healthcare Research and Quality, 2016. https://psnet.ahrq.gov/primers/primer/34/adverse-events-near-misses-and-errors Accessed April 22, 2017 – reference: De SilvaTLoSLAygunNAghionDMBoahAPetteysRUneriAKetchaMDYiTVogtSKleinszigGWeiWWeitenMYeXBydonASciubbaDMWithamTFWolinskyJPSiewerdsenJHUtility of the LevelCheck algorithm for decision support in vertebral localizationSpine (Phila Pa 1976)20164120E1249E125610.1097/BRS.0000000000001589 – reference: LoSFOtakeYPuvanesarajahVWangASUneriADe SilvaTVogtSKleinszigGElderBDGoodwinCRKosztowskiTALiauwJAGrovesMBydonASciubbaDMWithamTFWolinskyJPAygunNGokaslanZLSiewerdsenJHAutomatic localization of target vertebrae in spine surgerySpine (Phila Pa 1976)2015408E476E48310.1097/BRS.00000000000008144433144 – reference: Levinson D.R. Adverse events in hospitals: national incidence among medicare beneficiaries. Washington, DC: US Department of Health and Human Services, Office of the Inspector General; November 2010. Report No. OEI-06-09-00090. https://psnet.ahrq.gov/resources/resource/19811 Accessed April 22, 2017 – reference: Makary M. A. How to Stop Hospitals from Killing Us—WSJ. The Wall Street Journal. https://www.wsj.com/articles/SB10000872396390444620104578008263334441352. Published 2012. Accessed April 13, 2017. – volume: 40 start-page: E476 issue: 8 year: 2015 ident: 2099_CR9 publication-title: Spine (Phila Pa 1976) doi: 10.1097/BRS.0000000000000814 – volume: 20 start-page: 2323 issue: 10 year: 2010 ident: 2099_CR15 publication-title: Eur. Radiol. doi: 10.1007/s00330-010-1821-8 – volume: 61 start-page: 3009 issue: 8 year: 2016 ident: 2099_CR4 publication-title: Phys. Med. Biol. doi: 10.1088/0031-9155/61/8/3009 – year: 2017 ident: 2099_CR7 publication-title: Phys. Med. Biol. doi: 10.1088/1361-6560/aa6b3e – volume: 57 start-page: 5485 issue: 17 year: 2012 ident: 2099_CR12 publication-title: Phys. Med. Biol. doi: 10.1088/0031-9155/57/17/5485 – ident: 2099_CR10 – ident: 2099_CR5 – volume: 2 start-page: 47 year: 2011 ident: 2099_CR6 publication-title: Surg. Neurol. Int. doi: 10.4103/2152-7806.79769 – ident: 2099_CR1 – volume: 41 start-page: E1249 issue: 20 year: 2016 ident: 2099_CR2 publication-title: Spine (Phila Pa 1976) doi: 10.1097/BRS.0000000000001589 – volume: 33 start-page: 194 issue: 2 year: 2008 ident: 2099_CR11 publication-title: Spine (Phila Pa 1976) doi: 10.1097/BRS.0b013e31816043d1 – volume: 60 start-page: 2075 issue: 5 year: 2015 ident: 2099_CR14 publication-title: Phys. Med. Biol. doi: 10.1088/0031-9155/60/5/2075 – volume: 58 start-page: 8535 issue: 23 year: 2013 ident: 2099_CR13 publication-title: Phys. Med. Biol. doi: 10.1088/0031-9155/58/23/8535 – ident: 2099_CR8 – volume: 62 start-page: 684 issue: 2 year: 2017 ident: 2099_CR3 publication-title: Phys. Med. Biol. doi: 10.1088/1361-6560/62/2/684 |
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| Snippet | Recent work has yielded a method for automatic labeling of vertebrae in intraoperative radiographs as an assistant to manual level counting. The method, called... |
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| SubjectTerms | Algorithms Biochemistry Biological and Medical Physics Biomedical and Life Sciences Biomedical Engineering and Bioengineering Biomedicine Biophysics Bone surgery Classical Mechanics Computer simulation Decision Making, Computer-Assisted Feasibility studies Humans Labels Localization Medical Robotics Neurosurgical Procedures - instrumentation Neurosurgical Procedures - methods Radiographs Radiography Spinal Cord - diagnostic imaging Spinal Cord - surgery Spine Studies Surgeons Surgery Translational Research, Biomedical - instrumentation Translational Research, Biomedical - methods Vertebrae Workflow |
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| Title | Clinical Translation of the LevelCheck Decision Support Algorithm for Target Localization in Spine Surgery |
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