Surface Curvature as a Classifier of Abdominal Aortic Aneurysms: A Comparative Analysis View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2012-11-22

AUTHORS

Kibaek Lee, Junjun Zhu, Judy Shum, Yongjie Zhang, Satish C. Muluk, Ankur Chandra, Mark K. Eskandari, Ender A. Finol

ABSTRACT

An abdominal aortic aneurysm (AAA) carries one of the highest mortality rates among vascular diseases when it ruptures. To predict the role of surface curvature in rupture risk assessment, a discriminatory analysis of aneurysm geometry characterization was conducted. Data was obtained from 205 patient-specific computed tomography image sets corresponding to three AAA population subgroups: patients under surveillance, those that underwent elective repair of the aneurysm, and those with an emergent repair. Each AAA was reconstructed and their surface curvatures estimated using the biquintic Hermite finite element method. Local surface curvatures were processed into ten global curvature indices. Statistical analysis of the data revealed that the L2-norm of the Gaussian and Mean surface curvatures can be utilized as classifiers of the three AAA population subgroups. The application of statistical machine learning on the curvature features yielded 85.5% accuracy in classifying electively and emergent repaired AAAs, compared to a 68.9% accuracy obtained by using maximum aneurysm diameter alone. Such combination of non-invasive geometric quantification and statistical machine learning methods can be used in a clinical setting to assess the risk of rupture of aneurysms during regular patient follow-ups. More... »

PAGES

562-576

References to SciGraph publications

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s10439-012-0691-4

DOI

http://dx.doi.org/10.1007/s10439-012-0691-4

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1018683007

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/23180028


Indexing Status Check whether this publication has been indexed by Scopus and Web Of Science using the SN Indexing Status Tool
Incoming Citations Browse incoming citations for this publication using opencitations.net

JSON-LD is the canonical representation for SciGraph data.

TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT

[
  {
    "@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json", 
    "about": [
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/11", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Medical and Health Sciences", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/1103", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Clinical Sciences", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Angiography", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Aortic Aneurysm, Abdominal", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Aortic Rupture", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Artificial Intelligence", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Biomedical Engineering", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Computer Simulation", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Finite Element Analysis", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Humans", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Imaging, Three-Dimensional", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Models, Cardiovascular", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Radiographic Image Interpretation, Computer-Assisted", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Tomography, X-Ray Computed", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "Mechanical Engineering Department, Carnegie Mellon University, 5000 Forbes Avenue, 15213, Pittsburgh, PA, USA", 
          "id": "http://www.grid.ac/institutes/grid.147455.6", 
          "name": [
            "Mechanical Engineering Department, Carnegie Mellon University, 5000 Forbes Avenue, 15213, Pittsburgh, PA, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Lee", 
        "givenName": "Kibaek", 
        "id": "sg:person.0660627730.19", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0660627730.19"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Mechanical Engineering Department, Carnegie Mellon University, 5000 Forbes Avenue, 15213, Pittsburgh, PA, USA", 
          "id": "http://www.grid.ac/institutes/grid.147455.6", 
          "name": [
            "Mechanical Engineering Department, Carnegie Mellon University, 5000 Forbes Avenue, 15213, Pittsburgh, PA, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Zhu", 
        "givenName": "Junjun", 
        "id": "sg:person.011004600555.34", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011004600555.34"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Biomedical Engineering Department, Carnegie Mellon University, 5000 Forbes Avenue, 15213, Pittsburgh, PA, USA", 
          "id": "http://www.grid.ac/institutes/grid.147455.6", 
          "name": [
            "Biomedical Engineering Department, Carnegie Mellon University, 5000 Forbes Avenue, 15213, Pittsburgh, PA, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Shum", 
        "givenName": "Judy", 
        "id": "sg:person.0775056330.89", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0775056330.89"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Mechanical Engineering Department, Carnegie Mellon University, 5000 Forbes Avenue, 15213, Pittsburgh, PA, USA", 
          "id": "http://www.grid.ac/institutes/grid.147455.6", 
          "name": [
            "Mechanical Engineering Department, Carnegie Mellon University, 5000 Forbes Avenue, 15213, Pittsburgh, PA, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Zhang", 
        "givenName": "Yongjie", 
        "id": "sg:person.07646135347.75", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.07646135347.75"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Division of Vascular Surgery, Allegheny-Singer Research Institute, West Penn Allegheny Health System, 14th Floor, South Tower, 320 East North Avenue, 15212, Pittsburgh, PA, USA", 
          "id": "http://www.grid.ac/institutes/grid.280673.8", 
          "name": [
            "Division of Vascular Surgery, Allegheny-Singer Research Institute, West Penn Allegheny Health System, 14th Floor, South Tower, 320 East North Avenue, 15212, Pittsburgh, PA, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Muluk", 
        "givenName": "Satish C.", 
        "id": "sg:person.0764073425.77", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0764073425.77"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Division of Vascular Surgery, Rochester Institute of Technology, University of Rochester School of Medicine, and Dentistry, 601 Elmwood Avenue, Box 652, 14642, Rochester, NY, USA", 
          "id": "http://www.grid.ac/institutes/None", 
          "name": [
            "Division of Vascular Surgery, Rochester Institute of Technology, University of Rochester School of Medicine, and Dentistry, 601 Elmwood Avenue, Box 652, 14642, Rochester, NY, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Chandra", 
        "givenName": "Ankur", 
        "id": "sg:person.0774466300.01", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0774466300.01"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Division of Vascular Surgery, Northwestern University Feinberg School of Medicine, 676 North Saint Clair Street, Suite #650, 60611, Chicago, IL, USA", 
          "id": "http://www.grid.ac/institutes/grid.16753.36", 
          "name": [
            "Division of Vascular Surgery, Northwestern University Feinberg School of Medicine, 676 North Saint Clair Street, Suite #650, 60611, Chicago, IL, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Eskandari", 
        "givenName": "Mark K.", 
        "id": "sg:person.01246741357.55", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01246741357.55"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Department of Biomedical Engineering, The University of Texas at San Antonio, One UTSA Circle, AET 1.360, 78249, San Antonio, TX, USA", 
          "id": "http://www.grid.ac/institutes/grid.215352.2", 
          "name": [
            "Department of Biomedical Engineering, The University of Texas at San Antonio, One UTSA Circle, AET 1.360, 78249, San Antonio, TX, USA"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Finol", 
        "givenName": "Ender A.", 
        "id": "sg:person.01046253173.69", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01046253173.69"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "sg:pub.10.1007/s10439-010-0175-3", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1016650194", 
          "https://doi.org/10.1007/s10439-010-0175-3"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1114/1.202", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1031437224", 
          "https://doi.org/10.1114/1.202"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1023/b:abme.0000012746.31343.92", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1051822214", 
          "https://doi.org/10.1023/b:abme.0000012746.31343.92"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/bf02368182", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1029783791", 
          "https://doi.org/10.1007/bf02368182"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1114/1.1306342", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1018897655", 
          "https://doi.org/10.1114/1.1306342"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2012-11-22", 
    "datePublishedReg": "2012-11-22", 
    "description": "An abdominal aortic aneurysm (AAA) carries one of the highest mortality rates among vascular diseases when it ruptures. To predict the role of surface curvature in rupture risk assessment, a discriminatory analysis of aneurysm geometry characterization was conducted. Data was obtained from 205 patient-specific computed tomography image sets corresponding to three AAA population subgroups: patients under surveillance, those that underwent elective repair of the aneurysm, and those with an emergent repair. Each AAA was reconstructed and their surface curvatures estimated using the biquintic Hermite finite element method. Local surface curvatures were processed into ten global curvature indices. Statistical analysis of the data revealed that the L2-norm of the Gaussian and Mean surface curvatures can be utilized as classifiers of the three AAA population subgroups. The application of statistical machine learning on the curvature features yielded 85.5% accuracy in classifying electively and emergent repaired AAAs, compared to a 68.9% accuracy obtained by using maximum aneurysm diameter alone. Such combination of non-invasive geometric quantification and statistical machine learning methods can be used in a clinical setting to assess the risk of rupture of aneurysms during regular patient follow-ups.", 
    "genre": "article", 
    "id": "sg:pub.10.1007/s10439-012-0691-4", 
    "inLanguage": "en", 
    "isAccessibleForFree": true, 
    "isFundedItemOf": [
      {
        "id": "sg:grant.2610386", 
        "type": "MonetaryGrant"
      }, 
      {
        "id": "sg:grant.2593674", 
        "type": "MonetaryGrant"
      }, 
      {
        "id": "sg:grant.2610466", 
        "type": "MonetaryGrant"
      }
    ], 
    "isPartOf": [
      {
        "id": "sg:journal.1087247", 
        "issn": [
          "0145-3068", 
          "1573-9686"
        ], 
        "name": "Annals of Biomedical Engineering", 
        "publisher": "Springer Nature", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "3", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "41"
      }
    ], 
    "keywords": [
      "Hermite finite element method", 
      "statistical machine", 
      "L2 norm", 
      "finite element method", 
      "surface curvature", 
      "geometric quantification", 
      "element method", 
      "geometry characterization", 
      "mean surface curvature", 
      "curvature", 
      "local surface curvature", 
      "statistical analysis", 
      "curvature features", 
      "Gaussian", 
      "accuracy", 
      "rupture risk assessment", 
      "tomography image sets", 
      "curvature index", 
      "such combinations", 
      "set", 
      "machine", 
      "applications", 
      "discriminatory analysis", 
      "analysis", 
      "data", 
      "comparative analysis", 
      "abdominal aortic aneurysm", 
      "features", 
      "classifier", 
      "method", 
      "risk assessment", 
      "risk of rupture", 
      "diameter", 
      "subgroups", 
      "aortic aneurysm", 
      "characterization", 
      "image sets", 
      "combination", 
      "quantification", 
      "population subgroups", 
      "high mortality rate", 
      "maximum aneurysm diameter", 
      "elective repair", 
      "emergent repair", 
      "vascular disease", 
      "aneurysm diameter", 
      "setting", 
      "mortality rate", 
      "regular patients", 
      "aneurysms", 
      "clinical setting", 
      "index", 
      "rupture", 
      "rate", 
      "patients", 
      "repair", 
      "disease", 
      "assessment", 
      "risk", 
      "surveillance", 
      "role", 
      "aneurysm geometry characterization", 
      "AAA population subgroups", 
      "biquintic Hermite finite element method", 
      "global curvature indices", 
      "non-invasive geometric quantification"
    ], 
    "name": "Surface Curvature as a Classifier of Abdominal Aortic Aneurysms: A Comparative Analysis", 
    "pagination": "562-576", 
    "productId": [
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1018683007"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/s10439-012-0691-4"
        ]
      }, 
      {
        "name": "pubmed_id", 
        "type": "PropertyValue", 
        "value": [
          "23180028"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1007/s10439-012-0691-4", 
      "https://app.dimensions.ai/details/publication/pub.1018683007"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2021-11-01T18:16", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-springernature-scigraph/baseset/20211101/entities/gbq_results/article/article_561.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "https://doi.org/10.1007/s10439-012-0691-4"
  }
]
 

Download the RDF metadata as:  json-ld nt turtle xml License info

HOW TO GET THIS DATA PROGRAMMATICALLY:

JSON-LD is a popular format for linked data which is fully compatible with JSON.

curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1007/s10439-012-0691-4'

N-Triples is a line-based linked data format ideal for batch operations.

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/s10439-012-0691-4'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s10439-012-0691-4'

RDF/XML is a standard XML format for linked data.

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s10439-012-0691-4'


 

This table displays all metadata directly associated to this object as RDF triples.

265 TRIPLES      22 PREDICATES      109 URIs      96 LITERALS      19 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/s10439-012-0691-4 schema:about N00240f21a29046aeb2d1b597fc7ba6c6
2 N5853585562b547ff8f6863c1d8aec4ad
3 N62a231a39ba94db0849db8c519d6426f
4 N724c9bdd6ff84373af694a149d4e2f24
5 N79d46fd5f85d45f8ae661f3be60429ca
6 N92fcc625190e416a99bfc4e6a60c4207
7 N96a2f25583754031a16617e1314e97fe
8 N9921a5870e86440ebfb956023eab1790
9 N9d518092aaeb4f1a8638ec75a347b87d
10 Nb03a2dd7d2cb469fa3db6f187c82e3f2
11 Nb14c384222c6468cbbf8504dd9ff4b1d
12 Nedb2033292b0450fbd1bc124505b3a74
13 anzsrc-for:11
14 anzsrc-for:1103
15 schema:author N07c640aa82b14d319dbe1fa6d2906959
16 schema:citation sg:pub.10.1007/bf02368182
17 sg:pub.10.1007/s10439-010-0175-3
18 sg:pub.10.1023/b:abme.0000012746.31343.92
19 sg:pub.10.1114/1.1306342
20 sg:pub.10.1114/1.202
21 schema:datePublished 2012-11-22
22 schema:datePublishedReg 2012-11-22
23 schema:description An abdominal aortic aneurysm (AAA) carries one of the highest mortality rates among vascular diseases when it ruptures. To predict the role of surface curvature in rupture risk assessment, a discriminatory analysis of aneurysm geometry characterization was conducted. Data was obtained from 205 patient-specific computed tomography image sets corresponding to three AAA population subgroups: patients under surveillance, those that underwent elective repair of the aneurysm, and those with an emergent repair. Each AAA was reconstructed and their surface curvatures estimated using the biquintic Hermite finite element method. Local surface curvatures were processed into ten global curvature indices. Statistical analysis of the data revealed that the L2-norm of the Gaussian and Mean surface curvatures can be utilized as classifiers of the three AAA population subgroups. The application of statistical machine learning on the curvature features yielded 85.5% accuracy in classifying electively and emergent repaired AAAs, compared to a 68.9% accuracy obtained by using maximum aneurysm diameter alone. Such combination of non-invasive geometric quantification and statistical machine learning methods can be used in a clinical setting to assess the risk of rupture of aneurysms during regular patient follow-ups.
24 schema:genre article
25 schema:inLanguage en
26 schema:isAccessibleForFree true
27 schema:isPartOf N55dadec02f1a46ee97e542e984aa576e
28 N915a575adba149079e06946447c3855b
29 sg:journal.1087247
30 schema:keywords AAA population subgroups
31 Gaussian
32 Hermite finite element method
33 L2 norm
34 abdominal aortic aneurysm
35 accuracy
36 analysis
37 aneurysm diameter
38 aneurysm geometry characterization
39 aneurysms
40 aortic aneurysm
41 applications
42 assessment
43 biquintic Hermite finite element method
44 characterization
45 classifier
46 clinical setting
47 combination
48 comparative analysis
49 curvature
50 curvature features
51 curvature index
52 data
53 diameter
54 discriminatory analysis
55 disease
56 elective repair
57 element method
58 emergent repair
59 features
60 finite element method
61 geometric quantification
62 geometry characterization
63 global curvature indices
64 high mortality rate
65 image sets
66 index
67 local surface curvature
68 machine
69 maximum aneurysm diameter
70 mean surface curvature
71 method
72 mortality rate
73 non-invasive geometric quantification
74 patients
75 population subgroups
76 quantification
77 rate
78 regular patients
79 repair
80 risk
81 risk assessment
82 risk of rupture
83 role
84 rupture
85 rupture risk assessment
86 set
87 setting
88 statistical analysis
89 statistical machine
90 subgroups
91 such combinations
92 surface curvature
93 surveillance
94 tomography image sets
95 vascular disease
96 schema:name Surface Curvature as a Classifier of Abdominal Aortic Aneurysms: A Comparative Analysis
97 schema:pagination 562-576
98 schema:productId N11bbd8365ed747469120f19bc00b8cb8
99 N445b8f4fa9124e41bb76ebb59557df7b
100 Nc65604061f2b4a87b19160a58448b105
101 schema:sameAs https://app.dimensions.ai/details/publication/pub.1018683007
102 https://doi.org/10.1007/s10439-012-0691-4
103 schema:sdDatePublished 2021-11-01T18:16
104 schema:sdLicense https://scigraph.springernature.com/explorer/license/
105 schema:sdPublisher Nb90bcfb056de453a9b8e76dcf1fb31fb
106 schema:url https://doi.org/10.1007/s10439-012-0691-4
107 sgo:license sg:explorer/license/
108 sgo:sdDataset articles
109 rdf:type schema:ScholarlyArticle
110 N00240f21a29046aeb2d1b597fc7ba6c6 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
111 schema:name Computer Simulation
112 rdf:type schema:DefinedTerm
113 N07c640aa82b14d319dbe1fa6d2906959 rdf:first sg:person.0660627730.19
114 rdf:rest N4b88ec939fbe4558bc0f78f40197b061
115 N11bbd8365ed747469120f19bc00b8cb8 schema:name pubmed_id
116 schema:value 23180028
117 rdf:type schema:PropertyValue
118 N2d67f5cac73644b9ae49841358fa3b90 rdf:first sg:person.01246741357.55
119 rdf:rest N94d28ba0fef74a9ab9b12d281c8f6990
120 N3f6a39af28e54fdb9adc5f498bf813b9 rdf:first sg:person.0774466300.01
121 rdf:rest N2d67f5cac73644b9ae49841358fa3b90
122 N445b8f4fa9124e41bb76ebb59557df7b schema:name dimensions_id
123 schema:value pub.1018683007
124 rdf:type schema:PropertyValue
125 N4b88ec939fbe4558bc0f78f40197b061 rdf:first sg:person.011004600555.34
126 rdf:rest N7baee4a3f8364aae93eb4bc57009f77e
127 N55dadec02f1a46ee97e542e984aa576e schema:volumeNumber 41
128 rdf:type schema:PublicationVolume
129 N5853585562b547ff8f6863c1d8aec4ad schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
130 schema:name Imaging, Three-Dimensional
131 rdf:type schema:DefinedTerm
132 N62a231a39ba94db0849db8c519d6426f schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
133 schema:name Biomedical Engineering
134 rdf:type schema:DefinedTerm
135 N724c9bdd6ff84373af694a149d4e2f24 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
136 schema:name Artificial Intelligence
137 rdf:type schema:DefinedTerm
138 N79d46fd5f85d45f8ae661f3be60429ca schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
139 schema:name Radiographic Image Interpretation, Computer-Assisted
140 rdf:type schema:DefinedTerm
141 N7baee4a3f8364aae93eb4bc57009f77e rdf:first sg:person.0775056330.89
142 rdf:rest Na27b38fefcd54a5caf756722c2376342
143 N915a575adba149079e06946447c3855b schema:issueNumber 3
144 rdf:type schema:PublicationIssue
145 N92fcc625190e416a99bfc4e6a60c4207 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
146 schema:name Finite Element Analysis
147 rdf:type schema:DefinedTerm
148 N94d28ba0fef74a9ab9b12d281c8f6990 rdf:first sg:person.01046253173.69
149 rdf:rest rdf:nil
150 N96a2f25583754031a16617e1314e97fe schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
151 schema:name Models, Cardiovascular
152 rdf:type schema:DefinedTerm
153 N9921a5870e86440ebfb956023eab1790 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
154 schema:name Angiography
155 rdf:type schema:DefinedTerm
156 N9d518092aaeb4f1a8638ec75a347b87d schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
157 schema:name Humans
158 rdf:type schema:DefinedTerm
159 Na27b38fefcd54a5caf756722c2376342 rdf:first sg:person.07646135347.75
160 rdf:rest Nfdbe7953ca7242a2ba00e284cde288bb
161 Nb03a2dd7d2cb469fa3db6f187c82e3f2 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
162 schema:name Tomography, X-Ray Computed
163 rdf:type schema:DefinedTerm
164 Nb14c384222c6468cbbf8504dd9ff4b1d schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
165 schema:name Aortic Aneurysm, Abdominal
166 rdf:type schema:DefinedTerm
167 Nb90bcfb056de453a9b8e76dcf1fb31fb schema:name Springer Nature - SN SciGraph project
168 rdf:type schema:Organization
169 Nc65604061f2b4a87b19160a58448b105 schema:name doi
170 schema:value 10.1007/s10439-012-0691-4
171 rdf:type schema:PropertyValue
172 Nedb2033292b0450fbd1bc124505b3a74 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
173 schema:name Aortic Rupture
174 rdf:type schema:DefinedTerm
175 Nfdbe7953ca7242a2ba00e284cde288bb rdf:first sg:person.0764073425.77
176 rdf:rest N3f6a39af28e54fdb9adc5f498bf813b9
177 anzsrc-for:11 schema:inDefinedTermSet anzsrc-for:
178 schema:name Medical and Health Sciences
179 rdf:type schema:DefinedTerm
180 anzsrc-for:1103 schema:inDefinedTermSet anzsrc-for:
181 schema:name Clinical Sciences
182 rdf:type schema:DefinedTerm
183 sg:grant.2593674 http://pending.schema.org/fundedItem sg:pub.10.1007/s10439-012-0691-4
184 rdf:type schema:MonetaryGrant
185 sg:grant.2610386 http://pending.schema.org/fundedItem sg:pub.10.1007/s10439-012-0691-4
186 rdf:type schema:MonetaryGrant
187 sg:grant.2610466 http://pending.schema.org/fundedItem sg:pub.10.1007/s10439-012-0691-4
188 rdf:type schema:MonetaryGrant
189 sg:journal.1087247 schema:issn 0145-3068
190 1573-9686
191 schema:name Annals of Biomedical Engineering
192 schema:publisher Springer Nature
193 rdf:type schema:Periodical
194 sg:person.01046253173.69 schema:affiliation grid-institutes:grid.215352.2
195 schema:familyName Finol
196 schema:givenName Ender A.
197 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01046253173.69
198 rdf:type schema:Person
199 sg:person.011004600555.34 schema:affiliation grid-institutes:grid.147455.6
200 schema:familyName Zhu
201 schema:givenName Junjun
202 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011004600555.34
203 rdf:type schema:Person
204 sg:person.01246741357.55 schema:affiliation grid-institutes:grid.16753.36
205 schema:familyName Eskandari
206 schema:givenName Mark K.
207 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01246741357.55
208 rdf:type schema:Person
209 sg:person.0660627730.19 schema:affiliation grid-institutes:grid.147455.6
210 schema:familyName Lee
211 schema:givenName Kibaek
212 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0660627730.19
213 rdf:type schema:Person
214 sg:person.0764073425.77 schema:affiliation grid-institutes:grid.280673.8
215 schema:familyName Muluk
216 schema:givenName Satish C.
217 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0764073425.77
218 rdf:type schema:Person
219 sg:person.07646135347.75 schema:affiliation grid-institutes:grid.147455.6
220 schema:familyName Zhang
221 schema:givenName Yongjie
222 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.07646135347.75
223 rdf:type schema:Person
224 sg:person.0774466300.01 schema:affiliation grid-institutes:None
225 schema:familyName Chandra
226 schema:givenName Ankur
227 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0774466300.01
228 rdf:type schema:Person
229 sg:person.0775056330.89 schema:affiliation grid-institutes:grid.147455.6
230 schema:familyName Shum
231 schema:givenName Judy
232 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0775056330.89
233 rdf:type schema:Person
234 sg:pub.10.1007/bf02368182 schema:sameAs https://app.dimensions.ai/details/publication/pub.1029783791
235 https://doi.org/10.1007/bf02368182
236 rdf:type schema:CreativeWork
237 sg:pub.10.1007/s10439-010-0175-3 schema:sameAs https://app.dimensions.ai/details/publication/pub.1016650194
238 https://doi.org/10.1007/s10439-010-0175-3
239 rdf:type schema:CreativeWork
240 sg:pub.10.1023/b:abme.0000012746.31343.92 schema:sameAs https://app.dimensions.ai/details/publication/pub.1051822214
241 https://doi.org/10.1023/b:abme.0000012746.31343.92
242 rdf:type schema:CreativeWork
243 sg:pub.10.1114/1.1306342 schema:sameAs https://app.dimensions.ai/details/publication/pub.1018897655
244 https://doi.org/10.1114/1.1306342
245 rdf:type schema:CreativeWork
246 sg:pub.10.1114/1.202 schema:sameAs https://app.dimensions.ai/details/publication/pub.1031437224
247 https://doi.org/10.1114/1.202
248 rdf:type schema:CreativeWork
249 grid-institutes:None schema:alternateName Division of Vascular Surgery, Rochester Institute of Technology, University of Rochester School of Medicine, and Dentistry, 601 Elmwood Avenue, Box 652, 14642, Rochester, NY, USA
250 schema:name Division of Vascular Surgery, Rochester Institute of Technology, University of Rochester School of Medicine, and Dentistry, 601 Elmwood Avenue, Box 652, 14642, Rochester, NY, USA
251 rdf:type schema:Organization
252 grid-institutes:grid.147455.6 schema:alternateName Biomedical Engineering Department, Carnegie Mellon University, 5000 Forbes Avenue, 15213, Pittsburgh, PA, USA
253 Mechanical Engineering Department, Carnegie Mellon University, 5000 Forbes Avenue, 15213, Pittsburgh, PA, USA
254 schema:name Biomedical Engineering Department, Carnegie Mellon University, 5000 Forbes Avenue, 15213, Pittsburgh, PA, USA
255 Mechanical Engineering Department, Carnegie Mellon University, 5000 Forbes Avenue, 15213, Pittsburgh, PA, USA
256 rdf:type schema:Organization
257 grid-institutes:grid.16753.36 schema:alternateName Division of Vascular Surgery, Northwestern University Feinberg School of Medicine, 676 North Saint Clair Street, Suite #650, 60611, Chicago, IL, USA
258 schema:name Division of Vascular Surgery, Northwestern University Feinberg School of Medicine, 676 North Saint Clair Street, Suite #650, 60611, Chicago, IL, USA
259 rdf:type schema:Organization
260 grid-institutes:grid.215352.2 schema:alternateName Department of Biomedical Engineering, The University of Texas at San Antonio, One UTSA Circle, AET 1.360, 78249, San Antonio, TX, USA
261 schema:name Department of Biomedical Engineering, The University of Texas at San Antonio, One UTSA Circle, AET 1.360, 78249, San Antonio, TX, USA
262 rdf:type schema:Organization
263 grid-institutes:grid.280673.8 schema:alternateName Division of Vascular Surgery, Allegheny-Singer Research Institute, West Penn Allegheny Health System, 14th Floor, South Tower, 320 East North Avenue, 15212, Pittsburgh, PA, USA
264 schema:name Division of Vascular Surgery, Allegheny-Singer Research Institute, West Penn Allegheny Health System, 14th Floor, South Tower, 320 East North Avenue, 15212, Pittsburgh, PA, USA
265 rdf:type schema:Organization
 




Preview window. Press ESC to close (or click here)


...