Predicting Obstructive Hydronephrosis Based on Ultrasound Alone View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2020-09-29

AUTHORS

Lauren Erdman , Marta Skreta , Mandy Rickard , Carson McLean , Aziz Mezlini , Daniel T. Keefe , Anne-Sophie Blais , Michael Brudno , Armando Lorenzo , Anna Goldenberg

ABSTRACT

Prenatal hydronephrosis (HN) makes up nearly 30% of pediatric Urology Department visits, yet remains challenging to prognosticate without repeated ultrasounds and invasive clinical tests. We build a deep learning model, which uses still images from kidney ultrasound as input and predicts whether HN is due to an obstruction that will receive surgical intervention. We compare our custom convolutional neural network performance against other existing state-of-the-art models. Our best model predicts obstruction with an AUC of 0.93 and an AUPRC of 0.75 in a prospective test set of 89 patients (286 repeated kidney ultrasounds). We show that while maintaining a 5% false negative rate, our classifier identifies 58% of those who will have surgery due to obstruction yet received a functional renogram, indicating that this model could feasibly reduce the amount of testing done in more than half of non-surgical cases. This work demonstrates the ability of deep learning to predict obstructive HN with clinically relevant accuracy based on kidney ultrasound alone, without requiring other clinical variables as input. This algorithm has the potential to change clinical practice by stratifying HN patient risk, reducing repeated follow ups and invasive testing for less severe cases, and bringing more consistency to clinical management. More... »

PAGES

493-503

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-030-59716-0_47

DOI

http://dx.doi.org/10.1007/978-3-030-59716-0_47

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1131389642


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"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "Center for Computational Medicine, SickKids Hospital, Toronto, Canada", 
          "id": "http://www.grid.ac/institutes/grid.42327.30", 
          "name": [
            "Department of Computer Science, University of Toronto, Toronto, Canada", 
            "Program in Genetics and Genome Biology, Hospital for Sick Children, Toronto, Canada", 
            "Vector Institute, Toronto, Canada", 
            "Center for Computational Medicine, SickKids Hospital, Toronto, Canada"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Erdman", 
        "givenName": "Lauren", 
        "id": "sg:person.013621641365.54", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013621641365.54"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Center for Computational Medicine, SickKids Hospital, Toronto, Canada", 
          "id": "http://www.grid.ac/institutes/grid.42327.30", 
          "name": [
            "Department of Computer Science, University of Toronto, Toronto, Canada", 
            "Program in Genetics and Genome Biology, Hospital for Sick Children, Toronto, Canada", 
            "Center for Computational Medicine, SickKids Hospital, Toronto, Canada"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Skreta", 
        "givenName": "Marta", 
        "id": "sg:person.011511454163.31", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011511454163.31"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Department of Surgery, Division of Urology, Hospital for Sick Children, Toronto, Canada", 
          "id": "http://www.grid.ac/institutes/grid.42327.30", 
          "name": [
            "Department of Surgery, Division of Urology, Hospital for Sick Children, Toronto, Canada"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Rickard", 
        "givenName": "Mandy", 
        "id": "sg:person.010531576015.53", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010531576015.53"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Vector Institute, Toronto, Canada", 
          "id": "http://www.grid.ac/institutes/grid.494618.6", 
          "name": [
            "Department of Computer Science, University of Toronto, Toronto, Canada", 
            "Program in Genetics and Genome Biology, Hospital for Sick Children, Toronto, Canada", 
            "Vector Institute, Toronto, Canada"
          ], 
          "type": "Organization"
        }, 
        "familyName": "McLean", 
        "givenName": "Carson", 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Program in Genetics and Genome Biology, Hospital for Sick Children, Toronto, Canada", 
          "id": "http://www.grid.ac/institutes/grid.42327.30", 
          "name": [
            "Department of Computer Science, University of Toronto, Toronto, Canada", 
            "Program in Genetics and Genome Biology, Hospital for Sick Children, Toronto, Canada"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Mezlini", 
        "givenName": "Aziz", 
        "id": "sg:person.01071221437.82", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01071221437.82"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Department of Surgery, Division of Urology, Hospital for Sick Children, Toronto, Canada", 
          "id": "http://www.grid.ac/institutes/grid.42327.30", 
          "name": [
            "Department of Surgery, Division of Urology, Hospital for Sick Children, Toronto, Canada"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Keefe", 
        "givenName": "Daniel T.", 
        "id": "sg:person.0764357575.54", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0764357575.54"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Department of Surgery, Division of Urology, Hospital for Sick Children, Toronto, Canada", 
          "id": "http://www.grid.ac/institutes/grid.42327.30", 
          "name": [
            "Department of Surgery, Division of Urology, Hospital for Sick Children, Toronto, Canada"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Blais", 
        "givenName": "Anne-Sophie", 
        "id": "sg:person.0607075703.17", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0607075703.17"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Center for Computational Medicine, SickKids Hospital, Toronto, Canada", 
          "id": "http://www.grid.ac/institutes/grid.42327.30", 
          "name": [
            "Department of Computer Science, University of Toronto, Toronto, Canada", 
            "Program in Genetics and Genome Biology, Hospital for Sick Children, Toronto, Canada", 
            "Vector Institute, Toronto, Canada", 
            "Center for Computational Medicine, SickKids Hospital, Toronto, Canada"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Brudno", 
        "givenName": "Michael", 
        "id": "sg:person.01253563237.25", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01253563237.25"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Department of Surgery, Division of Urology, Hospital for Sick Children, Toronto, Canada", 
          "id": "http://www.grid.ac/institutes/grid.42327.30", 
          "name": [
            "Department of Surgery, Division of Urology, Hospital for Sick Children, Toronto, Canada"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Lorenzo", 
        "givenName": "Armando", 
        "id": "sg:person.0715132275.26", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0715132275.26"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Child and Brain Development, Canadian Institute for Advanced Research (CIFAR), Toronto, Canada", 
          "id": "http://www.grid.ac/institutes/grid.440050.5", 
          "name": [
            "Department of Computer Science, University of Toronto, Toronto, Canada", 
            "Program in Genetics and Genome Biology, Hospital for Sick Children, Toronto, Canada", 
            "Vector Institute, Toronto, Canada", 
            "Child and Brain Development, Canadian Institute for Advanced Research (CIFAR), Toronto, Canada"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Goldenberg", 
        "givenName": "Anna", 
        "id": "sg:person.0760316313.65", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0760316313.65"
        ], 
        "type": "Person"
      }
    ], 
    "datePublished": "2020-09-29", 
    "datePublishedReg": "2020-09-29", 
    "description": "Prenatal hydronephrosis (HN) makes up nearly 30% of pediatric Urology Department visits, yet remains challenging to prognosticate without repeated ultrasounds and invasive clinical tests. We build a deep learning model, which uses still images from kidney ultrasound as input and predicts whether HN is due to an obstruction that will receive surgical intervention. We compare our custom convolutional neural network performance against other existing state-of-the-art models. Our best model predicts obstruction with an AUC of 0.93 and an AUPRC of 0.75 in a prospective test set of 89 patients (286 repeated kidney ultrasounds). We show that while maintaining a 5% false negative rate, our classifier identifies 58% of those who will have surgery due to obstruction yet received a functional renogram, indicating that this model could feasibly reduce the amount of testing done in more than half of non-surgical cases. This work demonstrates the ability of deep learning to predict obstructive HN with clinically relevant accuracy based on kidney ultrasound alone, without requiring other clinical variables as input. This algorithm has the potential to change clinical practice by stratifying HN patient risk, reducing repeated follow ups and invasive testing for less severe cases, and bringing more consistency to clinical management.", 
    "editor": [
      {
        "familyName": "Martel", 
        "givenName": "Anne L.", 
        "type": "Person"
      }, 
      {
        "familyName": "Abolmaesumi", 
        "givenName": "Purang", 
        "type": "Person"
      }, 
      {
        "familyName": "Stoyanov", 
        "givenName": "Danail", 
        "type": "Person"
      }, 
      {
        "familyName": "Mateus", 
        "givenName": "Diana", 
        "type": "Person"
      }, 
      {
        "familyName": "Zuluaga", 
        "givenName": "Maria A.", 
        "type": "Person"
      }, 
      {
        "familyName": "Zhou", 
        "givenName": "S. Kevin", 
        "type": "Person"
      }, 
      {
        "familyName": "Racoceanu", 
        "givenName": "Daniel", 
        "type": "Person"
      }, 
      {
        "familyName": "Joskowicz", 
        "givenName": "Leo", 
        "type": "Person"
      }
    ], 
    "genre": "chapter", 
    "id": "sg:pub.10.1007/978-3-030-59716-0_47", 
    "inLanguage": "en", 
    "isAccessibleForFree": false, 
    "isPartOf": {
      "isbn": [
        "978-3-030-59715-3", 
        "978-3-030-59716-0"
      ], 
      "name": "Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2020", 
      "type": "Book"
    }, 
    "keywords": [
      "obstructive hydronephrosis", 
      "invasive clinical tests", 
      "non-surgical cases", 
      "department visits", 
      "kidney ultrasound", 
      "prenatal hydronephrosis", 
      "surgical intervention", 
      "clinical variables", 
      "clinical management", 
      "patient risk", 
      "Follow-up", 
      "invasive testing", 
      "hydronephrosis", 
      "severe cases", 
      "clinical practice", 
      "clinical tests", 
      "false negative rate", 
      "prospective test set", 
      "obstruction", 
      "ultrasound", 
      "negative rate", 
      "patients", 
      "surgery", 
      "renogram", 
      "kidney", 
      "visits", 
      "AUC", 
      "intervention", 
      "risk", 
      "testing", 
      "relevant accuracy", 
      "cases", 
      "management", 
      "half", 
      "AUPRC", 
      "up", 
      "rate", 
      "test", 
      "more consistency", 
      "best model", 
      "practice", 
      "ability", 
      "variables", 
      "model", 
      "potential", 
      "convolutional neural network performance", 
      "consistency", 
      "amount", 
      "test set", 
      "amount of testing", 
      "input", 
      "state", 
      "images", 
      "accuracy", 
      "deep learning models", 
      "learning", 
      "work", 
      "neural network performance", 
      "performance", 
      "learning model", 
      "set", 
      "deep learning", 
      "classifier", 
      "algorithm", 
      "art models", 
      "network performance", 
      "pediatric Urology Department visits", 
      "Urology Department visits", 
      "custom convolutional neural network performance", 
      "functional renogram", 
      "HN patient risk"
    ], 
    "name": "Predicting Obstructive Hydronephrosis Based on Ultrasound Alone", 
    "pagination": "493-503", 
    "productId": [
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1131389642"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/978-3-030-59716-0_47"
        ]
      }
    ], 
    "publisher": {
      "name": "Springer Nature", 
      "type": "Organisation"
    }, 
    "sameAs": [
      "https://doi.org/10.1007/978-3-030-59716-0_47", 
      "https://app.dimensions.ai/details/publication/pub.1131389642"
    ], 
    "sdDataset": "chapters", 
    "sdDatePublished": "2022-01-01T19:20", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-springernature-scigraph/baseset/20220101/entities/gbq_results/chapter/chapter_359.jsonl", 
    "type": "Chapter", 
    "url": "https://doi.org/10.1007/978-3-030-59716-0_47"
  }
]
 

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.10.1007/978-3-030-59716-0_47'

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

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/978-3-030-59716-0_47'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/978-3-030-59716-0_47'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/978-3-030-59716-0_47'


 

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

245 TRIPLES      23 PREDICATES      96 URIs      89 LITERALS      7 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/978-3-030-59716-0_47 schema:about anzsrc-for:11
2 anzsrc-for:1103
3 schema:author N2b6200b947f2437d8cfbd5b96506fdb8
4 schema:datePublished 2020-09-29
5 schema:datePublishedReg 2020-09-29
6 schema:description Prenatal hydronephrosis (HN) makes up nearly 30% of pediatric Urology Department visits, yet remains challenging to prognosticate without repeated ultrasounds and invasive clinical tests. We build a deep learning model, which uses still images from kidney ultrasound as input and predicts whether HN is due to an obstruction that will receive surgical intervention. We compare our custom convolutional neural network performance against other existing state-of-the-art models. Our best model predicts obstruction with an AUC of 0.93 and an AUPRC of 0.75 in a prospective test set of 89 patients (286 repeated kidney ultrasounds). We show that while maintaining a 5% false negative rate, our classifier identifies 58% of those who will have surgery due to obstruction yet received a functional renogram, indicating that this model could feasibly reduce the amount of testing done in more than half of non-surgical cases. This work demonstrates the ability of deep learning to predict obstructive HN with clinically relevant accuracy based on kidney ultrasound alone, without requiring other clinical variables as input. This algorithm has the potential to change clinical practice by stratifying HN patient risk, reducing repeated follow ups and invasive testing for less severe cases, and bringing more consistency to clinical management.
7 schema:editor N2122f8c5a7804967969d5c3fea683b6f
8 schema:genre chapter
9 schema:inLanguage en
10 schema:isAccessibleForFree false
11 schema:isPartOf N62dd6311aa0c4c02a170e7a1a47666d0
12 schema:keywords AUC
13 AUPRC
14 Follow-up
15 HN patient risk
16 Urology Department visits
17 ability
18 accuracy
19 algorithm
20 amount
21 amount of testing
22 art models
23 best model
24 cases
25 classifier
26 clinical management
27 clinical practice
28 clinical tests
29 clinical variables
30 consistency
31 convolutional neural network performance
32 custom convolutional neural network performance
33 deep learning
34 deep learning models
35 department visits
36 false negative rate
37 functional renogram
38 half
39 hydronephrosis
40 images
41 input
42 intervention
43 invasive clinical tests
44 invasive testing
45 kidney
46 kidney ultrasound
47 learning
48 learning model
49 management
50 model
51 more consistency
52 negative rate
53 network performance
54 neural network performance
55 non-surgical cases
56 obstruction
57 obstructive hydronephrosis
58 patient risk
59 patients
60 pediatric Urology Department visits
61 performance
62 potential
63 practice
64 prenatal hydronephrosis
65 prospective test set
66 rate
67 relevant accuracy
68 renogram
69 risk
70 set
71 severe cases
72 state
73 surgery
74 surgical intervention
75 test
76 test set
77 testing
78 ultrasound
79 up
80 variables
81 visits
82 work
83 schema:name Predicting Obstructive Hydronephrosis Based on Ultrasound Alone
84 schema:pagination 493-503
85 schema:productId N5c4fd8efa8b74890ac0cc28379a9b394
86 N931893ef97934a28977e772448771c8b
87 schema:publisher N3d8afdca22bb4a48ab5c577d12272661
88 schema:sameAs https://app.dimensions.ai/details/publication/pub.1131389642
89 https://doi.org/10.1007/978-3-030-59716-0_47
90 schema:sdDatePublished 2022-01-01T19:20
91 schema:sdLicense https://scigraph.springernature.com/explorer/license/
92 schema:sdPublisher Ned3c4039d4b7475cb52ea6eca80ee517
93 schema:url https://doi.org/10.1007/978-3-030-59716-0_47
94 sgo:license sg:explorer/license/
95 sgo:sdDataset chapters
96 rdf:type schema:Chapter
97 N084c51ffd1ba409aa396141c47e76d51 schema:familyName Racoceanu
98 schema:givenName Daniel
99 rdf:type schema:Person
100 N0d2bb4f5c2044577b03b6b42eba07cc8 rdf:first sg:person.0764357575.54
101 rdf:rest Nde77787c9621498dbad42b47954d763d
102 N10e64af5953d4ce6a6ea708f9859bfe6 schema:affiliation grid-institutes:grid.494618.6
103 schema:familyName McLean
104 schema:givenName Carson
105 rdf:type schema:Person
106 N1669fae1012445bdaee261fc67798050 schema:familyName Stoyanov
107 schema:givenName Danail
108 rdf:type schema:Person
109 N19a68c3274974bc5937f081830debe18 rdf:first sg:person.010531576015.53
110 rdf:rest N2bf492ac1c914b8d94209b8efa81f809
111 N2122f8c5a7804967969d5c3fea683b6f rdf:first N696e94675af849ee82cd3a1539287383
112 rdf:rest N797351c255254d9eb5b997bf0d466f56
113 N22d8a0eacbcc4793a464cf74b3ca0c19 schema:familyName Zhou
114 schema:givenName S. Kevin
115 rdf:type schema:Person
116 N2a67c3be7f0a4c8f8515165f06720915 rdf:first N7436f6aea3c2418eb2e74adeb28aff11
117 rdf:rest N9c5cc007c00b4bd88ff07f0e26fc7cce
118 N2b6200b947f2437d8cfbd5b96506fdb8 rdf:first sg:person.013621641365.54
119 rdf:rest N6a36a88a0ae54033ad8f5837166f7e74
120 N2bf492ac1c914b8d94209b8efa81f809 rdf:first N10e64af5953d4ce6a6ea708f9859bfe6
121 rdf:rest N96976154481d4eaf9587fbf89643a376
122 N31416972a1f1489aa5a78dfb75fb91d1 rdf:first N084c51ffd1ba409aa396141c47e76d51
123 rdf:rest Na705d4a7230e4fc4a245ab0e8068e25d
124 N34708b95c7e3435e888101d60e742227 rdf:first N1669fae1012445bdaee261fc67798050
125 rdf:rest N2a67c3be7f0a4c8f8515165f06720915
126 N3d8afdca22bb4a48ab5c577d12272661 schema:name Springer Nature
127 rdf:type schema:Organisation
128 N564b100fef4647ffb01ad489c00408be schema:familyName Joskowicz
129 schema:givenName Leo
130 rdf:type schema:Person
131 N5c4fd8efa8b74890ac0cc28379a9b394 schema:name dimensions_id
132 schema:value pub.1131389642
133 rdf:type schema:PropertyValue
134 N610bf9aba73142478489e8651531b78d schema:familyName Zuluaga
135 schema:givenName Maria A.
136 rdf:type schema:Person
137 N62dd6311aa0c4c02a170e7a1a47666d0 schema:isbn 978-3-030-59715-3
138 978-3-030-59716-0
139 schema:name Medical Image Computing and Computer Assisted Intervention – MICCAI 2020
140 rdf:type schema:Book
141 N696e94675af849ee82cd3a1539287383 schema:familyName Martel
142 schema:givenName Anne L.
143 rdf:type schema:Person
144 N6a36a88a0ae54033ad8f5837166f7e74 rdf:first sg:person.011511454163.31
145 rdf:rest N19a68c3274974bc5937f081830debe18
146 N7436f6aea3c2418eb2e74adeb28aff11 schema:familyName Mateus
147 schema:givenName Diana
148 rdf:type schema:Person
149 N797351c255254d9eb5b997bf0d466f56 rdf:first Nde22d4d6e66f4b9dbd8f305f80919d37
150 rdf:rest N34708b95c7e3435e888101d60e742227
151 N931893ef97934a28977e772448771c8b schema:name doi
152 schema:value 10.1007/978-3-030-59716-0_47
153 rdf:type schema:PropertyValue
154 N96976154481d4eaf9587fbf89643a376 rdf:first sg:person.01071221437.82
155 rdf:rest N0d2bb4f5c2044577b03b6b42eba07cc8
156 N9c5cc007c00b4bd88ff07f0e26fc7cce rdf:first N610bf9aba73142478489e8651531b78d
157 rdf:rest Nd7510ef332d34db99efd0a096602597c
158 Na705d4a7230e4fc4a245ab0e8068e25d rdf:first N564b100fef4647ffb01ad489c00408be
159 rdf:rest rdf:nil
160 Nad2e02fa163640a5ad8e5dc1b190b7b6 rdf:first sg:person.0715132275.26
161 rdf:rest Nb58db6852605456c8fc07312ab45c140
162 Nb58db6852605456c8fc07312ab45c140 rdf:first sg:person.0760316313.65
163 rdf:rest rdf:nil
164 Nd7510ef332d34db99efd0a096602597c rdf:first N22d8a0eacbcc4793a464cf74b3ca0c19
165 rdf:rest N31416972a1f1489aa5a78dfb75fb91d1
166 Nde22d4d6e66f4b9dbd8f305f80919d37 schema:familyName Abolmaesumi
167 schema:givenName Purang
168 rdf:type schema:Person
169 Nde77787c9621498dbad42b47954d763d rdf:first sg:person.0607075703.17
170 rdf:rest Nfc984a24ede74a73a6a8a0dc09e61035
171 Ned3c4039d4b7475cb52ea6eca80ee517 schema:name Springer Nature - SN SciGraph project
172 rdf:type schema:Organization
173 Nfc984a24ede74a73a6a8a0dc09e61035 rdf:first sg:person.01253563237.25
174 rdf:rest Nad2e02fa163640a5ad8e5dc1b190b7b6
175 anzsrc-for:11 schema:inDefinedTermSet anzsrc-for:
176 schema:name Medical and Health Sciences
177 rdf:type schema:DefinedTerm
178 anzsrc-for:1103 schema:inDefinedTermSet anzsrc-for:
179 schema:name Clinical Sciences
180 rdf:type schema:DefinedTerm
181 sg:person.010531576015.53 schema:affiliation grid-institutes:grid.42327.30
182 schema:familyName Rickard
183 schema:givenName Mandy
184 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010531576015.53
185 rdf:type schema:Person
186 sg:person.01071221437.82 schema:affiliation grid-institutes:grid.42327.30
187 schema:familyName Mezlini
188 schema:givenName Aziz
189 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01071221437.82
190 rdf:type schema:Person
191 sg:person.011511454163.31 schema:affiliation grid-institutes:grid.42327.30
192 schema:familyName Skreta
193 schema:givenName Marta
194 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011511454163.31
195 rdf:type schema:Person
196 sg:person.01253563237.25 schema:affiliation grid-institutes:grid.42327.30
197 schema:familyName Brudno
198 schema:givenName Michael
199 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01253563237.25
200 rdf:type schema:Person
201 sg:person.013621641365.54 schema:affiliation grid-institutes:grid.42327.30
202 schema:familyName Erdman
203 schema:givenName Lauren
204 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013621641365.54
205 rdf:type schema:Person
206 sg:person.0607075703.17 schema:affiliation grid-institutes:grid.42327.30
207 schema:familyName Blais
208 schema:givenName Anne-Sophie
209 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0607075703.17
210 rdf:type schema:Person
211 sg:person.0715132275.26 schema:affiliation grid-institutes:grid.42327.30
212 schema:familyName Lorenzo
213 schema:givenName Armando
214 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0715132275.26
215 rdf:type schema:Person
216 sg:person.0760316313.65 schema:affiliation grid-institutes:grid.440050.5
217 schema:familyName Goldenberg
218 schema:givenName Anna
219 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0760316313.65
220 rdf:type schema:Person
221 sg:person.0764357575.54 schema:affiliation grid-institutes:grid.42327.30
222 schema:familyName Keefe
223 schema:givenName Daniel T.
224 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0764357575.54
225 rdf:type schema:Person
226 grid-institutes:grid.42327.30 schema:alternateName Center for Computational Medicine, SickKids Hospital, Toronto, Canada
227 Department of Surgery, Division of Urology, Hospital for Sick Children, Toronto, Canada
228 Program in Genetics and Genome Biology, Hospital for Sick Children, Toronto, Canada
229 schema:name Center for Computational Medicine, SickKids Hospital, Toronto, Canada
230 Department of Computer Science, University of Toronto, Toronto, Canada
231 Department of Surgery, Division of Urology, Hospital for Sick Children, Toronto, Canada
232 Program in Genetics and Genome Biology, Hospital for Sick Children, Toronto, Canada
233 Vector Institute, Toronto, Canada
234 rdf:type schema:Organization
235 grid-institutes:grid.440050.5 schema:alternateName Child and Brain Development, Canadian Institute for Advanced Research (CIFAR), Toronto, Canada
236 schema:name Child and Brain Development, Canadian Institute for Advanced Research (CIFAR), Toronto, Canada
237 Department of Computer Science, University of Toronto, Toronto, Canada
238 Program in Genetics and Genome Biology, Hospital for Sick Children, Toronto, Canada
239 Vector Institute, Toronto, Canada
240 rdf:type schema:Organization
241 grid-institutes:grid.494618.6 schema:alternateName Vector Institute, Toronto, Canada
242 schema:name Department of Computer Science, University of Toronto, Toronto, Canada
243 Program in Genetics and Genome Biology, Hospital for Sick Children, Toronto, Canada
244 Vector Institute, Toronto, Canada
245 rdf:type schema:Organization
 




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


...