A case-control clinical trial on the diagnostic performance for Alzheimer’s Disease of a deep learning-based classification system using brain magnetic resonance imaging Article Swipe
Jong Bin Bae
,
Subin Lee
,
Hyunwoo Oh
,
Jinkyeong Sung
,
Dongsoo Lee
,
Jiwon Han
,
Jun Sung Kim
,
Jae Hyoung Kim
,
Sang Eun Kim
,
Ki Woong Kim
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.21203/rs.3.rs-754254/v1
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.21203/rs.3.rs-754254/v1
Objective To investigate diagnostic performance of a deep learning-based classification system using structural brain MRI (DLCS) for Alzheimer’s disease (AD). Methods A single-center, case-control clinical trial was conducted. T1-weighted brain MRI scans of 188 patients with mild cognitive impairment or dementia due to AD and 162 cognitively normal controls were retrospectively collected. The patients were amyloid beta (Aβ)-positive, whereas the controls were Aβ-negative, on 18F-florbetaben positron emission tomography. Sensitivity, specificity, positive predictive value, negative predictive value, and area under the receiver operating characteristic curve were calculated to evaluate the performance of DLCS in the classification of Aβ-positive AD patients from Aβ-negative controls. Results The DLCS was excellent in classifying AD patients from normal controls; sensitivity, specificity, positive predictive value, negative predictive value, and area under the receiver operating characteristic curve for AD were 85.6% (95%CI, 79.8–90), 90.1% (95%CI, 84.5–94.2), 91.0% (95%CI, 86.3–94.1), 84.4% (95%CI, 79.2–88.5), and 0.937 (95%CI, 0.911–0.963), respectively. Conclusion The DLCS shows promise in clinical settings where it may improve early detection of AD in any individual who has undergone an MRI scan regardless of purpose. Trial registration: Korean Clinical Trials Registry, KCT0004758. Registered 21 February 2020, https://cris.nih.go.kr/cris/search/detailSearch.do/17665.
Related Topics
Concepts
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.21203/rs.3.rs-754254/v1
- https://www.researchsquare.com/article/rs-754254/latest.pdf
- OA Status
- green
- Cited By
- 1
- References
- 19
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3187374399
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W3187374399Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.21203/rs.3.rs-754254/v1Digital Object Identifier
- Title
-
A case-control clinical trial on the diagnostic performance for Alzheimer’s Disease of a deep learning-based classification system using brain magnetic resonance imagingWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-08-05Full publication date if available
- Authors
-
Jong Bin Bae, Subin Lee, Hyunwoo Oh, Jinkyeong Sung, Dongsoo Lee, Jiwon Han, Jun Sung Kim, Jae Hyoung Kim, Sang Eun Kim, Ki Woong KimList of authors in order
- Landing page
-
https://doi.org/10.21203/rs.3.rs-754254/v1Publisher landing page
- PDF URL
-
https://www.researchsquare.com/article/rs-754254/latest.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://www.researchsquare.com/article/rs-754254/latest.pdfDirect OA link when available
- Concepts
-
Magnetic resonance imaging, Disease, Neuroimaging, Control (management), Medicine, Neuroscience, Artificial intelligence, Psychology, Computer science, Radiology, PathologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
1Total citation count in OpenAlex
- Citations by year (recent)
-
2022: 1Per-year citation counts (last 5 years)
- References (count)
-
19Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W3187374399 |
|---|---|
| doi | https://doi.org/10.21203/rs.3.rs-754254/v1 |
| ids.doi | https://doi.org/10.21203/rs.3.rs-754254/v1 |
| ids.mag | 3187374399 |
| ids.openalex | https://openalex.org/W3187374399 |
| fwci | 0.12670818 |
| type | preprint |
| title | A case-control clinical trial on the diagnostic performance for Alzheimer’s Disease of a deep learning-based classification system using brain magnetic resonance imaging |
| biblio.issue | |
| biblio.volume | |
| biblio.last_page | |
| biblio.first_page | |
| topics[0].id | https://openalex.org/T12702 |
| topics[0].field.id | https://openalex.org/fields/28 |
| topics[0].field.display_name | Neuroscience |
| topics[0].score | 0.9975000023841858 |
| topics[0].domain.id | https://openalex.org/domains/1 |
| topics[0].domain.display_name | Life Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/2808 |
| topics[0].subfield.display_name | Neurology |
| topics[0].display_name | Brain Tumor Detection and Classification |
| topics[1].id | https://openalex.org/T10009 |
| topics[1].field.id | https://openalex.org/fields/27 |
| topics[1].field.display_name | Medicine |
| topics[1].score | 0.9948999881744385 |
| topics[1].domain.id | https://openalex.org/domains/4 |
| topics[1].domain.display_name | Health Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/2738 |
| topics[1].subfield.display_name | Psychiatry and Mental health |
| topics[1].display_name | Dementia and Cognitive Impairment Research |
| topics[2].id | https://openalex.org/T10241 |
| topics[2].field.id | https://openalex.org/fields/28 |
| topics[2].field.display_name | Neuroscience |
| topics[2].score | 0.9837999939918518 |
| topics[2].domain.id | https://openalex.org/domains/1 |
| topics[2].domain.display_name | Life Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/2805 |
| topics[2].subfield.display_name | Cognitive Neuroscience |
| topics[2].display_name | Functional Brain Connectivity Studies |
| is_xpac | False |
| apc_list | |
| apc_paid | |
| concepts[0].id | https://openalex.org/C143409427 |
| concepts[0].level | 2 |
| concepts[0].score | 0.6963528990745544 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q161238 |
| concepts[0].display_name | Magnetic resonance imaging |
| concepts[1].id | https://openalex.org/C2779134260 |
| concepts[1].level | 2 |
| concepts[1].score | 0.5748295187950134 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q12136 |
| concepts[1].display_name | Disease |
| concepts[2].id | https://openalex.org/C58693492 |
| concepts[2].level | 2 |
| concepts[2].score | 0.4908159673213959 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q551875 |
| concepts[2].display_name | Neuroimaging |
| concepts[3].id | https://openalex.org/C2775924081 |
| concepts[3].level | 2 |
| concepts[3].score | 0.4546303451061249 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q55608371 |
| concepts[3].display_name | Control (management) |
| concepts[4].id | https://openalex.org/C71924100 |
| concepts[4].level | 0 |
| concepts[4].score | 0.4172561764717102 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q11190 |
| concepts[4].display_name | Medicine |
| concepts[5].id | https://openalex.org/C169760540 |
| concepts[5].level | 1 |
| concepts[5].score | 0.40388551354408264 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q207011 |
| concepts[5].display_name | Neuroscience |
| concepts[6].id | https://openalex.org/C154945302 |
| concepts[6].level | 1 |
| concepts[6].score | 0.38975995779037476 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[6].display_name | Artificial intelligence |
| concepts[7].id | https://openalex.org/C15744967 |
| concepts[7].level | 0 |
| concepts[7].score | 0.3387172818183899 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q9418 |
| concepts[7].display_name | Psychology |
| concepts[8].id | https://openalex.org/C41008148 |
| concepts[8].level | 0 |
| concepts[8].score | 0.28154271841049194 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[8].display_name | Computer science |
| concepts[9].id | https://openalex.org/C126838900 |
| concepts[9].level | 1 |
| concepts[9].score | 0.22310996055603027 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q77604 |
| concepts[9].display_name | Radiology |
| concepts[10].id | https://openalex.org/C142724271 |
| concepts[10].level | 1 |
| concepts[10].score | 0.2077312469482422 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q7208 |
| concepts[10].display_name | Pathology |
| keywords[0].id | https://openalex.org/keywords/magnetic-resonance-imaging |
| keywords[0].score | 0.6963528990745544 |
| keywords[0].display_name | Magnetic resonance imaging |
| keywords[1].id | https://openalex.org/keywords/disease |
| keywords[1].score | 0.5748295187950134 |
| keywords[1].display_name | Disease |
| keywords[2].id | https://openalex.org/keywords/neuroimaging |
| keywords[2].score | 0.4908159673213959 |
| keywords[2].display_name | Neuroimaging |
| keywords[3].id | https://openalex.org/keywords/control |
| keywords[3].score | 0.4546303451061249 |
| keywords[3].display_name | Control (management) |
| keywords[4].id | https://openalex.org/keywords/medicine |
| keywords[4].score | 0.4172561764717102 |
| keywords[4].display_name | Medicine |
| keywords[5].id | https://openalex.org/keywords/neuroscience |
| keywords[5].score | 0.40388551354408264 |
| keywords[5].display_name | Neuroscience |
| keywords[6].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[6].score | 0.38975995779037476 |
| keywords[6].display_name | Artificial intelligence |
| keywords[7].id | https://openalex.org/keywords/psychology |
| keywords[7].score | 0.3387172818183899 |
| keywords[7].display_name | Psychology |
| keywords[8].id | https://openalex.org/keywords/computer-science |
| keywords[8].score | 0.28154271841049194 |
| keywords[8].display_name | Computer science |
| keywords[9].id | https://openalex.org/keywords/radiology |
| keywords[9].score | 0.22310996055603027 |
| keywords[9].display_name | Radiology |
| keywords[10].id | https://openalex.org/keywords/pathology |
| keywords[10].score | 0.2077312469482422 |
| keywords[10].display_name | Pathology |
| language | en |
| locations[0].id | doi:10.21203/rs.3.rs-754254/v1 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S4306402450 |
| locations[0].source.issn | |
| locations[0].source.type | repository |
| locations[0].source.is_oa | False |
| locations[0].source.issn_l | |
| locations[0].source.is_core | False |
| locations[0].source.is_in_doaj | False |
| locations[0].source.display_name | Research Square (Research Square) |
| locations[0].source.host_organization | https://openalex.org/I4210096694 |
| locations[0].source.host_organization_name | Research Square (United States) |
| locations[0].source.host_organization_lineage | https://openalex.org/I4210096694 |
| locations[0].license | cc-by |
| locations[0].pdf_url | https://www.researchsquare.com/article/rs-754254/latest.pdf |
| locations[0].version | acceptedVersion |
| locations[0].raw_type | posted-content |
| locations[0].license_id | https://openalex.org/licenses/cc-by |
| locations[0].is_accepted | True |
| locations[0].is_published | False |
| locations[0].raw_source_name | |
| locations[0].landing_page_url | https://doi.org/10.21203/rs.3.rs-754254/v1 |
| indexed_in | crossref |
| authorships[0].author.id | https://openalex.org/A5027005127 |
| authorships[0].author.orcid | https://orcid.org/0000-0002-3913-1011 |
| authorships[0].author.display_name | Jong Bin Bae |
| authorships[0].countries | KR |
| authorships[0].affiliations[0].institution_ids | https://openalex.org/I2803058125 |
| authorships[0].affiliations[0].raw_affiliation_string | Seoul National University Bundang Hospital |
| authorships[0].institutions[0].id | https://openalex.org/I2803058125 |
| authorships[0].institutions[0].ror | https://ror.org/00cb3km46 |
| authorships[0].institutions[0].type | healthcare |
| authorships[0].institutions[0].lineage | https://openalex.org/I2802835388, https://openalex.org/I2803058125 |
| authorships[0].institutions[0].country_code | KR |
| authorships[0].institutions[0].display_name | Seoul National University Bundang Hospital |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Jong Bin Bae |
| authorships[0].is_corresponding | False |
| authorships[0].raw_affiliation_strings | Seoul National University Bundang Hospital |
| authorships[1].author.id | https://openalex.org/A5106613924 |
| authorships[1].author.orcid | https://orcid.org/0000-0001-7583-6468 |
| authorships[1].author.display_name | Subin Lee |
| authorships[1].countries | KR |
| authorships[1].affiliations[0].institution_ids | https://openalex.org/I139264467 |
| authorships[1].affiliations[0].raw_affiliation_string | Seoul National University |
| authorships[1].institutions[0].id | https://openalex.org/I139264467 |
| authorships[1].institutions[0].ror | https://ror.org/04h9pn542 |
| authorships[1].institutions[0].type | education |
| authorships[1].institutions[0].lineage | https://openalex.org/I139264467 |
| authorships[1].institutions[0].country_code | KR |
| authorships[1].institutions[0].display_name | Seoul National University |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Subin Lee |
| authorships[1].is_corresponding | False |
| authorships[1].raw_affiliation_strings | Seoul National University |
| authorships[2].author.id | https://openalex.org/A5103173471 |
| authorships[2].author.orcid | https://orcid.org/0000-0002-0952-2046 |
| authorships[2].author.display_name | Hyunwoo Oh |
| authorships[2].affiliations[0].raw_affiliation_string | Vuno Inc |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Hyunwoo Oh |
| authorships[2].is_corresponding | False |
| authorships[2].raw_affiliation_strings | Vuno Inc |
| authorships[3].author.id | https://openalex.org/A5047691691 |
| authorships[3].author.orcid | https://orcid.org/0000-0003-3546-6081 |
| authorships[3].author.display_name | Jinkyeong Sung |
| authorships[3].affiliations[0].raw_affiliation_string | Vuno Inc |
| authorships[3].author_position | middle |
| authorships[3].raw_author_name | Jinkyeong Sung |
| authorships[3].is_corresponding | False |
| authorships[3].raw_affiliation_strings | Vuno Inc |
| authorships[4].author.id | https://openalex.org/A5101748622 |
| authorships[4].author.orcid | https://orcid.org/0000-0002-7057-745X |
| authorships[4].author.display_name | Dongsoo Lee |
| authorships[4].affiliations[0].raw_affiliation_string | Vuno Inc |
| authorships[4].author_position | middle |
| authorships[4].raw_author_name | Dongsoo Lee |
| authorships[4].is_corresponding | False |
| authorships[4].raw_affiliation_strings | Vuno Inc |
| authorships[5].author.id | https://openalex.org/A5101464966 |
| authorships[5].author.orcid | https://orcid.org/0000-0002-7828-618X |
| authorships[5].author.display_name | Jiwon Han |
| authorships[5].countries | KR |
| authorships[5].affiliations[0].institution_ids | https://openalex.org/I2803058125 |
| authorships[5].affiliations[0].raw_affiliation_string | Seoul National University Bundang Hospital |
| authorships[5].institutions[0].id | https://openalex.org/I2803058125 |
| authorships[5].institutions[0].ror | https://ror.org/00cb3km46 |
| authorships[5].institutions[0].type | healthcare |
| authorships[5].institutions[0].lineage | https://openalex.org/I2802835388, https://openalex.org/I2803058125 |
| authorships[5].institutions[0].country_code | KR |
| authorships[5].institutions[0].display_name | Seoul National University Bundang Hospital |
| authorships[5].author_position | middle |
| authorships[5].raw_author_name | Jiwon Han |
| authorships[5].is_corresponding | False |
| authorships[5].raw_affiliation_strings | Seoul National University Bundang Hospital |
| authorships[6].author.id | https://openalex.org/A5100721059 |
| authorships[6].author.orcid | https://orcid.org/0000-0002-4579-8218 |
| authorships[6].author.display_name | Jun Sung Kim |
| authorships[6].countries | KR |
| authorships[6].affiliations[0].institution_ids | https://openalex.org/I139264467 |
| authorships[6].affiliations[0].raw_affiliation_string | Seoul National University |
| authorships[6].institutions[0].id | https://openalex.org/I139264467 |
| authorships[6].institutions[0].ror | https://ror.org/04h9pn542 |
| authorships[6].institutions[0].type | education |
| authorships[6].institutions[0].lineage | https://openalex.org/I139264467 |
| authorships[6].institutions[0].country_code | KR |
| authorships[6].institutions[0].display_name | Seoul National University |
| authorships[6].author_position | middle |
| authorships[6].raw_author_name | Jun Sung Kim |
| authorships[6].is_corresponding | False |
| authorships[6].raw_affiliation_strings | Seoul National University |
| authorships[7].author.id | https://openalex.org/A5038702450 |
| authorships[7].author.orcid | https://orcid.org/0000-0002-0545-4138 |
| authorships[7].author.display_name | Jae Hyoung Kim |
| authorships[7].countries | KR |
| authorships[7].affiliations[0].institution_ids | https://openalex.org/I2803058125 |
| authorships[7].affiliations[0].raw_affiliation_string | Seoul National University Bundang Hospital |
| authorships[7].institutions[0].id | https://openalex.org/I2803058125 |
| authorships[7].institutions[0].ror | https://ror.org/00cb3km46 |
| authorships[7].institutions[0].type | healthcare |
| authorships[7].institutions[0].lineage | https://openalex.org/I2802835388, https://openalex.org/I2803058125 |
| authorships[7].institutions[0].country_code | KR |
| authorships[7].institutions[0].display_name | Seoul National University Bundang Hospital |
| authorships[7].author_position | middle |
| authorships[7].raw_author_name | Jae Hyoung Kim |
| authorships[7].is_corresponding | False |
| authorships[7].raw_affiliation_strings | Seoul National University Bundang Hospital |
| authorships[8].author.id | https://openalex.org/A5101707931 |
| authorships[8].author.orcid | https://orcid.org/0000-0003-1434-8369 |
| authorships[8].author.display_name | Sang Eun Kim |
| authorships[8].countries | KR |
| authorships[8].affiliations[0].institution_ids | https://openalex.org/I2803058125 |
| authorships[8].affiliations[0].raw_affiliation_string | Seoul National University Bundang Hospital |
| authorships[8].institutions[0].id | https://openalex.org/I2803058125 |
| authorships[8].institutions[0].ror | https://ror.org/00cb3km46 |
| authorships[8].institutions[0].type | healthcare |
| authorships[8].institutions[0].lineage | https://openalex.org/I2802835388, https://openalex.org/I2803058125 |
| authorships[8].institutions[0].country_code | KR |
| authorships[8].institutions[0].display_name | Seoul National University Bundang Hospital |
| authorships[8].author_position | middle |
| authorships[8].raw_author_name | Sang Eun Kim |
| authorships[8].is_corresponding | False |
| authorships[8].raw_affiliation_strings | Seoul National University Bundang Hospital |
| authorships[9].author.id | https://openalex.org/A5043085745 |
| authorships[9].author.orcid | https://orcid.org/0000-0002-1103-3858 |
| authorships[9].author.display_name | Ki Woong Kim |
| authorships[9].countries | ET, KR, PR |
| authorships[9].affiliations[0].institution_ids | https://openalex.org/I2802457231, https://openalex.org/I2803058125, https://openalex.org/I4210123907 |
| authorships[9].affiliations[0].raw_affiliation_string | Seoul National University Bundang Hospital; Seoul National University College of Natural Sciences; Seoul National University College of Medicine |
| authorships[9].institutions[0].id | https://openalex.org/I2802457231 |
| authorships[9].institutions[0].ror | https://ror.org/015aem925 |
| authorships[9].institutions[0].type | education |
| authorships[9].institutions[0].lineage | https://openalex.org/I2802457231 |
| authorships[9].institutions[0].country_code | ET |
| authorships[9].institutions[0].display_name | New Generation University College |
| authorships[9].institutions[1].id | https://openalex.org/I2803058125 |
| authorships[9].institutions[1].ror | https://ror.org/00cb3km46 |
| authorships[9].institutions[1].type | healthcare |
| authorships[9].institutions[1].lineage | https://openalex.org/I2802835388, https://openalex.org/I2803058125 |
| authorships[9].institutions[1].country_code | KR |
| authorships[9].institutions[1].display_name | Seoul National University Bundang Hospital |
| authorships[9].institutions[2].id | https://openalex.org/I4210123907 |
| authorships[9].institutions[2].ror | https://ror.org/02hs8e066 |
| authorships[9].institutions[2].type | education |
| authorships[9].institutions[2].lineage | https://openalex.org/I4210123907 |
| authorships[9].institutions[2].country_code | PR |
| authorships[9].institutions[2].display_name | National University College |
| authorships[9].author_position | last |
| authorships[9].raw_author_name | Ki Woong Kim |
| authorships[9].is_corresponding | False |
| authorships[9].raw_affiliation_strings | Seoul National University Bundang Hospital; Seoul National University College of Natural Sciences; Seoul National University College of Medicine |
| has_content.pdf | True |
| has_content.grobid_xml | True |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://www.researchsquare.com/article/rs-754254/latest.pdf |
| open_access.oa_status | green |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | A case-control clinical trial on the diagnostic performance for Alzheimer’s Disease of a deep learning-based classification system using brain magnetic resonance imaging |
| has_fulltext | True |
| is_retracted | False |
| updated_date | 2025-11-06T03:46:38.306776 |
| primary_topic.id | https://openalex.org/T12702 |
| primary_topic.field.id | https://openalex.org/fields/28 |
| primary_topic.field.display_name | Neuroscience |
| primary_topic.score | 0.9975000023841858 |
| primary_topic.domain.id | https://openalex.org/domains/1 |
| primary_topic.domain.display_name | Life Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/2808 |
| primary_topic.subfield.display_name | Neurology |
| primary_topic.display_name | Brain Tumor Detection and Classification |
| related_works | https://openalex.org/W2327340211, https://openalex.org/W2027542625, https://openalex.org/W4292199793, https://openalex.org/W2282195379, https://openalex.org/W2089784006, https://openalex.org/W2295388821, https://openalex.org/W2102312026, https://openalex.org/W2071433170, https://openalex.org/W2885663991, https://openalex.org/W1016623679 |
| cited_by_count | 1 |
| counts_by_year[0].year | 2022 |
| counts_by_year[0].cited_by_count | 1 |
| locations_count | 1 |
| best_oa_location.id | doi:10.21203/rs.3.rs-754254/v1 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S4306402450 |
| best_oa_location.source.issn | |
| best_oa_location.source.type | repository |
| best_oa_location.source.is_oa | False |
| best_oa_location.source.issn_l | |
| best_oa_location.source.is_core | False |
| best_oa_location.source.is_in_doaj | False |
| best_oa_location.source.display_name | Research Square (Research Square) |
| best_oa_location.source.host_organization | https://openalex.org/I4210096694 |
| best_oa_location.source.host_organization_name | Research Square (United States) |
| best_oa_location.source.host_organization_lineage | https://openalex.org/I4210096694 |
| best_oa_location.license | cc-by |
| best_oa_location.pdf_url | https://www.researchsquare.com/article/rs-754254/latest.pdf |
| best_oa_location.version | acceptedVersion |
| best_oa_location.raw_type | posted-content |
| best_oa_location.license_id | https://openalex.org/licenses/cc-by |
| best_oa_location.is_accepted | True |
| best_oa_location.is_published | False |
| best_oa_location.raw_source_name | |
| best_oa_location.landing_page_url | https://doi.org/10.21203/rs.3.rs-754254/v1 |
| primary_location.id | doi:10.21203/rs.3.rs-754254/v1 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S4306402450 |
| primary_location.source.issn | |
| primary_location.source.type | repository |
| primary_location.source.is_oa | False |
| primary_location.source.issn_l | |
| primary_location.source.is_core | False |
| primary_location.source.is_in_doaj | False |
| primary_location.source.display_name | Research Square (Research Square) |
| primary_location.source.host_organization | https://openalex.org/I4210096694 |
| primary_location.source.host_organization_name | Research Square (United States) |
| primary_location.source.host_organization_lineage | https://openalex.org/I4210096694 |
| primary_location.license | cc-by |
| primary_location.pdf_url | https://www.researchsquare.com/article/rs-754254/latest.pdf |
| primary_location.version | acceptedVersion |
| primary_location.raw_type | posted-content |
| primary_location.license_id | https://openalex.org/licenses/cc-by |
| primary_location.is_accepted | True |
| primary_location.is_published | False |
| primary_location.raw_source_name | |
| primary_location.landing_page_url | https://doi.org/10.21203/rs.3.rs-754254/v1 |
| publication_date | 2021-08-05 |
| publication_year | 2021 |
| referenced_works | https://openalex.org/W2164586215, https://openalex.org/W7075654479, https://openalex.org/W7073692750, https://openalex.org/W2887365662, https://openalex.org/W2328176404, https://openalex.org/W2015629132, https://openalex.org/W2995673771, https://openalex.org/W2065338300, https://openalex.org/W1974300727, https://openalex.org/W2582524520, https://openalex.org/W2802245552, https://openalex.org/W2764239667, https://openalex.org/W2895106932, https://openalex.org/W2115017507, https://openalex.org/W4291982393, https://openalex.org/W2765366332, https://openalex.org/W1969395203, https://openalex.org/W2583703070, https://openalex.org/W2415793260 |
| referenced_works_count | 19 |
| abstract_inverted_index.A | 22 |
| abstract_inverted_index.a | 7 |
| abstract_inverted_index.21 | 187 |
| abstract_inverted_index.AD | 44, 98, 110, 132, 166 |
| abstract_inverted_index.To | 2 |
| abstract_inverted_index.an | 173 |
| abstract_inverted_index.in | 93, 108, 156, 167 |
| abstract_inverted_index.it | 160 |
| abstract_inverted_index.of | 6, 33, 91, 96, 165, 177 |
| abstract_inverted_index.on | 64 |
| abstract_inverted_index.or | 40 |
| abstract_inverted_index.to | 43, 87 |
| abstract_inverted_index.162 | 46 |
| abstract_inverted_index.188 | 34 |
| abstract_inverted_index.MRI | 15, 31, 174 |
| abstract_inverted_index.The | 53, 104, 152 |
| abstract_inverted_index.and | 45, 77, 123, 146 |
| abstract_inverted_index.any | 168 |
| abstract_inverted_index.due | 42 |
| abstract_inverted_index.for | 17, 131 |
| abstract_inverted_index.has | 171 |
| abstract_inverted_index.may | 161 |
| abstract_inverted_index.the | 60, 80, 89, 94, 126 |
| abstract_inverted_index.was | 27, 106 |
| abstract_inverted_index.who | 170 |
| abstract_inverted_index.DLCS | 92, 105, 153 |
| abstract_inverted_index.area | 78, 124 |
| abstract_inverted_index.beta | 57 |
| abstract_inverted_index.deep | 8 |
| abstract_inverted_index.from | 100, 112 |
| abstract_inverted_index.mild | 37 |
| abstract_inverted_index.scan | 175 |
| abstract_inverted_index.were | 50, 55, 62, 85, 133 |
| abstract_inverted_index.with | 36 |
| abstract_inverted_index.(AD). | 20 |
| abstract_inverted_index.0.937 | 147 |
| abstract_inverted_index.2020, | 189 |
| abstract_inverted_index.84.4% | 143 |
| abstract_inverted_index.85.6% | 134 |
| abstract_inverted_index.90.1% | 137 |
| abstract_inverted_index.91.0% | 140 |
| abstract_inverted_index.Trial | 179 |
| abstract_inverted_index.brain | 14, 30 |
| abstract_inverted_index.curve | 84, 130 |
| abstract_inverted_index.early | 163 |
| abstract_inverted_index.scans | 32 |
| abstract_inverted_index.shows | 154 |
| abstract_inverted_index.trial | 26 |
| abstract_inverted_index.under | 79, 125 |
| abstract_inverted_index.using | 12 |
| abstract_inverted_index.where | 159 |
| abstract_inverted_index.(DLCS) | 16 |
| abstract_inverted_index.Korean | 181 |
| abstract_inverted_index.Trials | 183 |
| abstract_inverted_index.normal | 48, 113 |
| abstract_inverted_index.system | 11 |
| abstract_inverted_index.value, | 73, 76, 119, 122 |
| abstract_inverted_index.(95%CI, | 135, 138, 141, 144, 148 |
| abstract_inverted_index.Methods | 21 |
| abstract_inverted_index.Results | 103 |
| abstract_inverted_index.amyloid | 56 |
| abstract_inverted_index.disease | 19 |
| abstract_inverted_index.improve | 162 |
| abstract_inverted_index.promise | 155 |
| abstract_inverted_index.whereas | 59 |
| abstract_inverted_index.Abstract | 0 |
| abstract_inverted_index.Clinical | 182 |
| abstract_inverted_index.February | 188 |
| abstract_inverted_index.clinical | 25, 157 |
| abstract_inverted_index.controls | 49, 61 |
| abstract_inverted_index.dementia | 41 |
| abstract_inverted_index.emission | 67 |
| abstract_inverted_index.evaluate | 88 |
| abstract_inverted_index.negative | 74, 120 |
| abstract_inverted_index.patients | 35, 54, 99, 111 |
| abstract_inverted_index.positive | 71, 117 |
| abstract_inverted_index.positron | 66 |
| abstract_inverted_index.purpose. | 178 |
| abstract_inverted_index.receiver | 81, 127 |
| abstract_inverted_index.settings | 158 |
| abstract_inverted_index.Objective | 1 |
| abstract_inverted_index.Registry, | 184 |
| abstract_inverted_index.cognitive | 38 |
| abstract_inverted_index.controls. | 102 |
| abstract_inverted_index.controls; | 114 |
| abstract_inverted_index.detection | 164 |
| abstract_inverted_index.excellent | 107 |
| abstract_inverted_index.operating | 82, 128 |
| abstract_inverted_index.undergone | 172 |
| abstract_inverted_index.Conclusion | 151 |
| abstract_inverted_index.Registered | 186 |
| abstract_inverted_index.calculated | 86 |
| abstract_inverted_index.collected. | 52 |
| abstract_inverted_index.conducted. | 28 |
| abstract_inverted_index.diagnostic | 4 |
| abstract_inverted_index.impairment | 39 |
| abstract_inverted_index.individual | 169 |
| abstract_inverted_index.predictive | 72, 75, 118, 121 |
| abstract_inverted_index.regardless | 176 |
| abstract_inverted_index.structural | 13 |
| abstract_inverted_index.79.8–90), | 136 |
| abstract_inverted_index.KCT0004758. | 185 |
| abstract_inverted_index.T1-weighted | 29 |
| abstract_inverted_index.classifying | 109 |
| abstract_inverted_index.cognitively | 47 |
| abstract_inverted_index.investigate | 3 |
| abstract_inverted_index.performance | 5, 90 |
| abstract_inverted_index.tomography. | 68 |
| abstract_inverted_index.Aβ-negative | 101 |
| abstract_inverted_index.Aβ-positive | 97 |
| abstract_inverted_index.Sensitivity, | 69 |
| abstract_inverted_index.case-control | 24 |
| abstract_inverted_index.sensitivity, | 115 |
| abstract_inverted_index.specificity, | 70, 116 |
| abstract_inverted_index.79.2–88.5), | 145 |
| abstract_inverted_index.84.5–94.2), | 139 |
| abstract_inverted_index.86.3–94.1), | 142 |
| abstract_inverted_index.Alzheimer’s | 18 |
| abstract_inverted_index.Aβ-negative, | 63 |
| abstract_inverted_index.registration: | 180 |
| abstract_inverted_index.respectively. | 150 |
| abstract_inverted_index.characteristic | 83, 129 |
| abstract_inverted_index.classification | 10, 95 |
| abstract_inverted_index.learning-based | 9 |
| abstract_inverted_index.single-center, | 23 |
| abstract_inverted_index.(Aβ)-positive, | 58 |
| abstract_inverted_index.0.911–0.963), | 149 |
| abstract_inverted_index.18F-florbetaben | 65 |
| abstract_inverted_index.retrospectively | 51 |
| abstract_inverted_index.https://cris.nih.go.kr/cris/search/detailSearch.do/17665. | 190 |
| cited_by_percentile_year.max | 94 |
| cited_by_percentile_year.min | 89 |
| countries_distinct_count | 3 |
| institutions_distinct_count | 10 |
| citation_normalized_percentile.value | 0.44158438 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |