Deep Learning-Based Multimodal 3 T MRI for the Diagnosis of Knee Osteoarthritis

The objective of this study was to investigate the application effect of deep learning model combined with different magnetic resonance imaging (MRI) sequences in the evaluation of cartilage injury of knee osteoarthritis (KOA). Specifically, an image superresolution algorithm based on an improved mu...

Full description

Saved in:
Bibliographic Details
Published inComputational and mathematical methods in medicine Vol. 2022; pp. 1 - 13
Main Authors Hu, Yong, Tang, Jie, Zhao, Shenghao, Li, Ye
Format Journal Article
LanguageEnglish
Published United States Hindawi 29.04.2022
Online AccessGet full text
ISSN1748-670X
1748-6718
1748-6718
DOI10.1155/2022/7643487

Cover

Loading…
Abstract The objective of this study was to investigate the application effect of deep learning model combined with different magnetic resonance imaging (MRI) sequences in the evaluation of cartilage injury of knee osteoarthritis (KOA). Specifically, an image superresolution algorithm based on an improved multiscale wide residual network model was proposed and compared with the single-shot multibox detector (SSD) algorithm, superresolution convolutional neural network (SRCNN) algorithm, and enhanced deep superresolution (EDSR) algorithm. Meanwhile, 104 patients with KOA diagnosed with cartilage injury were selected as the research subjects and underwent MRI scans, and the diagnostic performance of different MRI sequences was analyzed using arthroscopic results as the gold standard. It was found that the image reconstructed by the model in this study was clear enough, with minimum noise and artifacts, and the overall quality was better than that processed by other algorithms. Arthroscopic analysis found that grade I and grade II lesions concentrated on patella (26) and femoral trochlear (15). In addition to involving the patella and femoral trochlea, grade III and grade IV lesions gradually developed into the medial and lateral articular cartilage. The 3D-DS-WE sequence was found to be the best sequence for diagnosing KOA injury, with high diagnostic accuracy of over 95% in grade IV lesions. The consistency test showed that the 3D-DESS-WE sequence and T2∗ mapping sequence had a strong consistency with the results of arthroscopy, and the Kappa consistency test values were 0.748 and 0.682, respectively. In conclusion, MRI based on deep learning could clearly show the cartilage lesions of KOA. Of different MRI sequences, 3D-DS-WE sequence and T2∗ mapping sequence showed the best diagnosis results for different degrees of KOA injury.
AbstractList The objective of this study was to investigate the application effect of deep learning model combined with different magnetic resonance imaging (MRI) sequences in the evaluation of cartilage injury of knee osteoarthritis (KOA). Specifically, an image superresolution algorithm based on an improved multiscale wide residual network model was proposed and compared with the single-shot multibox detector (SSD) algorithm, superresolution convolutional neural network (SRCNN) algorithm, and enhanced deep superresolution (EDSR) algorithm. Meanwhile, 104 patients with KOA diagnosed with cartilage injury were selected as the research subjects and underwent MRI scans, and the diagnostic performance of different MRI sequences was analyzed using arthroscopic results as the gold standard. It was found that the image reconstructed by the model in this study was clear enough, with minimum noise and artifacts, and the overall quality was better than that processed by other algorithms. Arthroscopic analysis found that grade I and grade II lesions concentrated on patella (26) and femoral trochlear (15). In addition to involving the patella and femoral trochlea, grade III and grade IV lesions gradually developed into the medial and lateral articular cartilage. The 3D-DS-WE sequence was found to be the best sequence for diagnosing KOA injury, with high diagnostic accuracy of over 95% in grade IV lesions. The consistency test showed that the 3D-DESS-WE sequence and T2∗ mapping sequence had a strong consistency with the results of arthroscopy, and the Kappa consistency test values were 0.748 and 0.682, respectively. In conclusion, MRI based on deep learning could clearly show the cartilage lesions of KOA. Of different MRI sequences, 3D-DS-WE sequence and T2∗ mapping sequence showed the best diagnosis results for different degrees of KOA injury.
The objective of this study was to investigate the application effect of deep learning model combined with different magnetic resonance imaging (MRI) sequences in the evaluation of cartilage injury of knee osteoarthritis (KOA). Specifically, an image superresolution algorithm based on an improved multiscale wide residual network model was proposed and compared with the single-shot multibox detector (SSD) algorithm, superresolution convolutional neural network (SRCNN) algorithm, and enhanced deep superresolution (EDSR) algorithm. Meanwhile, 104 patients with KOA diagnosed with cartilage injury were selected as the research subjects and underwent MRI scans, and the diagnostic performance of different MRI sequences was analyzed using arthroscopic results as the gold standard. It was found that the image reconstructed by the model in this study was clear enough, with minimum noise and artifacts, and the overall quality was better than that processed by other algorithms. Arthroscopic analysis found that grade I and grade II lesions concentrated on patella (26) and femoral trochlear (15). In addition to involving the patella and femoral trochlea, grade III and grade IV lesions gradually developed into the medial and lateral articular cartilage. The 3D-DS-WE sequence was found to be the best sequence for diagnosing KOA injury, with high diagnostic accuracy of over 95% in grade IV lesions. The consistency test showed that the 3D-DESS-WE sequence and T2 ∗ mapping sequence had a strong consistency with the results of arthroscopy, and the Kappa consistency test values were 0.748 and 0.682, respectively. In conclusion, MRI based on deep learning could clearly show the cartilage lesions of KOA. Of different MRI sequences, 3D-DS-WE sequence and T2 ∗ mapping sequence showed the best diagnosis results for different degrees of KOA injury.
The objective of this study was to investigate the application effect of deep learning model combined with different magnetic resonance imaging (MRI) sequences in the evaluation of cartilage injury of knee osteoarthritis (KOA). Specifically, an image superresolution algorithm based on an improved multiscale wide residual network model was proposed and compared with the single-shot multibox detector (SSD) algorithm, superresolution convolutional neural network (SRCNN) algorithm, and enhanced deep superresolution (EDSR) algorithm. Meanwhile, 104 patients with KOA diagnosed with cartilage injury were selected as the research subjects and underwent MRI scans, and the diagnostic performance of different MRI sequences was analyzed using arthroscopic results as the gold standard. It was found that the image reconstructed by the model in this study was clear enough, with minimum noise and artifacts, and the overall quality was better than that processed by other algorithms. Arthroscopic analysis found that grade I and grade II lesions concentrated on patella (26) and femoral trochlear (15). In addition to involving the patella and femoral trochlea, grade III and grade IV lesions gradually developed into the medial and lateral articular cartilage. The 3D-DS-WE sequence was found to be the best sequence for diagnosing KOA injury, with high diagnostic accuracy of over 95% in grade IV lesions. The consistency test showed that the 3D-DESS-WE sequence and T2∗ mapping sequence had a strong consistency with the results of arthroscopy, and the Kappa consistency test values were 0.748 and 0.682, respectively. In conclusion, MRI based on deep learning could clearly show the cartilage lesions of KOA. Of different MRI sequences, 3D-DS-WE sequence and T2∗ mapping sequence showed the best diagnosis results for different degrees of KOA injury.The objective of this study was to investigate the application effect of deep learning model combined with different magnetic resonance imaging (MRI) sequences in the evaluation of cartilage injury of knee osteoarthritis (KOA). Specifically, an image superresolution algorithm based on an improved multiscale wide residual network model was proposed and compared with the single-shot multibox detector (SSD) algorithm, superresolution convolutional neural network (SRCNN) algorithm, and enhanced deep superresolution (EDSR) algorithm. Meanwhile, 104 patients with KOA diagnosed with cartilage injury were selected as the research subjects and underwent MRI scans, and the diagnostic performance of different MRI sequences was analyzed using arthroscopic results as the gold standard. It was found that the image reconstructed by the model in this study was clear enough, with minimum noise and artifacts, and the overall quality was better than that processed by other algorithms. Arthroscopic analysis found that grade I and grade II lesions concentrated on patella (26) and femoral trochlear (15). In addition to involving the patella and femoral trochlea, grade III and grade IV lesions gradually developed into the medial and lateral articular cartilage. The 3D-DS-WE sequence was found to be the best sequence for diagnosing KOA injury, with high diagnostic accuracy of over 95% in grade IV lesions. The consistency test showed that the 3D-DESS-WE sequence and T2∗ mapping sequence had a strong consistency with the results of arthroscopy, and the Kappa consistency test values were 0.748 and 0.682, respectively. In conclusion, MRI based on deep learning could clearly show the cartilage lesions of KOA. Of different MRI sequences, 3D-DS-WE sequence and T2∗ mapping sequence showed the best diagnosis results for different degrees of KOA injury.
Author Hu, Yong
Li, Ye
Zhao, Shenghao
Tang, Jie
AuthorAffiliation Department of Orthopaedic, Wuhan Fourth Hospital, Wuhan, 430000 Hubei, China
AuthorAffiliation_xml – name: Department of Orthopaedic, Wuhan Fourth Hospital, Wuhan, 430000 Hubei, China
Author_xml – sequence: 1
  givenname: Yong
  orcidid: 0000-0002-5787-1929
  surname: Hu
  fullname: Hu, Yong
  organization: Department of OrthopaedicWuhan Fourth HospitalWuhan430000 HubeiChina
– sequence: 2
  givenname: Jie
  orcidid: 0000-0001-6874-3626
  surname: Tang
  fullname: Tang, Jie
  organization: Department of OrthopaedicWuhan Fourth HospitalWuhan430000 HubeiChina
– sequence: 3
  givenname: Shenghao
  orcidid: 0000-0002-9428-3173
  surname: Zhao
  fullname: Zhao, Shenghao
  organization: Department of OrthopaedicWuhan Fourth HospitalWuhan430000 HubeiChina
– sequence: 4
  givenname: Ye
  orcidid: 0000-0002-9516-2525
  surname: Li
  fullname: Li, Ye
  organization: Department of OrthopaedicWuhan Fourth HospitalWuhan430000 HubeiChina
BackLink https://www.ncbi.nlm.nih.gov/pubmed/35529263$$D View this record in MEDLINE/PubMed
BookMark eNp9kUtrFEEUhQuJ5KU711JLwbSpZ1f3RsjLGJwQlAjuijvVt2dKeqomVTWG7Nz6N_0lmWEmIQq6uhfud86Be_bIVogBCXnF2TvOtT4UTIhDUyupGvOM7HKjmqo2vNl63Nm3HbKX83fGNDeab5MdqbVoRS13yedTxDkdIaTgw6Q6howdvVwMxc9iBwOVv3_-uqaXXy5oHxMtU6SnHiYhZp9p7OmngEivcsEIqUyTLz6_IM97GDK-3Mx98vXD2fXJx2p0dX5xcjSqnBKsVK3QCliNOGZGtEZ1Ura849BLo9m47nTrHCgptdJaGeCArudOGuEatzyD3Cfv177zxXiGncNQEgx2nvwM0p2N4O2fl-CndhJ_2JaZWjKxNHizMUjxZoG52JnPDocBAsZFtqKuuWqk4iv09dOsx5CHPy4BsQZcijkn7K3zBYqPq2g_WM7sqiy7KstuylqKDv4SPfj-A3-7xqc-dHDr_0_fA7yYoc0
CitedBy_id crossref_primary_10_1007_s00330_024_11105_8
crossref_primary_10_1007_s00256_024_04627_1
Cites_doi 10.1016/j.ijscr.2021.106534
10.36290/vnl.2020.041
10.1097/RMR.0000000000000237
10.1016/j.arth.2018.12.001
10.1186/s13018-017-0521-3
10.2169/internalmedicine.5250-20
10.1186/s13063-017-2129-7
10.1002/jmri.27131
10.1148/radiol.2020200417
10.1186/s12891-019-2895-3
10.1111/1756-185X.13082.Epub
10.12968/hmed.2018.79.4.C54
10.1111/jjns.12254.Epub
10.3390/genes11080854
10.1007/s11606-018-4763-5
10.1016/j.apmr.2019.04.020
10.1016/j.ocl.2019.05.001.Epub
10.12809/hkmj187600
10.1109/EMBC46164.2021.9629705
10.2147/IMCRJ.S89507
10.1002/sctm.18-0122
10.1002/jmri.26991
10.1155/2019/8514808
ContentType Journal Article
Copyright Copyright © 2022 Yong Hu et al.
Copyright © 2022 Yong Hu et al. 2022
Copyright_xml – notice: Copyright © 2022 Yong Hu et al.
– notice: Copyright © 2022 Yong Hu et al. 2022
DBID RHU
RHW
RHX
AAYXX
CITATION
NPM
7X8
5PM
DOI 10.1155/2022/7643487
DatabaseName Hindawi Publishing Complete
Hindawi Publishing Subscription Journals
Hindawi Publishing Open Access
CrossRef
PubMed
MEDLINE - Academic
PubMed Central (Full Participant titles)
DatabaseTitle CrossRef
PubMed
MEDLINE - Academic
DatabaseTitleList
PubMed

CrossRef
MEDLINE - Academic
Database_xml – sequence: 1
  dbid: RHX
  name: Hindawi Publishing Open Access
  url: http://www.hindawi.com/journals/
  sourceTypes: Publisher
– sequence: 2
  dbid: NPM
  name: PubMed
  url: https://proxy.k.utb.cz/login?url=http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed
  sourceTypes: Index Database
DeliveryMethod fulltext_linktorsrc
Discipline Medicine
EISSN 1748-6718
Editor Hussein, Ahmed Faeq
Editor_xml – sequence: 1
  givenname: Ahmed Faeq
  surname: Hussein
  fullname: Hussein, Ahmed Faeq
EndPage 13
ExternalDocumentID PMC9076302
35529263
10_1155_2022_7643487
Genre Journal Article
GroupedDBID ---
29F
2DF
3YN
4.4
53G
5GY
5VS
6J9
AAFWJ
AAJEY
ABDBF
ACGFO
ACIPV
ACIWK
ADBBV
ADRAZ
AENEX
AFKVX
AHMBA
AIAGR
AJWEG
ALMA_UNASSIGNED_HOLDINGS
AOIJS
BAWUL
BCNDV
CAG
CS3
DIK
EAD
EAP
EAS
EBC
EBD
EBS
EMK
EMOBN
EPL
EST
ESX
F5P
GROUPED_DOAJ
GX1
HYE
IAO
IEA
IHR
INH
INR
ITC
J.P
J9A
KQ8
M48
M4Z
ML~
O5R
OK1
P2P
REM
RHU
RHW
RHX
RNS
RPM
SV3
TFW
TUS
TWF
0R~
24P
AAYXX
ACCMX
ACUHS
CITATION
H13
PGMZT
7X7
88E
8FE
8FG
8FI
8FJ
ABJCF
ABUWG
AFKRA
AWYRJ
BENPR
BGLVJ
BPHCQ
BVXVI
CCPQU
COF
EJD
FYUFA
HCIFZ
HF~
HMCUK
IPNFZ
L6V
M1P
M7S
NPM
O5S
PHGZT
PQQKQ
PROAC
PSQYO
PTHSS
RIG
UKHRP
7X8
AAMMB
AEFGJ
AGXDD
AIDQK
AIDYY
5PM
ID FETCH-LOGICAL-c420t-9254a06eeb072974d3391d1af3750b6d59cca433545547a1aecf1c372c8cb6da3
IEDL.DBID M48
ISSN 1748-670X
1748-6718
IngestDate Thu Aug 21 18:10:28 EDT 2025
Fri Jul 11 13:04:03 EDT 2025
Thu Apr 03 07:05:35 EDT 2025
Thu Apr 24 23:03:28 EDT 2025
Tue Jul 01 00:37:03 EDT 2025
Sun Jun 02 18:52:38 EDT 2024
IsDoiOpenAccess true
IsOpenAccess true
IsPeerReviewed true
IsScholarly true
Language English
License This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
https://creativecommons.org/licenses/by/4.0
Copyright © 2022 Yong Hu et al.
LinkModel DirectLink
MergedId FETCHMERGED-LOGICAL-c420t-9254a06eeb072974d3391d1af3750b6d59cca433545547a1aecf1c372c8cb6da3
Notes ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
Academic Editor: Ahmed Faeq Hussein
ORCID 0000-0002-9516-2525
0000-0002-5787-1929
0000-0001-6874-3626
0000-0002-9428-3173
OpenAccessLink http://journals.scholarsportal.info/openUrl.xqy?doi=10.1155/2022/7643487
PMID 35529263
PQID 2661483412
PQPubID 23479
PageCount 13
ParticipantIDs pubmedcentral_primary_oai_pubmedcentral_nih_gov_9076302
proquest_miscellaneous_2661483412
pubmed_primary_35529263
crossref_citationtrail_10_1155_2022_7643487
crossref_primary_10_1155_2022_7643487
hindawi_primary_10_1155_2022_7643487
ProviderPackageCode CITATION
AAYXX
PublicationCentury 2000
PublicationDate 2022-04-29
PublicationDateYYYYMMDD 2022-04-29
PublicationDate_xml – month: 04
  year: 2022
  text: 2022-04-29
  day: 29
PublicationDecade 2020
PublicationPlace United States
PublicationPlace_xml – name: United States
PublicationTitle Computational and mathematical methods in medicine
PublicationTitleAlternate Comput Math Methods Med
PublicationYear 2022
Publisher Hindawi
Publisher_xml – name: Hindawi
References 22
23
24
25
V. D. Zavadovskaia (3) 2003; 1
10
11
12
13
14
15
16
17
18
19
1
2
M. Hanus (4) 2020; 87
5
6
7
8
9
20
21
References_xml – ident: 2
  doi: 10.1016/j.ijscr.2021.106534
– ident: 6
  doi: 10.36290/vnl.2020.041
– volume: 1
  start-page: 49
  year: 2003
  ident: 3
  article-title: Artrosonografiia v diagnostike revmatoidnogo artrita kolennogo sustava [Arthrosonography in the diagnosis of rheumatoid arthritis of the knee joint]
  publication-title: Vestnik Rentgenologii i Radiologii
– ident: 19
  doi: 10.1097/RMR.0000000000000237
– ident: 13
  doi: 10.1016/j.arth.2018.12.001
– ident: 12
  doi: 10.1186/s13018-017-0521-3
– ident: 5
  doi: 10.2169/internalmedicine.5250-20
– ident: 20
  doi: 10.1186/s13063-017-2129-7
– ident: 17
  doi: 10.1002/jmri.27131
– ident: 18
  doi: 10.1148/radiol.2020200417
– ident: 24
  doi: 10.1186/s12891-019-2895-3
– ident: 25
  doi: 10.1111/1756-185X.13082.Epub
– ident: 10
  doi: 10.12968/hmed.2018.79.4.C54
– volume: 87
  start-page: 356
  issue: 5
  year: 2020
  ident: 4
  article-title: Vzácná cévní komplikace po náhradě předního zkříženého vazu [Rare vascular complication after ACL reconstruction]
  publication-title: Acta Chirurgiae Orthopaedicae et Traumatologiae Cechoslovaca
– ident: 22
  doi: 10.1111/jjns.12254.Epub
– ident: 8
  doi: 10.3390/genes11080854
– ident: 11
  doi: 10.1007/s11606-018-4763-5
– ident: 23
  doi: 10.1016/j.apmr.2019.04.020
– ident: 9
  doi: 10.1016/j.ocl.2019.05.001.Epub
– ident: 7
  doi: 10.12809/hkmj187600
– ident: 16
  doi: 10.1109/EMBC46164.2021.9629705
– ident: 1
  doi: 10.2147/IMCRJ.S89507
– ident: 15
  doi: 10.1002/sctm.18-0122
– ident: 14
  doi: 10.1002/jmri.26991
– ident: 21
  doi: 10.1155/2019/8514808
SSID ssj0051751
Score 2.299056
Snippet The objective of this study was to investigate the application effect of deep learning model combined with different magnetic resonance imaging (MRI) sequences...
SourceID pubmedcentral
proquest
pubmed
crossref
hindawi
SourceType Open Access Repository
Aggregation Database
Index Database
Enrichment Source
Publisher
StartPage 1
SummonAdditionalLinks – databaseName: Hindawi Publishing Open Access
  dbid: RHX
  link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwjV1LS8NAEF5sQfEivq0vVtCTBLPPZI9qlapUUSz0FpLNri1oIrbi1at_01_ibJIW6wO9BMLOhjDfsPMNs_stQrs8FobrJPU0PD3OZOKFxipPWGq1IIby0J1Gbl_KVoefd0W3EkkafG_hQ7Zz5Tk9CCBzAreuoRoEmCvKW93RgisgA5Ly3GPoycDvjva3f5k7kXmme67kfen_RCy_7o_8lHBO59FcxRTxYQntApoy2SKaaVe98CV03TTmEVf6qHfeEaSjFBfnaR_yFCay99e3W9y-OcPASzHwPNwst9X1Bzi3-CIzBl8BxjkET6-QNlpGndOT2-OWV12QAJ6l_tBTUN3FvjQmcfrfAU8ZUyQlsWXAAxKZCgX4cMaAJQkexCQ22hLNAqpDDcMxW0H1LM_MGsIqSWxKGKexBOx0GopE-lZZIjVRIrANtD9yXqQr9XB3icV9VFQRQkTO1VHl6gbaG1s_lqoZv9jtVjj8YbYzAimC6HctjTgz-fMgKuhFCJmYNtBqCdr4S8CkqKKSNVAwAefYwClrT45k_V6hsK18WHZ9uv6_39tAs-7V9Zio2kT14dOz2QKqMky2i0D9ACBU4WU
  priority: 102
  providerName: Hindawi Publishing
Title Deep Learning-Based Multimodal 3 T MRI for the Diagnosis of Knee Osteoarthritis
URI https://dx.doi.org/10.1155/2022/7643487
https://www.ncbi.nlm.nih.gov/pubmed/35529263
https://www.proquest.com/docview/2661483412
https://pubmed.ncbi.nlm.nih.gov/PMC9076302
Volume 2022
hasFullText 1
inHoldings 1
isFullTextHit
isPrint
link http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV1Lb9QwEB61RVS9VOVVti8ZqZxQIH4mPlQI-mABLYiqK-UWJY7NrtQmbXerwq1X_ia_hHEeq25VhMQlF48jZb6ZzDcaewZgV2TSCpMXgcFnILjKg9g6HUjHnJHUMhH728iDL6o_FJ8SmSxAN220VeDk3tTOz5MaXp6-_nHx8y06_F7t8FL6_J29iTC0IvlehAcYkyLvogMxqydIDJK0uRoZByoKk-4I_J3dK7CM8ZdppvhcnHo48gny9fg-Gnr3NOWt8HS0BqstryTvGkN4BAu2fAzLg7Zy_gS-HVh7Ttpuqt-D9xi8ClLfvj2rCtzIf9_8OiGD448EWSxBVkgOmkN44wmpHPlcWku-okVUaGqjuhHSUxgeHZ7s94N2nALiwMJpoDEXzEJlbe67hUei4FzTgmaOI2vIVSE1oik4R04lRZTRzBpHDY-YiQ0uZ_wZLJVVaZ8D0XnuCsoFyxQibYpY5ip02lFlqJaR68GrTnmpaXuN-5EXp2mdc0iZeq2nrdZ78HImfd702PiL3G6Lwz_EXnQgpegrvgCSlba6mqQ1GYkxbrMerDegzd7U4d6DaA7OmYDvwz2_Uo5HdT9uHeJPOmQb_71zE1b8B_giFdNbsDS9vLLbyHWm-Q4sfkjoTm3M-DzuJ38AnLz8sw
linkProvider Scholars Portal
openUrl ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Deep+Learning-Based+Multimodal+3%E2%80%89T+MRI+for+the+Diagnosis+of+Knee+Osteoarthritis&rft.jtitle=Computational+and+mathematical+methods+in+medicine&rft.au=Hu%2C+Yong&rft.au=Tang%2C+Jie&rft.au=Zhao%2C+Shenghao&rft.au=Li%2C+Ye&rft.date=2022-04-29&rft.pub=Hindawi&rft.issn=1748-670X&rft.eissn=1748-6718&rft.volume=2022&rft_id=info:doi/10.1155%2F2022%2F7643487&rft_id=info%3Apmid%2F35529263&rft.externalDocID=PMC9076302
thumbnail_l http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1748-670X&client=summon
thumbnail_m http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1748-670X&client=summon
thumbnail_s http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1748-670X&client=summon