Real‐time biomechanics using the finite element method and machine learning: Review and perspective
Purpose The finite element method (FEM) is the preferred method to simulate phenomena in anatomical structures. However, purely FEM‐based mechanical simulations require considerable time, limiting their use in clinical applications that require real‐time responses, such as haptics simulators. Machin...
Gespeichert in:
| Veröffentlicht in: | Medical physics (Lancaster) Jg. 48; H. 1; S. 7 - 18 |
|---|---|
| Hauptverfasser: | , , , , |
| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
United States
01.01.2021
|
| Schlagworte: | |
| ISSN: | 0094-2405, 2473-4209, 2473-4209 |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| Abstract | Purpose
The finite element method (FEM) is the preferred method to simulate phenomena in anatomical structures. However, purely FEM‐based mechanical simulations require considerable time, limiting their use in clinical applications that require real‐time responses, such as haptics simulators. Machine learning (ML) approaches have been proposed to help with the reduction of the required time. The present paper reviews cases where ML could help to generate faster simulations, without considerably affecting the performance results.
Methods
This review details the ML approaches used, considering the anatomical structures involved, the data collection strategies, the selected ML algorithms, with corresponding features, the metrics used for validation, and the resulting time gains.
Results
A total of 41 references were found. ML algorithms are mainly trained with FEM‐based simulations in 32 publications. The preferred ML approach is neural networks, including deep learning in 35 publications. Tissue deformation is simulated in 18 applications, but other features are also considered. The average distance error and mean squared error are the most frequently used performance metrics, in 14 and 17 publications, respectively. The time gains were considerable, going from hours or minutes for purely FEM‐based simulations to milliseconds, when using ML.
Conclusions
ML algorithms can be used to accelerate FEM‐based biomechanical simulations of anatomical structures, possibly reaching real‐time responses. Fast and real‐time simulations of anatomical structures, generated with ML algorithms, can help to reduce the time required by FEM‐based simulations and accelerate their adoption in the clinical practice. |
|---|---|
| AbstractList | Purpose
The finite element method (FEM) is the preferred method to simulate phenomena in anatomical structures. However, purely FEM‐based mechanical simulations require considerable time, limiting their use in clinical applications that require real‐time responses, such as haptics simulators. Machine learning (ML) approaches have been proposed to help with the reduction of the required time. The present paper reviews cases where ML could help to generate faster simulations, without considerably affecting the performance results.
Methods
This review details the ML approaches used, considering the anatomical structures involved, the data collection strategies, the selected ML algorithms, with corresponding features, the metrics used for validation, and the resulting time gains.
Results
A total of 41 references were found. ML algorithms are mainly trained with FEM‐based simulations in 32 publications. The preferred ML approach is neural networks, including deep learning in 35 publications. Tissue deformation is simulated in 18 applications, but other features are also considered. The average distance error and mean squared error are the most frequently used performance metrics, in 14 and 17 publications, respectively. The time gains were considerable, going from hours or minutes for purely FEM‐based simulations to milliseconds, when using ML.
Conclusions
ML algorithms can be used to accelerate FEM‐based biomechanical simulations of anatomical structures, possibly reaching real‐time responses. Fast and real‐time simulations of anatomical structures, generated with ML algorithms, can help to reduce the time required by FEM‐based simulations and accelerate their adoption in the clinical practice. The finite element method (FEM) is the preferred method to simulate phenomena in anatomical structures. However, purely FEM-based mechanical simulations require considerable time, limiting their use in clinical applications that require real-time responses, such as haptics simulators. Machine learning (ML) approaches have been proposed to help with the reduction of the required time. The present paper reviews cases where ML could help to generate faster simulations, without considerably affecting the performance results.PURPOSEThe finite element method (FEM) is the preferred method to simulate phenomena in anatomical structures. However, purely FEM-based mechanical simulations require considerable time, limiting their use in clinical applications that require real-time responses, such as haptics simulators. Machine learning (ML) approaches have been proposed to help with the reduction of the required time. The present paper reviews cases where ML could help to generate faster simulations, without considerably affecting the performance results.This review details the ML approaches used, considering the anatomical structures involved, the data collection strategies, the selected ML algorithms, with corresponding features, the metrics used for validation, and the resulting time gains.METHODSThis review details the ML approaches used, considering the anatomical structures involved, the data collection strategies, the selected ML algorithms, with corresponding features, the metrics used for validation, and the resulting time gains.A total of 41 references were found. ML algorithms are mainly trained with FEM-based simulations in 32 publications. The preferred ML approach is neural networks, including deep learning in 35 publications. Tissue deformation is simulated in 18 applications, but other features are also considered. The average distance error and mean squared error are the most frequently used performance metrics, in 14 and 17 publications, respectively. The time gains were considerable, going from hours or minutes for purely FEM-based simulations to milliseconds, when using ML.RESULTSA total of 41 references were found. ML algorithms are mainly trained with FEM-based simulations in 32 publications. The preferred ML approach is neural networks, including deep learning in 35 publications. Tissue deformation is simulated in 18 applications, but other features are also considered. The average distance error and mean squared error are the most frequently used performance metrics, in 14 and 17 publications, respectively. The time gains were considerable, going from hours or minutes for purely FEM-based simulations to milliseconds, when using ML.ML algorithms can be used to accelerate FEM-based biomechanical simulations of anatomical structures, possibly reaching real-time responses. Fast and real-time simulations of anatomical structures, generated with ML algorithms, can help to reduce the time required by FEM-based simulations and accelerate their adoption in the clinical practice.CONCLUSIONSML algorithms can be used to accelerate FEM-based biomechanical simulations of anatomical structures, possibly reaching real-time responses. Fast and real-time simulations of anatomical structures, generated with ML algorithms, can help to reduce the time required by FEM-based simulations and accelerate their adoption in the clinical practice. The finite element method (FEM) is the preferred method to simulate phenomena in anatomical structures. However, purely FEM-based mechanical simulations require considerable time, limiting their use in clinical applications that require real-time responses, such as haptics simulators. Machine learning (ML) approaches have been proposed to help with the reduction of the required time. The present paper reviews cases where ML could help to generate faster simulations, without considerably affecting the performance results. This review details the ML approaches used, considering the anatomical structures involved, the data collection strategies, the selected ML algorithms, with corresponding features, the metrics used for validation, and the resulting time gains. A total of 41 references were found. ML algorithms are mainly trained with FEM-based simulations in 32 publications. The preferred ML approach is neural networks, including deep learning in 35 publications. Tissue deformation is simulated in 18 applications, but other features are also considered. The average distance error and mean squared error are the most frequently used performance metrics, in 14 and 17 publications, respectively. The time gains were considerable, going from hours or minutes for purely FEM-based simulations to milliseconds, when using ML. ML algorithms can be used to accelerate FEM-based biomechanical simulations of anatomical structures, possibly reaching real-time responses. Fast and real-time simulations of anatomical structures, generated with ML algorithms, can help to reduce the time required by FEM-based simulations and accelerate their adoption in the clinical practice. |
| Author | Duong, Luc Hachem, Bahe Phellan, Renzo Clin, Julien Mac‐Thiong, Jean‐Marc |
| Author_xml | – sequence: 1 givenname: Renzo surname: Phellan fullname: Phellan, Renzo email: renzo.phellanaro@mail.mcgill.ca organization: University of Quebec – sequence: 2 givenname: Bahe surname: Hachem fullname: Hachem, Bahe organization: Spinologics Inc – sequence: 3 givenname: Julien surname: Clin fullname: Clin, Julien organization: Spinologics Inc – sequence: 4 givenname: Jean‐Marc surname: Mac‐Thiong fullname: Mac‐Thiong, Jean‐Marc organization: Spinologics Inc – sequence: 5 givenname: Luc surname: Duong fullname: Duong, Luc organization: University of Quebec |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/33222226$$D View this record in MEDLINE/PubMed |
| BookMark | eNp10EtOwzAQBmALFdHykDgB8pJNiu04TsIOIV4SCFTBOpo6E2oUOyF2W3XHETgjJ6GlPCQEs5nFfPMv_m3Sc41DQvY5G3LGxJFth1wqJjbIQMg0jqRgeY8MGMtlJCRL-mTb-yfGmIoTtkX6cSxWowYERwj128trMBbp2DQW9QSc0Z5OvXGPNEyQVsaZgBRrtOgCtRgmTUnBldSCnhiHtEbo3JIf0xHODM4_ji12vkUdzAx3yWYFtce9z71DHs7P7k8vo-vbi6vTk-tIx4KLCGWe6FJznskSeSoVqCxhapyhEpyPIYdSQJoqhWnO4kpDloFIQMlE6ByrMt4hh-vctmuep-hDYY3XWNfgsJn6QkgVK6Zkli7pwSedji2WRdsZC92i-KrmJ0t3jfcdVt-Es2LVemHb4qP1JR3-otoECKZxoQNT__UQrR_mpsbFv8HFzd3avwNhBJKK |
| CitedBy_id | crossref_primary_10_1177_14653125231203743 crossref_primary_10_3390_met13020286 crossref_primary_10_3390_s24186104 crossref_primary_10_3390_ma17071506 crossref_primary_10_1080_17480272_2022_2037704 crossref_primary_10_1016_j_autcon_2024_105935 crossref_primary_10_1088_2515_7655_ac8e30 crossref_primary_10_1088_1361_6560_adde0d crossref_primary_10_1007_s11548_022_02596_1 crossref_primary_10_1016_j_media_2024_103221 crossref_primary_10_1109_TMI_2022_3180078 crossref_primary_10_1080_10255842_2024_2431892 crossref_primary_10_1016_j_compbiomed_2021_104547 crossref_primary_10_1007_s00419_024_02601_w crossref_primary_10_1002_mp_17554 crossref_primary_10_1016_j_compbiomed_2024_109646 crossref_primary_10_1155_bmri_8284581 crossref_primary_10_1016_j_jocn_2024_02_024 crossref_primary_10_3390_s25092893 crossref_primary_10_1016_j_compbiomed_2022_105699 crossref_primary_10_1177_15589250251352192 crossref_primary_10_3390_bioengineering10060627 crossref_primary_10_1016_j_compbiomed_2025_110683 crossref_primary_10_1016_j_clinbiomech_2023_106003 crossref_primary_10_1016_j_eswa_2024_123953 crossref_primary_10_3390_bioengineering10091066 crossref_primary_10_3390_s24185872 crossref_primary_10_1016_j_measurement_2022_111534 crossref_primary_10_1007_s11831_024_10100_y crossref_primary_10_1007_s11144_021_02088_4 crossref_primary_10_1016_j_bonr_2025_101870 crossref_primary_10_1016_j_ijmecsci_2021_106972 crossref_primary_10_3389_fbioe_2022_855791 crossref_primary_10_3390_cancers13153662 crossref_primary_10_1088_1361_6668_adfa48 crossref_primary_10_1007_s10237_024_01862_2 crossref_primary_10_1007_s10439_025_03798_9 crossref_primary_10_3390_app14209296 crossref_primary_10_1002_aisy_202300082 crossref_primary_10_1016_j_eswa_2024_126343 crossref_primary_10_1016_j_cmpb_2021_106528 crossref_primary_10_1038_s41598_024_83598_8 crossref_primary_10_3389_fneur_2023_1123607 crossref_primary_10_1016_j_clinbiomech_2023_106117 crossref_primary_10_1016_j_jcis_2024_08_204 crossref_primary_10_1016_j_medengphy_2025_104321 crossref_primary_10_1016_j_compmedimag_2022_102165 crossref_primary_10_1080_24699322_2024_2357164 crossref_primary_10_1111_jace_70241 crossref_primary_10_1016_j_jmbbm_2024_106736 crossref_primary_10_1109_JPROC_2022_3166253 crossref_primary_10_1016_j_biosystemseng_2023_04_012 crossref_primary_10_1016_j_media_2024_103350 |
| Cites_doi | 10.1007/978-981-15-1925-3_31 10.1016/j.medengphy.2014.06.016 10.1002/jrsm.1378 10.1162/neco.1992.4.4.473 10.1007/978-1-4614-6849-3 10.1002/rcs.1493 10.1016/j.eswa.2016.11.037 10.1109/ACCESS.2019.2949699 10.1007/s11548-019-01965-7 10.1038/nature14539 10.3389/fphy.2019.00117 10.1109/EMBC.2013.6610170 10.1016/j.apm.2012.10.049 10.1007/s10237-017-0903-9 10.1016/j.cma.2018.12.030 10.1115/1.4002536 10.1016/j.finel.2011.02.014 10.1002/ecs2.1976 10.1016/j.actbio.2017.09.025 10.1016/j.artmed.2017.07.004 10.1109/ICDMW.2016.0042 10.1109/TBME.2012.2185495 10.1017/CBO9781107298019 10.1162/PRES_a_00054 10.1002/cnm.3103 10.1155/2017/3602928 10.1007/s10237-006-0015-4 10.1109/WHC.2009.4810896 10.1007/978-3-540-85990-1_89 10.1093/bib/bbx044 10.1117/12.911171 10.1007/8415_2012_146 10.1115/1.4043290 10.1016/j.media.2019.101569 10.1016/j.jmbbm.2019.103527 10.5194/gmd-7-1247-2014 10.1016/j.compbiomed.2018.09.029 10.1109/ICSMC.2011.6084112 10.3163/1536-5050.95.4.442 10.1016/j.eswa.2019.113083 10.3390/app9142775 10.1016/j.jbiomech.2019.109544 10.1007/s11548-007-0078-4 10.1007/978-3-030-42428-2_4 10.1016/j.bbe.2019.09.001 10.1016/j.media.2017.07.005 10.1109/MCG.2007.51 10.1007/978-1-4419-9326-7_5 10.1016/j.bspc.2013.04.004 10.1016/j.jmbbm.2011.03.002 10.1016/j.mechrescom.2008.10.004 10.1016/j.jbiomech.2015.12.014 10.1016/j.compbiomed.2017.09.019 10.1016/j.media.2019.06.002 10.1109/MSP.2008.930649 |
| ContentType | Journal Article |
| Copyright | 2020 American Association of Physicists in Medicine 2020 American Association of Physicists in Medicine. |
| Copyright_xml | – notice: 2020 American Association of Physicists in Medicine – notice: 2020 American Association of Physicists in Medicine. |
| DBID | AAYXX CITATION CGR CUY CVF ECM EIF NPM 7X8 |
| DOI | 10.1002/mp.14602 |
| DatabaseName | CrossRef Medline MEDLINE MEDLINE (Ovid) MEDLINE MEDLINE PubMed MEDLINE - Academic |
| DatabaseTitle | CrossRef MEDLINE Medline Complete MEDLINE with Full Text PubMed MEDLINE (Ovid) MEDLINE - Academic |
| DatabaseTitleList | MEDLINE - Academic MEDLINE |
| Database_xml | – sequence: 1 dbid: NPM name: PubMed url: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 2 dbid: 7X8 name: MEDLINE - Academic url: https://search.proquest.com/medline sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Medicine Physics |
| EISSN | 2473-4209 |
| EndPage | 18 |
| ExternalDocumentID | 33222226 10_1002_mp_14602 MP14602 |
| Genre | reviewArticle Journal Article Review |
| GroupedDBID | --- --Z -DZ .GJ 0R~ 1OB 1OC 29M 2WC 33P 36B 3O- 4.4 53G 5GY 5RE 5VS AAHHS AAHQN AAIPD AAMNL AANLZ AAQQT AASGY AAXRX AAYCA AAZKR ABCUV ABDPE ABEFU ABFTF ABJNI ABLJU ABQWH ABTAH ABXGK ACAHQ ACBEA ACCFJ ACCZN ACGFO ACGFS ACGOF ACPOU ACXBN ACXQS ADBBV ADBTR ADKYN ADOZA ADXAS ADZMN AEEZP AEGXH AEIGN AENEX AEQDE AEUYR AFBPY AFFPM AFWVQ AHBTC AIACR AIAGR AITYG AIURR AIWBW AJBDE ALMA_UNASSIGNED_HOLDINGS ALUQN ALVPJ AMYDB ASPBG BFHJK C45 CS3 DCZOG DRFUL DRMAN DRSTM DU5 EBD EBS EJD EMB EMOBN F5P HDBZQ HGLYW I-F KBYEO LATKE LEEKS LOXES LUTES LYRES MEWTI O9- OVD P2P P2W PALCI PHY RJQFR RNS ROL SAMSI SUPJJ SV3 TEORI TN5 TWZ USG WOHZO WXSBR XJT ZGI ZVN ZXP ZY4 ZZTAW AAMMB AAYXX ABUFD ADMLS AEFGJ AEYWJ AGHNM AGXDD AGYGG AIDQK AIDYY AIQQE CITATION LH4 CGR CUY CVF ECM EIF NPM 7X8 |
| ID | FETCH-LOGICAL-c3212-e495cdc1184de1746a68506b8e6211ba9ad2a7766e7903fca88a25a6452c9efd3 |
| IEDL.DBID | DRFUL |
| ISICitedReferencesCount | 71 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000596112800001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 0094-2405 2473-4209 |
| IngestDate | Thu Sep 04 18:23:58 EDT 2025 Thu Apr 03 06:55:17 EDT 2025 Sat Nov 29 06:02:44 EST 2025 Tue Nov 18 21:36:27 EST 2025 Wed Jan 22 16:58:41 EST 2025 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 1 |
| Keywords | biomechanical simulation, finite element modelling, machine learning, neural network, real-time |
| Language | English |
| License | 2020 American Association of Physicists in Medicine. |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c3212-e495cdc1184de1746a68506b8e6211ba9ad2a7766e7903fca88a25a6452c9efd3 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 ObjectType-Review-3 content type line 23 |
| PMID | 33222226 |
| PQID | 2463606487 |
| PQPubID | 23479 |
| PageCount | 12 |
| ParticipantIDs | proquest_miscellaneous_2463606487 pubmed_primary_33222226 crossref_primary_10_1002_mp_14602 crossref_citationtrail_10_1002_mp_14602 wiley_primary_10_1002_mp_14602_MP14602 |
| PublicationCentury | 2000 |
| PublicationDate | January 2021 2021-01-00 2021-Jan 20210101 |
| PublicationDateYYYYMMDD | 2021-01-01 |
| PublicationDate_xml | – month: 01 year: 2021 text: January 2021 |
| PublicationDecade | 2020 |
| PublicationPlace | United States |
| PublicationPlace_xml | – name: United States |
| PublicationTitle | Medical physics (Lancaster) |
| PublicationTitleAlternate | Med Phys |
| PublicationYear | 2021 |
| References | 2017; 42 2017; 8 2017; 80 2013; 26 2017; 1 2012; 8316 2019; 56 2019; 14 2020; 59 2020; 11 2020; 99 2012; 59 2013; 8 2013; 9 2018; 5 2017; 71 2011; 20 2014; 15 2007; 2 2018; 34 2016; 49 2014; 7 1992; 4 2007; 27 2019; 7 2019; 9 2017; 63 2017; 2017 2012 2011 2015; 521 2020; 143 2018; 103 2009 2008 1997 2019; 347 2006; 5 2007 2019; 102 2006 2007; 95 2011; 4 2019; 141 2009; 26 2018; 19 2009; 36 2013; 37 2017; 90 2019; 40 2017; 16 2020 2010; 132 2019 2014; 36 2017 2016 2015 2014 2013 2011; 47 e_1_2_12_4_1 e_1_2_12_6_1 e_1_2_12_19_1 Ripley BD (e_1_2_12_47_1) 2007 e_1_2_12_2_1 e_1_2_12_17_1 e_1_2_12_38_1 Brenner S (e_1_2_12_22_1) 2007 e_1_2_12_20_1 e_1_2_12_41_1 e_1_2_12_43_1 e_1_2_12_64_1 e_1_2_12_24_1 e_1_2_12_45_1 Drucker H (e_1_2_12_26_1) 1997 e_1_2_12_68_1 e_1_2_12_60_1 e_1_2_12_28_1 e_1_2_12_49_1 Bengio Y (e_1_2_12_66_1) 2017 e_1_2_12_31_1 e_1_2_12_52_1 e_1_2_12_54_1 e_1_2_12_35_1 e_1_2_12_56_1 e_1_2_12_37_1 e_1_2_12_58_1 e_1_2_12_14_1 e_1_2_12_12_1 e_1_2_12_8_1 Aranda A (e_1_2_12_36_1) 2018; 5 e_1_2_12_10_1 e_1_2_12_50_1 e_1_2_12_3_1 e_1_2_12_5_1 e_1_2_12_18_1 e_1_2_12_16_1 e_1_2_12_39_1 Rupérez M (e_1_2_12_33_1) 2017 e_1_2_12_42_1 e_1_2_12_65_1 e_1_2_12_21_1 e_1_2_12_44_1 e_1_2_12_23_1 e_1_2_12_46_1 e_1_2_12_25_1 e_1_2_12_48_1 e_1_2_12_67_1 e_1_2_12_61_1 Lane DM (e_1_2_12_62_1) 2017 e_1_2_12_40_1 Srivastava N (e_1_2_12_51_1) 2014; 15 Tanenbaum AS (e_1_2_12_63_1) 2015 e_1_2_12_27_1 e_1_2_12_29_1 e_1_2_12_30_1 e_1_2_12_53_1 e_1_2_12_32_1 e_1_2_12_55_1 e_1_2_12_34_1 e_1_2_12_57_1 e_1_2_12_59_1 e_1_2_12_15_1 e_1_2_12_13_1 e_1_2_12_11_1 e_1_2_12_7_1 e_1_2_12_9_1 |
| References_xml | – volume: 90 start-page: 116 year: 2017 end-page: 124 article-title: A finite element‐based machine learning approach for modeling the mechanical behavior of the breast tissues under compression in real‐time publication-title: Comput Biol Med – volume: 143 start-page: 113083 year: 2020 article-title: Real‐time biomechanical modeling of the liver using machine learning models trained on finite element method simulations publication-title: Expert Syst Appl – volume: 56 start-page: 184 year: 2019 end-page: 192 article-title: A methodology for generating four‐dimensional arterial spin labeling MR angiography virtual phantoms publication-title: Med Image Anal – volume: 11 start-page: 181 year: 2020 end-page: 217 article-title: Which academic search systems are suitable for systematic reviews or meta‐analyses? Evaluating retrieval qualities of Google Scholar, PubMed, and 26 other resources publication-title: Res Synth Meth – volume: 14 start-page: 1147 year: 2019 end-page: 1155 article-title: Learning soft tissue behavior of organs for surgical navigation with convolutional neural networks publication-title: Int J Comput Assist Radiol Surg – volume: 36 start-page: 1253 year: 2014 end-page: 1265 article-title: A neural network approach for determining gait modifications to reduce the contact force in knee joint implant publication-title: Med Eng Phys – start-page: 3 year: 2013 end-page: 30 – volume: 7 start-page: 1247 year: 2014 end-page: 1250 article-title: Root mean square error (RMSE) or mean absolute error (MAE)?–arguments against avoiding rmse in the literature publication-title: Geosci Model Develp – volume: 16 start-page: 1519 year: 2017 end-page: 1533 article-title: A machine learning approach to investigate the relationship between shape features and numerically predicted risk of ascending aortic aneurysm publication-title: Biomech Model Mechanobiol – volume: 2 start-page: 1 year: 2007 end-page: 10 article-title: A real‐time navigation system for laparoscopic surgery based on three‐dimensional ultrasound using magneto‐optic hybrid tracking configuration publication-title: Int J Comput Assist Radiol Surg – start-page: 30 year: 2009 end-page: 34 – volume: 8 start-page: 1 year: 2017 end-page: 16 article-title: Statistically reinforced machine learning for nonlinear patterns and variable interactions publication-title: Ecosphere – volume: 71 start-page: 342 year: 2017 end-page: 357 article-title: A framework for modelling the biomechanical behaviour of the human liver during breathing in real time using machine learning publication-title: Expert Syst Appl – volume: 40 start-page: 849 year: 2019 end-page: 863 article-title: Prediction of displacement in the equine third metacarpal bone using a neural network prediction algorithm publication-title: Biocybern Biomed Eng – year: 2014 – volume: 49 start-page: 631 year: 2016 end-page: 637 article-title: Determination of the mechanical and physical properties of cartilage by coupling poroelastic‐based finite element models of indentation with artificial neural networks publication-title: J Biomech – start-page: 157 year: 2012 end-page: 175 – volume: 102 start-page: 103527 year: 2019 article-title: Prediction of load in a long bone using an artificial neural network prediction algorithm publication-title: J Mech Behav Biomed Mater – volume: 4 start-page: 473 year: 1992 end-page: 493 article-title: Simplifying neural networks by soft weight‐sharing publication-title: Neural Comput – volume: 103 start-page: 34 year: 2018 end-page: 43 article-title: Prediction of spinal curve progression in adolescent idiopathic scoliosis using random forest regression publication-title: Comput Biol Med – volume: 9 start-page: 2775 year: 2019 article-title: Survey of finite element method‐based real‐time simulations publication-title: App Sci – volume: 37 start-page: 5260 year: 2013 end-page: 5276 article-title: Neural network prediction of load from the morphology of trabecular bone publication-title: Appl Math Model – volume: 7 start-page: 117 year: 2019 article-title: Prediction of left ventricular mechanics using machine learning publication-title: Front Phys – volume: 8316 start-page: 83160J year: 2012 – volume: 63 start-page: 227 year: 2017 end-page: 235 article-title: A deep learning approach to estimate chemically‐treated collagenous tissue nonlinear anisotropic stress‐strain responses from microscopy images publication-title: Acta Biomater – start-page: 247 year: 2016 end-page: 253 – volume: 7 start-page: 156779 year: 2019 end-page: 156786 article-title: Predicting three‐dimensional ground reaction forces in running by using artificial neural networks and lower body kinematics publication-title: IEEE Acc – volume: 34 start-page: e3103 year: 2018 article-title: A machine learning approach as a surrogate of finite element analysis–based inverse method to estimate the zero‐pressure geometry of human thoracic aorta publication-title: Int J Num Meth Biomed Eng – year: 2019 – volume: 347 start-page: 201 year: 2019 end-page: 217 article-title: Estimation of in vivo constitutive parameters of the aortic wall using a machine learning approach publication-title: Comput Methods Appl Mech Eng – volume: 20 start-page: 289 year: 2011 end-page: 308 article-title: A physics‐driven neural networks‐based simulation system (Phynness) for multimodal interactive virtual environments involving nonlinear deformable objects publication-title: Presence: Teleoperators Virtual Environ – start-page: 1 year: 2020 end-page: 4 – year: 2015 – volume: 9 start-page: e52 year: 2013 end-page: e60 article-title: A framework for predicting three‐dimensional prostate deformation in real time publication-title: Int J Med Robot Comput Ass Surg – start-page: 431 year: 2019 end-page: 440 – start-page: 2842 year: 2011 end-page: 2846 – volume: 132 start-page: 114502‐1 year: 2010 end-page: 114502‐5 article-title: Application of neural networks and finite element computation for multiscale simulation of bone remodeling publication-title: J Biomech Eng – volume: 47 start-page: 835 year: 2011 end-page: 842 article-title: Numerical procedure for multiscale bone adaptation prediction based on neural networks and finite element simulation publication-title: Finite Elem Anal Des – volume: 4 start-page: 868 year: 2011 end-page: 878 article-title: Apparent damage accumulation in cancellous bone using neural networks publication-title: J Mech Behav Biomed Mater – volume: 95 start-page: 442 year: 2007 article-title: Comparing test searches in PubMed and Google Scholar publication-title: J Med Libr Ass – volume: 8 start-page: 475 year: 2013 end-page: 482 article-title: Application of neural networks for the prediction of cartilage stress in a musculoskeletal system publication-title: Biomed Signal Process Control – volume: 36 start-page: 284 year: 2009 end-page: 293 article-title: A model for mechanical adaptation of trabecular bone incorporating cellular accommodation and effects of microdamage and disuse publication-title: Mech Res Commun – year: 2007 – volume: 2017 start-page: 1 year: 2017 end-page: 8 article-title: Predictive behavior of a computational foot/ankle model through artificial neural networks publication-title: Comput Math Meth Med – volume: 99 start-page: 109544 year: 2020 article-title: A feasibility study of deep learning for predicting hemodynamics of human thoracic aorta publication-title: J Biomech – start-page: 583 year: 2017 end-page: 592 – volume: 141 start-page: 084502‐1 year: 2019 end-page: 084502‐9 article-title: Bridging finite element and machine learning modeling: stress prediction of arterial walls in atherosclerosis publication-title: J Biomech Eng – volume: 5 start-page: 253 year: 2006 end-page: 261 article-title: Biomechanics of the C5‐C6 spinal unit before and after placement of a disc prosthesis publication-title: Biomech Model Mechanobiol – volume: 521 start-page: 436 year: 2015 end-page: 444 article-title: Deep learning publication-title: Nature – start-page: 155 year: 1997 end-page: 161 – volume: 5 start-page: 1 year: 2018 end-page: 13 article-title: Study on cerebral aneurysms: Rupture risk prediction using geometrical parameters and wall shear stress with CFD and machine learning tools publication-title: Mach Lear Appl – volume: 59 start-page: 1155 year: 2012 end-page: 1161 article-title: Machine learning techniques as a helpful tool toward determination of plaque vulnerability publication-title: IEEE Trans Biomed Eng – volume: 19 start-page: 1236 year: 2018 end-page: 1246 article-title: Deep learning for healthcare: review, opportunities and challenges publication-title: Brief Bioinform – volume: 1 year: 2017 – volume: 59 start-page: 101569 year: 2020 article-title: Simulation of hyperelastic materials in real‐time using deep learning publication-title: Med Image Anal – start-page: 2996 year: 2013 end-page: 2999 – start-page: 742 year: 2008 end-page: 749 – volume: 42 start-page: 60 year: 2017 end-page: 88 article-title: A survey on deep learning in medical image analysis publication-title: Med Image Anal – volume: 26 start-page: 98 year: 2009 end-page: 117 article-title: Mean squared error: Love it or leave it? A new look at signal fidelity measures publication-title: IEEE Signal Process Mag – volume: 80 start-page: 39 year: 2017 end-page: 47 article-title: A machine learning approach for real‐time modelling of tissue deformation in image‐guided neurosurgery publication-title: Artif Intell Med – volume: 26 year: 2013 – volume: 15 start-page: 1929 year: 2014 end-page: 1958 article-title: Dropout: a simple way to prevent neural networks from overfitting publication-title: J Mach Learn Res – volume: 27 start-page: 54 year: 2007 end-page: 66 article-title: VR‐based simulators for training in minimally invasive surgery publication-title: IEEE Comput Graphics Appl – year: 2017 – start-page: 380 year: 2006 end-page: 385 – ident: e_1_2_12_19_1 doi: 10.1007/978-981-15-1925-3_31 – volume: 15 start-page: 1929 year: 2014 ident: e_1_2_12_51_1 article-title: Dropout: a simple way to prevent neural networks from overfitting publication-title: J Mach Learn Res – ident: e_1_2_12_41_1 doi: 10.1016/j.medengphy.2014.06.016 – ident: e_1_2_12_24_1 doi: 10.1002/jrsm.1378 – ident: e_1_2_12_52_1 doi: 10.1162/neco.1992.4.4.473 – ident: e_1_2_12_49_1 doi: 10.1007/978-1-4614-6849-3 – ident: e_1_2_12_9_1 doi: 10.1002/rcs.1493 – ident: e_1_2_12_40_1 doi: 10.1016/j.eswa.2016.11.037 – ident: e_1_2_12_42_1 doi: 10.1109/ACCESS.2019.2949699 – ident: e_1_2_12_7_1 doi: 10.1007/s11548-019-01965-7 – ident: e_1_2_12_30_1 doi: 10.1038/nature14539 – volume-title: Modern Operating Systems year: 2015 ident: e_1_2_12_63_1 – ident: e_1_2_12_4_1 doi: 10.3389/fphy.2019.00117 – ident: e_1_2_12_43_1 – ident: e_1_2_12_3_1 doi: 10.1109/EMBC.2013.6610170 – start-page: 583 volume-title: European Congress on Computational Methods in Applied Sciences and Engineering year: 2017 ident: e_1_2_12_33_1 – ident: e_1_2_12_48_1 doi: 10.1016/j.apm.2012.10.049 – ident: e_1_2_12_37_1 doi: 10.1007/s10237-017-0903-9 – ident: e_1_2_12_31_1 – ident: e_1_2_12_58_1 doi: 10.1016/j.cma.2018.12.030 – volume-title: The Mathematical Theory of Finite Element Methods year: 2007 ident: e_1_2_12_22_1 – ident: e_1_2_12_39_1 doi: 10.1115/1.4002536 – volume-title: Pattern Recognition and Neural Networks year: 2007 ident: e_1_2_12_47_1 – ident: e_1_2_12_55_1 doi: 10.1016/j.finel.2011.02.014 – ident: e_1_2_12_64_1 doi: 10.1002/ecs2.1976 – ident: e_1_2_12_44_1 doi: 10.1016/j.actbio.2017.09.025 – ident: e_1_2_12_38_1 doi: 10.1016/j.artmed.2017.07.004 – ident: e_1_2_12_53_1 – ident: e_1_2_12_34_1 doi: 10.1109/ICDMW.2016.0042 – ident: e_1_2_12_5_1 doi: 10.1109/TBME.2012.2185495 – ident: e_1_2_12_27_1 doi: 10.1017/CBO9781107298019 – volume: 5 start-page: 1 year: 2018 ident: e_1_2_12_36_1 article-title: Study on cerebral aneurysms: Rupture risk prediction using geometrical parameters and wall shear stress with CFD and machine learning tools publication-title: Mach Lear Appl – ident: e_1_2_12_11_1 doi: 10.1162/PRES_a_00054 – ident: e_1_2_12_50_1 doi: 10.1002/cnm.3103 – ident: e_1_2_12_57_1 doi: 10.1155/2017/3602928 – ident: e_1_2_12_14_1 doi: 10.1007/s10237-006-0015-4 – ident: e_1_2_12_10_1 doi: 10.1109/WHC.2009.4810896 – ident: e_1_2_12_6_1 doi: 10.1007/978-3-540-85990-1_89 – ident: e_1_2_12_29_1 doi: 10.1093/bib/bbx044 – ident: e_1_2_12_12_1 doi: 10.1117/12.911171 – ident: e_1_2_12_17_1 doi: 10.1007/8415_2012_146 – ident: e_1_2_12_20_1 doi: 10.1115/1.4043290 – ident: e_1_2_12_32_1 doi: 10.1016/j.media.2019.101569 – ident: e_1_2_12_45_1 doi: 10.1016/j.jmbbm.2019.103527 – ident: e_1_2_12_67_1 doi: 10.5194/gmd-7-1247-2014 – ident: e_1_2_12_18_1 doi: 10.1016/j.compbiomed.2018.09.029 – ident: e_1_2_12_54_1 doi: 10.1109/ICSMC.2011.6084112 – ident: e_1_2_12_25_1 doi: 10.3163/1536-5050.95.4.442 – ident: e_1_2_12_8_1 doi: 10.1016/j.eswa.2019.113083 – ident: e_1_2_12_23_1 doi: 10.3390/app9142775 – ident: e_1_2_12_59_1 doi: 10.1016/j.jbiomech.2019.109544 – ident: e_1_2_12_13_1 doi: 10.1007/s11548-007-0078-4 – ident: e_1_2_12_21_1 doi: 10.1007/978-3-030-42428-2_4 – volume-title: Deep Learning year: 2017 ident: e_1_2_12_66_1 – ident: e_1_2_12_46_1 doi: 10.1016/j.bbe.2019.09.001 – ident: e_1_2_12_28_1 doi: 10.1016/j.media.2017.07.005 – ident: e_1_2_12_2_1 doi: 10.1109/MCG.2007.51 – ident: e_1_2_12_65_1 doi: 10.1007/978-1-4419-9326-7_5 – ident: e_1_2_12_68_1 doi: 10.1016/j.bspc.2013.04.004 – ident: e_1_2_12_16_1 doi: 10.1016/j.jmbbm.2011.03.002 – volume-title: An Introduction to Statistics year: 2017 ident: e_1_2_12_62_1 – start-page: 155 volume-title: Advances in Neural Information Processing Systems year: 1997 ident: e_1_2_12_26_1 – ident: e_1_2_12_15_1 doi: 10.1016/j.mechrescom.2008.10.004 – ident: e_1_2_12_56_1 doi: 10.1016/j.jbiomech.2015.12.014 – ident: e_1_2_12_35_1 doi: 10.1016/j.compbiomed.2017.09.019 – ident: e_1_2_12_61_1 doi: 10.1016/j.media.2019.06.002 – ident: e_1_2_12_60_1 doi: 10.1109/MSP.2008.930649 |
| SSID | ssj0006350 |
| Score | 2.5953948 |
| SecondaryResourceType | review_article |
| Snippet | Purpose
The finite element method (FEM) is the preferred method to simulate phenomena in anatomical structures. However, purely FEM‐based mechanical... The finite element method (FEM) is the preferred method to simulate phenomena in anatomical structures. However, purely FEM-based mechanical simulations... |
| SourceID | proquest pubmed crossref wiley |
| SourceType | Aggregation Database Index Database Enrichment Source Publisher |
| StartPage | 7 |
| SubjectTerms | Algorithms Biomechanical Phenomena biomechanical simulation, finite element modelling, machine learning, neural network, real‐time Computer Simulation Finite Element Analysis Machine Learning |
| Title | Real‐time biomechanics using the finite element method and machine learning: Review and perspective |
| URI | https://onlinelibrary.wiley.com/doi/abs/10.1002%2Fmp.14602 https://www.ncbi.nlm.nih.gov/pubmed/33222226 https://www.proquest.com/docview/2463606487 |
| Volume | 48 |
| WOSCitedRecordID | wos000596112800001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVWIB databaseName: Wiley Online Library Full Collection 2020 customDbUrl: eissn: 2473-4209 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0006350 issn: 0094-2405 databaseCode: DRFUL dateStart: 19970101 isFulltext: true titleUrlDefault: https://onlinelibrary.wiley.com providerName: Wiley-Blackwell |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1LS8NAEB60PvDio77qo6wgegqWpHl5E7V4aEspFnoLm82kCDYWo579Cf5Gf4kzu0lLUUHwEBaymxAyO7vz7cx8A3AqCfu7iXQsdOLAaiaxtGTacC0fkyC00aNWJwq3_W43GA7DXhFVybkwhh9ieuDGmqHXa1ZwGecXM9LQ8YS1nHkkl2yatm4Flm76rUF7ug7TVmoSUMIm-xDcknq2YV-Uz85vRt8szHmDVe84rY3_fOsmrBd2prgyE2MLFjCrwmqn8KRXYUWHfqp8G7BPtuLn-wdXmRc6G5-TgalLcEz8SJCFKNIHNk0FmlhzYcpOC5klYqyDMVEU1SdGl8J4G3TnZJbJuQOD1u399Z1VFF-wlEPbmYWEnFSiCH80EyTY4kmPye3iAD3CjLEMZWJL3_c89MOGkyoZBNJ2JftJVYhp4uxCJXvKcB8Es5aldC8MCPu5DkrCZHEDnZSupqdkDc5LKUSqYCbnAhmPkeFUtqPxJNL_rwYn05ETw8bx05hSkBGpCvs_ZIZPr3lka3I0jyBaDfaMhKdvcdjjRKZoDc60IH99fdTp6fbgrwMPYc3mOBh9bHMElZfnVzyGZfX28pA_12HRHwb1Yup-AeTh8Gs |
| linkProvider | Wiley-Blackwell |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1ZS8RADA7ier14H-s5guhTceldfRJ1UdxdZFnBtzKdpiK4ddnDZ3-Cv9FfYjLTrogKgg9loDMtpUkmyST5AnAgyff3UulY6CSh5aaJtGRW86wA0zCy0adRFwo3glYrvL-PbifgtKyFMfgQ4wM3lgy9X7OA84H08SdqaLfHYs5AkhWXuIjYu3LRrt81xhsx6VJTgRK5HETwSuzZmn1cPvtVG30zMb9arFrl1Bf-9bGLMF9YmuLMsMYSTGC-DDPNIpa-DNM6-VMNVgDbZC2-v75xn3mh6_G5HJimBGfFPwiyEUX2yMapQJNtLkzjaSHzVHR1OiaKov_Ew4kw8QY92fus5VyFu_pl5_zKKtovWMohhWYh-U4qVeSBuCmS4-JLn-HtkhB98hoTGcnUlkHg-xhENSdTMgyl7UmOlKoIs9RZg8n8OccNEIxbltG9KCTvz3NQkleW1NDJ6HJ9JatwVJIhVgU2ObfIeIoNqrIdd3ux_n9V2B-v7Bk8jp_WlJSMSVg4AiJzfB4NYlvDo_nkpFVh3ZB4_BaHY05kjFbhUFPy19fHzVs9bv514R7MXnWajbhx3brZgjmbs2L0Ic42TA77I9yBKfUyfBz0dwsO_gCgXfNz |
| linkToPdf | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1LS8QwEB5kfeDF92N9RhA9FZe-qydRF8V1WUTBW0mTqQhuLa569if4G_0lziTtiqggeCiBJi2lk0nmy8x8A7AtCfsHWnoOelns-DqTjsxbgROhjhMXQ2pNonAn6nbjm5ukNwIHdS6M5YcYHrixZpj1mhUcS53vfbKG9ktWcyaSHPW5hkwDRo8v29ed4UJMe6nNQEl8diIENfdsy92rn_26G30zMb9arGbLaU__62NnYKqyNMWhnRqzMILFHExcVL70ORg3wZ9qMA94Sdbi--sb15kXJh-f04GpS3BU_K0gG1Hkd2ycCrTR5sIWnhay0KJvwjFRVPUnbveF9TeYzvIzl3MBrtsnV0enTlV-wVEebWgOEnZSWhEC8TUScAllyPR2WYwhocZMJlK7MorCEKOk5eVKxrF0A8meUpVgrr1FaBQPBS6DYN6ynO4lMaG_wENJqCxroZfT5YdKNmG3FkOqKm5yLpFxn1pWZTftl6n5f03YGo4sLR_HT2NqSaakLOwBkQU-PA9S19CjhQTSmrBkRTx8i8c-JzJGm7BjJPnr69OLnmlX_jpwEyZ6x-20c9Y9X4VJl4NizBnOGjSeHp9xHcbUy9Pd4HGjmsAfPlDy7g |
| 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=Real%E2%80%90time+biomechanics+using+the+finite+element+method+and+machine+learning%3A+Review+and+perspective&rft.jtitle=Medical+physics+%28Lancaster%29&rft.au=Phellan%2C+Renzo&rft.au=Hachem%2C+Bahe&rft.au=Clin%2C+Julien&rft.au=Mac%E2%80%90Thiong%2C+Jean%E2%80%90Marc&rft.date=2021-01-01&rft.issn=0094-2405&rft.eissn=2473-4209&rft.volume=48&rft.issue=1&rft.spage=7&rft.epage=18&rft_id=info:doi/10.1002%2Fmp.14602&rft.externalDBID=n%2Fa&rft.externalDocID=10_1002_mp_14602 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0094-2405&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0094-2405&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0094-2405&client=summon |