Retraining an Artificial Intelligence Algorithm to Calculate Left Ventricular Ejection Fraction in Pediatrics
Identifying patients with low left ventricular ejection fraction (LVEF) and monitoring LVEF responses to treatment are important clinical goals. Can a deep-learning algorithm predict pediatric LVEF within clinically acceptable error? The study authors wanted to fine-tune an adult deep-learning algor...
Uloženo v:
| Vydáno v: | Journal of cardiothoracic and vascular anesthesia Ročník 36; číslo 9; s. 3610 - 3616 |
|---|---|
| Hlavní autoři: | , , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
United States
Elsevier Inc
01.09.2022
|
| Témata: | |
| ISSN: | 1053-0770, 1532-8422, 1532-8422 |
| On-line přístup: | Získat plný text |
| Tagy: |
Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
|
| Abstract | Identifying patients with low left ventricular ejection fraction (LVEF) and monitoring LVEF responses to treatment are important clinical goals. Can a deep-learning algorithm predict pediatric LVEF within clinically acceptable error?
The study authors wanted to fine-tune an adult deep-learning algorithm to calculate LVEF in pediatric patients. A priori, their objective was to refine the algorithm to perform LVEF calculation with a mean absolute error (MAE) ≤5%.
A quaternary pediatric hospital
A convenience sample (n = 321) of echocardiograms from newborns to 18 years old with normal cardiac anatomy or hemodynamically insignificant anomalies. Echocardiograms were chosen from a group of healthy controls with known normal LVEF (n = 267) and a dilated cardiomyopathy patient group with reduced LVEF (n = 54).
The artificial intelligence model EchoNet-Dynamic was tested on this data set and then retrained, tested, and further validated to improve LVEF calculation. The gold standard value was LVEF calculated by clinical experts.
In a random subset of subjects (n = 40) not analyzed prior to selection of the final model, EchoNet-Dynamic calculated LVEF with a MAE of 8.39%, R2 = 0.47 without, and MAE 4.47%, R2 = 0.87 with fine-tuning. Bland-Altman analysis suggested that the model slightly underestimates LVEF (bias = –2.42%). The 95% limits of agreement between actual and calculated values were –12.32% to 7.47%.
The fine-tuned model calculates LVEF in a range of pediatric patients within clinically acceptable error. Potential advantages include reducing operator error in LVEF calculation and supporting independent LVEF assessment by inexperienced users.
[Display omitted] |
|---|---|
| AbstractList | Identifying patients with low left ventricular ejection fraction (LVEF) and monitoring LVEF responses to treatment are important clinical goals. Can a deep-learning algorithm predict pediatric LVEF within clinically acceptable error?
The study authors wanted to fine-tune an adult deep-learning algorithm to calculate LVEF in pediatric patients. A priori, their objective was to refine the algorithm to perform LVEF calculation with a mean absolute error (MAE) ≤5%.
A quaternary pediatric hospital
A convenience sample (n = 321) of echocardiograms from newborns to 18 years old with normal cardiac anatomy or hemodynamically insignificant anomalies. Echocardiograms were chosen from a group of healthy controls with known normal LVEF (n = 267) and a dilated cardiomyopathy patient group with reduced LVEF (n = 54).
The artificial intelligence model EchoNet-Dynamic was tested on this data set and then retrained, tested, and further validated to improve LVEF calculation. The gold standard value was LVEF calculated by clinical experts.
In a random subset of subjects (n = 40) not analyzed prior to selection of the final model, EchoNet-Dynamic calculated LVEF with a MAE of 8.39%, R2 = 0.47 without, and MAE 4.47%, R2 = 0.87 with fine-tuning. Bland-Altman analysis suggested that the model slightly underestimates LVEF (bias = –2.42%). The 95% limits of agreement between actual and calculated values were –12.32% to 7.47%.
The fine-tuned model calculates LVEF in a range of pediatric patients within clinically acceptable error. Potential advantages include reducing operator error in LVEF calculation and supporting independent LVEF assessment by inexperienced users.
[Display omitted] Identifying patients with low left ventricular ejection fraction (LVEF) and monitoring LVEF responses to treatment are important clinical goals. Can a deep-learning algorithm predict pediatric LVEF within clinically acceptable error?OBJECTIVESIdentifying patients with low left ventricular ejection fraction (LVEF) and monitoring LVEF responses to treatment are important clinical goals. Can a deep-learning algorithm predict pediatric LVEF within clinically acceptable error?The study authors wanted to fine-tune an adult deep-learning algorithm to calculate LVEF in pediatric patients. A priori, their objective was to refine the algorithm to perform LVEF calculation with a mean absolute error (MAE) ≤5%.DESIGNThe study authors wanted to fine-tune an adult deep-learning algorithm to calculate LVEF in pediatric patients. A priori, their objective was to refine the algorithm to perform LVEF calculation with a mean absolute error (MAE) ≤5%.A quaternary pediatric hospital PARTICIPANTS: A convenience sample (n = 321) of echocardiograms from newborns to 18 years old with normal cardiac anatomy or hemodynamically insignificant anomalies. Echocardiograms were chosen from a group of healthy controls with known normal LVEF (n = 267) and a dilated cardiomyopathy patient group with reduced LVEF (n = 54).SETTINGA quaternary pediatric hospital PARTICIPANTS: A convenience sample (n = 321) of echocardiograms from newborns to 18 years old with normal cardiac anatomy or hemodynamically insignificant anomalies. Echocardiograms were chosen from a group of healthy controls with known normal LVEF (n = 267) and a dilated cardiomyopathy patient group with reduced LVEF (n = 54).The artificial intelligence model EchoNet-Dynamic was tested on this data set and then retrained, tested, and further validated to improve LVEF calculation. The gold standard value was LVEF calculated by clinical experts.INTERVENTIONSThe artificial intelligence model EchoNet-Dynamic was tested on this data set and then retrained, tested, and further validated to improve LVEF calculation. The gold standard value was LVEF calculated by clinical experts.In a random subset of subjects (n = 40) not analyzed prior to selection of the final model, EchoNet-Dynamic calculated LVEF with a MAE of 8.39%, R2 = 0.47 without, and MAE 4.47%, R2 = 0.87 with fine-tuning. Bland-Altman analysis suggested that the model slightly underestimates LVEF (bias = -2.42%). The 95% limits of agreement between actual and calculated values were -12.32% to 7.47%.MEASUREMENTS AND MAIN RESULTSIn a random subset of subjects (n = 40) not analyzed prior to selection of the final model, EchoNet-Dynamic calculated LVEF with a MAE of 8.39%, R2 = 0.47 without, and MAE 4.47%, R2 = 0.87 with fine-tuning. Bland-Altman analysis suggested that the model slightly underestimates LVEF (bias = -2.42%). The 95% limits of agreement between actual and calculated values were -12.32% to 7.47%.The fine-tuned model calculates LVEF in a range of pediatric patients within clinically acceptable error. Potential advantages include reducing operator error in LVEF calculation and supporting independent LVEF assessment by inexperienced users.CONCLUSIONSThe fine-tuned model calculates LVEF in a range of pediatric patients within clinically acceptable error. Potential advantages include reducing operator error in LVEF calculation and supporting independent LVEF assessment by inexperienced users. Identifying patients with low left ventricular ejection fraction (LVEF) and monitoring LVEF responses to treatment are important clinical goals. Can a deep-learning algorithm predict pediatric LVEF within clinically acceptable error? The study authors wanted to fine-tune an adult deep-learning algorithm to calculate LVEF in pediatric patients. A priori, their objective was to refine the algorithm to perform LVEF calculation with a mean absolute error (MAE) ≤5%. A quaternary pediatric hospital PARTICIPANTS: A convenience sample (n = 321) of echocardiograms from newborns to 18 years old with normal cardiac anatomy or hemodynamically insignificant anomalies. Echocardiograms were chosen from a group of healthy controls with known normal LVEF (n = 267) and a dilated cardiomyopathy patient group with reduced LVEF (n = 54). The artificial intelligence model EchoNet-Dynamic was tested on this data set and then retrained, tested, and further validated to improve LVEF calculation. The gold standard value was LVEF calculated by clinical experts. In a random subset of subjects (n = 40) not analyzed prior to selection of the final model, EchoNet-Dynamic calculated LVEF with a MAE of 8.39%, R = 0.47 without, and MAE 4.47%, R = 0.87 with fine-tuning. Bland-Altman analysis suggested that the model slightly underestimates LVEF (bias = -2.42%). The 95% limits of agreement between actual and calculated values were -12.32% to 7.47%. The fine-tuned model calculates LVEF in a range of pediatric patients within clinically acceptable error. Potential advantages include reducing operator error in LVEF calculation and supporting independent LVEF assessment by inexperienced users. |
| Author | Slorach, Cameron Ufkes, Steven Erdman, Lauren Mertens, Luc Taylor, Katherine Zuercher, Mael |
| Author_xml | – sequence: 1 givenname: Mael orcidid: 0000-0002-9644-2940 surname: Zuercher fullname: Zuercher, Mael organization: Department of Anesthesia and Pain Medicine, The Hospital for Sick Children, Toronto, Ontario, Canada – sequence: 2 givenname: Steven orcidid: 0000-0001-7460-8666 surname: Ufkes fullname: Ufkes, Steven organization: Division of Genetics and Genome Biology, Hospital for Sick Children, Research Institute, Toronto, Ontario, Canada – sequence: 3 givenname: Lauren surname: Erdman fullname: Erdman, Lauren organization: Division of Genetics and Genome Biology, Hospital for Sick Children, Research Institute, Toronto, Ontario, Canada – sequence: 4 givenname: Cameron surname: Slorach fullname: Slorach, Cameron organization: Department of Cardiology, The Hospital for Sick Children, Toronto, Ontario, Canada – sequence: 5 givenname: Luc surname: Mertens fullname: Mertens, Luc organization: Department of Cardiology, The Hospital for Sick Children, Toronto, Ontario, Canada – sequence: 6 givenname: Katherine orcidid: 0000-0001-8663-3578 surname: Taylor fullname: Taylor, Katherine email: Katherine.taylor@sickkids.ca organization: Department of Anesthesia and Pain Medicine, The Hospital for Sick Children, Toronto, Ontario, Canada |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/35641411$$D View this record in MEDLINE/PubMed |
| BookMark | eNqFkc1u1DAUhS1URH_gBVggL9kk-DfOIDajUQuVRipCwNby2DeDg-MU21Opb4-jaTddtCtfWee70v3OOTqJcwSE3lPSUiL5p7Ed76xpGWGsJbIlRLxCZ1Ry1vSCsZM611RDlCKn6DznkRBKpVRv0CmXnaCC0jM0_YCSjI8-7rGJeJ2KH7z1JuDrWCAEv4doAa_Dfk6-_JlwmfHGBHsIpgDewlDwb4gl-eUn4csRbPFzxFfJHAcf8Xdw3iyR_Ba9HkzI8O7hvUC_ri5_br4125uv15v1trGCqNIMltidEJIQs-KiNyvhuOk4pcCUGxRQN3RSEqMYH_odcNevXKeUZWKlmKvMBfp43Hub5n8HyEVPPtt6jokwH7JmXUWrt57X6IeH6GE3gdO3yU8m3etHRTXQHwM2zTknGLT1xSy3LeKCpkQvnvWolzb00oYmUtc2KsqeoI_bn4W-HCGogu48JJ2tX0pwPlW52s3-efzzE9yGWq814S_cvwT_B7iqtyQ |
| CitedBy_id | crossref_primary_10_1016_j_echo_2023_01_015 crossref_primary_10_1109_ACCESS_2025_3545829 crossref_primary_10_3390_children12010014 crossref_primary_10_1088_2057_1976_ad7594 crossref_primary_10_3390_jcm11237072 crossref_primary_10_1017_pcm_2023_4 |
| Cites_doi | 10.1016/j.echo.2010.03.019 10.1016/j.jcmg.2011.06.004 10.1111/echo.14086 10.1186/s13089-017-0075-y 10.1161/JAHA.118.009124 10.1016/j.cardfail.2012.03.001 10.1016/j.ultrasmedbio.2018.07.024 10.1038/s41586-020-2145-8 10.1002/clc.20262 10.1007/s00246-010-9832-4 10.1093/ehjci/jev014 10.1016/j.echo.2019.05.025 10.1111/echo.15025 10.1038/s41746-019-0216-8 10.1007/s00246-013-0835-9 10.1016/j.echo.2015.03.004 10.1186/s13054-020-2787-9 10.1016/j.pedneo.2017.01.001 10.1093/eurheartj/ehw128 10.3390/jcm10071391 10.1213/ANE.0b013e3181c9f927 10.1186/s13089-016-0052-x 10.1101/2021.03.29.437045 |
| ContentType | Journal Article |
| Copyright | 2022 Crown Copyright © 2022. Published by Elsevier Inc. All rights reserved. |
| Copyright_xml | – notice: 2022 – notice: Crown Copyright © 2022. Published by Elsevier Inc. All rights reserved. |
| DBID | AAYXX CITATION NPM 7X8 |
| DOI | 10.1053/j.jvca.2022.05.004 |
| DatabaseName | CrossRef PubMed MEDLINE - Academic |
| DatabaseTitle | CrossRef PubMed MEDLINE - Academic |
| DatabaseTitleList | MEDLINE - Academic PubMed |
| 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 |
| EISSN | 1532-8422 |
| EndPage | 3616 |
| ExternalDocumentID | 35641411 10_1053_j_jvca_2022_05_004 S1053077022003299 |
| Genre | Journal Article |
| GroupedDBID | --- --K --M .1- .FO .GJ .~1 0R~ 1B1 1P~ 1RT 1~. 1~5 4.4 457 4G. 53G 5GY 5RE 5VS 7-5 71M 8P~ 9JM AABNK AAEDT AAEDW AAIKJ AAKOC AALRI AAOAW AAQFI AAQQT AAQXK AATTM AAXKI AAXUO AAYWO ABBQC ABFRF ABJNI ABMAC ABMZM ABOCM ABWVN ABXDB ACDAQ ACGFO ACGFS ACIEU ACLOT ACRLP ACRPL ACVFH ADBBV ADCNI ADEZE ADMUD ADNMO AEBSH AEFWE AEIPS AEKER AENEX AEUPX AEVXI AFJKZ AFPUW AFRHN AFTJW AFXIZ AGHFR AGQPQ AGUBO AGYEJ AHHHB AIEXJ AIGII AIIUN AIKHN AITUG AJRQY AJUYK AKBMS AKRWK AKYEP ALMA_UNASSIGNED_HOLDINGS AMRAJ ANKPU ANZVX APXCP ASPBG AVWKF AXJTR AZFZN BKOJK BLXMC BNPGV CAG COF CS3 DU5 EBS EFJIC EFKBS EFLBG EJD EO8 EO9 EP2 EP3 F5P FDB FEDTE FGOYB FIRID FNPLU FYGXN G-2 G-Q GBLVA HEB HMK HMO HVGLF HZ~ IHE J1W J5H K-O KOM M29 M41 MO0 N9A O-L O9- O90 OAUVE OL- OZT P-8 P-9 P2P PC. Q38 R2- ROL RPZ SAE SCC SDF SDG SDP SEL SES SEW SJN SPCBC SSH SSZ T5K UHS UNMZH UV1 WUQ Z5R ~G- ~HD AACTN AAIAV ABLVK ABYKQ AFKWA AJBFU AJOXV AMFUW LCYCR RIG ZA5 9DU AAYXX CITATION AFCTW NPM 7X8 |
| ID | FETCH-LOGICAL-c407t-fc0cb44500a9348a94d3a6311e27df7e1df6550a723f8be3d89d677c24972da93 |
| ISICitedReferencesCount | 5 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000863663200023&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 1053-0770 1532-8422 |
| IngestDate | Sun Sep 28 08:56:35 EDT 2025 Thu Apr 03 06:59:37 EDT 2025 Sat Nov 29 06:57:34 EST 2025 Tue Nov 18 21:38:37 EST 2025 Fri Feb 23 02:41:22 EST 2024 Tue Oct 14 19:30:16 EDT 2025 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 9 |
| Keywords | deep learning cardiac function artificial intelligence pediatric patients |
| Language | English |
| License | Crown Copyright © 2022. Published by Elsevier Inc. All rights reserved. |
| LinkModel | OpenURL |
| MergedId | FETCHMERGED-LOGICAL-c407t-fc0cb44500a9348a94d3a6311e27df7e1df6550a723f8be3d89d677c24972da93 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
| ORCID | 0000-0002-9644-2940 0000-0001-8663-3578 0000-0001-7460-8666 |
| PMID | 35641411 |
| PQID | 2672320283 |
| PQPubID | 23479 |
| PageCount | 7 |
| ParticipantIDs | proquest_miscellaneous_2672320283 pubmed_primary_35641411 crossref_citationtrail_10_1053_j_jvca_2022_05_004 crossref_primary_10_1053_j_jvca_2022_05_004 elsevier_sciencedirect_doi_10_1053_j_jvca_2022_05_004 elsevier_clinicalkey_doi_10_1053_j_jvca_2022_05_004 |
| PublicationCentury | 2000 |
| PublicationDate | 2022-09-01 |
| PublicationDateYYYYMMDD | 2022-09-01 |
| PublicationDate_xml | – month: 09 year: 2022 text: 2022-09-01 day: 01 |
| PublicationDecade | 2020 |
| PublicationPlace | United States |
| PublicationPlace_xml | – name: United States |
| PublicationTitle | Journal of cardiothoracic and vascular anesthesia |
| PublicationTitleAlternate | J Cardiothorac Vasc Anesth |
| PublicationYear | 2022 |
| Publisher | Elsevier Inc |
| Publisher_xml | – name: Elsevier Inc |
| References | Rajpurkar P, Hannun AY, Haghpanahi M, et al. Cardiologist-level arrhythmia detection with convolutional neural networks. arXiv 2017:1707.01836v1:1-9. Kouris, Kostopoulos, Psarrou (bib0010) 2021; 38 Ouyang, He, Ghorbani (bib0015) 2020; 580 Ramamoorthy, Haberkern, Bhananker (bib0002) 2010; 110 Ponikowski, Voors, Anker (bib0007) 2016; 37 Akkus, Aly, Attia (bib0019) 2021; 10 Lang, Badano, Mor-Avi (bib0006) 2015; 16 Gandhi, Mosleh, Shen (bib0022) 2018; 35 Murphy, Smith, Ranger (bib0004) 2011; 32 Blanco, Volpicelli (bib0026) 2016; 8 Lee, Margossian, Sleeper (bib0013) 2014; 35 Accessed April 18, 2021. Ghorbani, Ouyang, Abid (bib0014) 2020; 3 Masarone, Valente, Rubino (bib0005) 2017; 58 O'Dell (bib0018) 2019; 8 Singh, Tissot, Fraga (bib0024) 2020; 24 Lopez, Colan, Frommelt (bib0011) 2010; 23 Frommelt, Minich, Trachtenberg (bib0012) 2019; 32 Østvik, Smistad, Aase (bib0023) 2019; 45 Accessed April 2021 Daubert, Yow, Barnhart (bib0009) 2015; 28 Accessed April 18 2021 Massin, Astadicko, Dessy (bib0001) 2008; 31 Wang Y, Haghpanah FS, Zhang X, et al. ID-Seg: An accurate and reliable infant deep learning segmentation framework for limbic structures [e-pub ahead of print]. bioRxiv. doi Rossano, Kim, Decker (bib0003) 2012; 18 Tran D, Wang H, Torresani L, et al. A closer look at spatiotemporal convolutions for action recognition. Available at Kingma DP, Ba J. Adam: A method for stochastic optimization. Available at Johri, Picard, Newell (bib0008) 2011; 4 De Marchi, Meineri (bib0025) 2017; 9 10.1053/j.jvca.2022.05.004_bib0017 Østvik (10.1053/j.jvca.2022.05.004_bib0023) 2019; 45 Rossano (10.1053/j.jvca.2022.05.004_bib0003) 2012; 18 Frommelt (10.1053/j.jvca.2022.05.004_bib0012) 2019; 32 Johri (10.1053/j.jvca.2022.05.004_bib0008) 2011; 4 Daubert (10.1053/j.jvca.2022.05.004_bib0009) 2015; 28 Singh (10.1053/j.jvca.2022.05.004_bib0024) 2020; 24 Akkus (10.1053/j.jvca.2022.05.004_bib0019) 2021; 10 10.1053/j.jvca.2022.05.004_bib0020 10.1053/j.jvca.2022.05.004_bib0021 Murphy (10.1053/j.jvca.2022.05.004_bib0004) 2011; 32 Gandhi (10.1053/j.jvca.2022.05.004_bib0022) 2018; 35 Ouyang (10.1053/j.jvca.2022.05.004_bib0015) 2020; 580 Lang (10.1053/j.jvca.2022.05.004_bib0006) 2015; 16 Blanco (10.1053/j.jvca.2022.05.004_bib0026) 2016; 8 De Marchi (10.1053/j.jvca.2022.05.004_bib0025) 2017; 9 Massin (10.1053/j.jvca.2022.05.004_bib0001) 2008; 31 Ramamoorthy (10.1053/j.jvca.2022.05.004_bib0002) 2010; 110 Kouris (10.1053/j.jvca.2022.05.004_bib0010) 2021; 38 Ponikowski (10.1053/j.jvca.2022.05.004_bib0007) 2016; 37 Lopez (10.1053/j.jvca.2022.05.004_bib0011) 2010; 23 Ghorbani (10.1053/j.jvca.2022.05.004_bib0014) 2020; 3 Masarone (10.1053/j.jvca.2022.05.004_bib0005) 2017; 58 Lee (10.1053/j.jvca.2022.05.004_bib0013) 2014; 35 10.1053/j.jvca.2022.05.004_bib0016 O'Dell (10.1053/j.jvca.2022.05.004_bib0018) 2019; 8 |
| References_xml | – volume: 9 start-page: 19 year: 2017 ident: bib0025 article-title: POCUS in perioperative medicine: A North American perspective publication-title: Crit Ultrasound J – volume: 28 start-page: 959 year: 2015 end-page: 968 ident: bib0009 article-title: Quality improvement implementation: Improving reproducibility in the echocardiography laboratory publication-title: J Am Soc Echocardiogr – volume: 32 start-page: 1331 year: 2019 end-page: 1338 ident: bib0012 article-title: Challenges with left ventricular functional parameters: The Pediatric Heart Network Normal Echocardiogram Database publication-title: J Am Soc Echocardiogr – volume: 110 start-page: 1376 year: 2010 end-page: 1382 ident: bib0002 article-title: Anesthesia-related cardiac arrest in children with heart disease: Data from the Pediatric Perioperative Cardiac Arrest (POCA) Registry publication-title: Anesth Analg – volume: 31 start-page: 388 year: 2008 end-page: 391 ident: bib0001 article-title: Epidemiology of heart failure in a tertiary pediatric center publication-title: Clin Cardiol – volume: 37 start-page: 2129 year: 2016 end-page: 2200 ident: bib0007 article-title: 2016 ESC Guidelines for the diagnosis and treatment of acute and chronic heart failure: The Task Force for the diagnosis and treatment of acute and chronic heart failure of the European Society of Cardiology (ESC) Developed with the special contribution of the Heart Failure Association (HFA) of the ESC publication-title: Eur Heart J – volume: 16 start-page: 233 year: 2015 end-page: 271 ident: bib0006 article-title: Recommendations for cardiac chamber quantification by echocardiography in adults: An update from the American Society of Echocardiography and the European Association of Cardiovascular Imaging publication-title: Eur Heart J Cardiovasc Imaging – volume: 38 start-page: 582 year: 2021 end-page: 589 ident: bib0010 article-title: Left ventricular ejection fraction and Global Longitudinal Strain variability between methodology and experience publication-title: Echocardiography – volume: 32 start-page: 139 year: 2011 end-page: 144 ident: bib0004 article-title: General anesthesia for children with severe heart failure publication-title: Pediatr Cardiol – volume: 8 year: 2019 ident: bib0018 article-title: Accuracy of left ventricular cavity volume and ejection fraction for conventional estimation methods and 3D surface fitting publication-title: J Am Heart Assoc – volume: 8 start-page: 15 year: 2016 ident: bib0026 article-title: Common pitfalls in point-of-care ultrasound: A practical guide for emergency and critical care physicians publication-title: Crit Ultrasound J – reference: . Accessed April 2021 – volume: 18 start-page: 459 year: 2012 end-page: 470 ident: bib0003 article-title: Prevalence, morbidity, and mortality of heart failure-related hospitalizations in children in the United States: A population-based study publication-title: J Card Fail – reference: Wang Y, Haghpanah FS, Zhang X, et al. ID-Seg: An accurate and reliable infant deep learning segmentation framework for limbic structures [e-pub ahead of print]. bioRxiv. doi: – volume: 3 start-page: 10 year: 2020 ident: bib0014 article-title: Deep learning interpretation of echocardiograms publication-title: NPJ Digit Med – reference: Kingma DP, Ba J. Adam: A method for stochastic optimization. Available at: – reference: Tran D, Wang H, Torresani L, et al. A closer look at spatiotemporal convolutions for action recognition. Available at: – volume: 24 start-page: 65 year: 2020 ident: bib0024 article-title: International evidence-based guidelines on Point of Care Ultrasound (POCUS) for critically ill neonates and children issued by the POCUS Working Group of the European Society of Paediatric and Neonatal Intensive Care (ESPNIC) publication-title: Crit Care – volume: 4 start-page: 821 year: 2011 end-page: 829 ident: bib0008 article-title: Can a teaching intervention reduce interobserver variability in LVEF assessment: A quality control exercise in the echocardiography lab publication-title: JACC Cardiovasc Imaging – reference: . Accessed April 18 2021 – volume: 23 start-page: 465 year: 2010 end-page: 495 ident: bib0011 article-title: Recommendations for quantification methods during the performance of a pediatric echocardiogram: A report from the Pediatric Measurements Writing Group of the American Society of Echocardiography Pediatric and Congenital Heart Disease Council publication-title: J Am Soc Echocardiogr – volume: 35 start-page: 658 year: 2014 end-page: 667 ident: bib0013 article-title: Variability of M-mode versus two-dimensional echocardiography measurements in children with dilated cardiomyopathy publication-title: Pediatr Cardiol – volume: 45 start-page: 374 year: 2019 end-page: 384 ident: bib0023 article-title: Real-time standard view classification in transthoracic echocardiography using convolutional neural networks publication-title: Ultrasound Med Biol – reference: . Accessed April 18, 2021. – volume: 58 start-page: 303 year: 2017 end-page: 312 ident: bib0005 article-title: Pediatric heart failure: A practical guide to diagnosis and management publication-title: Pediatr Neonatol – volume: 10 start-page: 1391 year: 2021 ident: bib0019 article-title: Artificial intelligence (AI)-empowered echocardiography interpretation: A state-of-the-art review publication-title: J Clin Med – reference: Rajpurkar P, Hannun AY, Haghpanahi M, et al. Cardiologist-level arrhythmia detection with convolutional neural networks. arXiv 2017:1707.01836v1:1-9. – volume: 580 start-page: 252 year: 2020 end-page: 256 ident: bib0015 article-title: Video-based AI for beat-to-beat assessment of cardiac function publication-title: Nature – volume: 35 start-page: 1402 year: 2018 end-page: 1418 ident: bib0022 article-title: Automation, machine learning, and artificial intelligence in echocardiography: A brave new world publication-title: Echocardiography – volume: 23 start-page: 465 year: 2010 ident: 10.1053/j.jvca.2022.05.004_bib0011 article-title: Recommendations for quantification methods during the performance of a pediatric echocardiogram: A report from the Pediatric Measurements Writing Group of the American Society of Echocardiography Pediatric and Congenital Heart Disease Council publication-title: J Am Soc Echocardiogr doi: 10.1016/j.echo.2010.03.019 – volume: 4 start-page: 821 year: 2011 ident: 10.1053/j.jvca.2022.05.004_bib0008 article-title: Can a teaching intervention reduce interobserver variability in LVEF assessment: A quality control exercise in the echocardiography lab publication-title: JACC Cardiovasc Imaging doi: 10.1016/j.jcmg.2011.06.004 – ident: 10.1053/j.jvca.2022.05.004_bib0017 – volume: 35 start-page: 1402 year: 2018 ident: 10.1053/j.jvca.2022.05.004_bib0022 article-title: Automation, machine learning, and artificial intelligence in echocardiography: A brave new world publication-title: Echocardiography doi: 10.1111/echo.14086 – volume: 9 start-page: 19 year: 2017 ident: 10.1053/j.jvca.2022.05.004_bib0025 article-title: POCUS in perioperative medicine: A North American perspective publication-title: Crit Ultrasound J doi: 10.1186/s13089-017-0075-y – volume: 8 year: 2019 ident: 10.1053/j.jvca.2022.05.004_bib0018 article-title: Accuracy of left ventricular cavity volume and ejection fraction for conventional estimation methods and 3D surface fitting publication-title: J Am Heart Assoc doi: 10.1161/JAHA.118.009124 – volume: 18 start-page: 459 year: 2012 ident: 10.1053/j.jvca.2022.05.004_bib0003 article-title: Prevalence, morbidity, and mortality of heart failure-related hospitalizations in children in the United States: A population-based study publication-title: J Card Fail doi: 10.1016/j.cardfail.2012.03.001 – volume: 45 start-page: 374 year: 2019 ident: 10.1053/j.jvca.2022.05.004_bib0023 article-title: Real-time standard view classification in transthoracic echocardiography using convolutional neural networks publication-title: Ultrasound Med Biol doi: 10.1016/j.ultrasmedbio.2018.07.024 – volume: 580 start-page: 252 year: 2020 ident: 10.1053/j.jvca.2022.05.004_bib0015 article-title: Video-based AI for beat-to-beat assessment of cardiac function publication-title: Nature doi: 10.1038/s41586-020-2145-8 – volume: 31 start-page: 388 year: 2008 ident: 10.1053/j.jvca.2022.05.004_bib0001 article-title: Epidemiology of heart failure in a tertiary pediatric center publication-title: Clin Cardiol doi: 10.1002/clc.20262 – volume: 32 start-page: 139 year: 2011 ident: 10.1053/j.jvca.2022.05.004_bib0004 article-title: General anesthesia for children with severe heart failure publication-title: Pediatr Cardiol doi: 10.1007/s00246-010-9832-4 – volume: 16 start-page: 233 year: 2015 ident: 10.1053/j.jvca.2022.05.004_bib0006 article-title: Recommendations for cardiac chamber quantification by echocardiography in adults: An update from the American Society of Echocardiography and the European Association of Cardiovascular Imaging publication-title: Eur Heart J Cardiovasc Imaging doi: 10.1093/ehjci/jev014 – volume: 32 start-page: 1331 year: 2019 ident: 10.1053/j.jvca.2022.05.004_bib0012 article-title: Challenges with left ventricular functional parameters: The Pediatric Heart Network Normal Echocardiogram Database publication-title: J Am Soc Echocardiogr doi: 10.1016/j.echo.2019.05.025 – volume: 38 start-page: 582 year: 2021 ident: 10.1053/j.jvca.2022.05.004_bib0010 article-title: Left ventricular ejection fraction and Global Longitudinal Strain variability between methodology and experience publication-title: Echocardiography doi: 10.1111/echo.15025 – ident: 10.1053/j.jvca.2022.05.004_bib0016 – volume: 3 start-page: 10 year: 2020 ident: 10.1053/j.jvca.2022.05.004_bib0014 article-title: Deep learning interpretation of echocardiograms publication-title: NPJ Digit Med doi: 10.1038/s41746-019-0216-8 – volume: 35 start-page: 658 year: 2014 ident: 10.1053/j.jvca.2022.05.004_bib0013 article-title: Variability of M-mode versus two-dimensional echocardiography measurements in children with dilated cardiomyopathy publication-title: Pediatr Cardiol doi: 10.1007/s00246-013-0835-9 – volume: 28 start-page: 959 year: 2015 ident: 10.1053/j.jvca.2022.05.004_bib0009 article-title: Quality improvement implementation: Improving reproducibility in the echocardiography laboratory publication-title: J Am Soc Echocardiogr doi: 10.1016/j.echo.2015.03.004 – volume: 24 start-page: 65 year: 2020 ident: 10.1053/j.jvca.2022.05.004_bib0024 article-title: International evidence-based guidelines on Point of Care Ultrasound (POCUS) for critically ill neonates and children issued by the POCUS Working Group of the European Society of Paediatric and Neonatal Intensive Care (ESPNIC) publication-title: Crit Care doi: 10.1186/s13054-020-2787-9 – volume: 58 start-page: 303 year: 2017 ident: 10.1053/j.jvca.2022.05.004_bib0005 article-title: Pediatric heart failure: A practical guide to diagnosis and management publication-title: Pediatr Neonatol doi: 10.1016/j.pedneo.2017.01.001 – volume: 37 start-page: 2129 year: 2016 ident: 10.1053/j.jvca.2022.05.004_bib0007 publication-title: Eur Heart J doi: 10.1093/eurheartj/ehw128 – volume: 10 start-page: 1391 year: 2021 ident: 10.1053/j.jvca.2022.05.004_bib0019 article-title: Artificial intelligence (AI)-empowered echocardiography interpretation: A state-of-the-art review publication-title: J Clin Med doi: 10.3390/jcm10071391 – volume: 110 start-page: 1376 year: 2010 ident: 10.1053/j.jvca.2022.05.004_bib0002 article-title: Anesthesia-related cardiac arrest in children with heart disease: Data from the Pediatric Perioperative Cardiac Arrest (POCA) Registry publication-title: Anesth Analg doi: 10.1213/ANE.0b013e3181c9f927 – volume: 8 start-page: 15 year: 2016 ident: 10.1053/j.jvca.2022.05.004_bib0026 article-title: Common pitfalls in point-of-care ultrasound: A practical guide for emergency and critical care physicians publication-title: Crit Ultrasound J doi: 10.1186/s13089-016-0052-x – ident: 10.1053/j.jvca.2022.05.004_bib0020 doi: 10.1101/2021.03.29.437045 – ident: 10.1053/j.jvca.2022.05.004_bib0021 |
| SSID | ssj0011557 |
| Score | 2.3638349 |
| Snippet | Identifying patients with low left ventricular ejection fraction (LVEF) and monitoring LVEF responses to treatment are important clinical goals. Can a... |
| SourceID | proquest pubmed crossref elsevier |
| SourceType | Aggregation Database Index Database Enrichment Source Publisher |
| StartPage | 3610 |
| SubjectTerms | artificial intelligence cardiac function deep learning pediatric patients |
| Title | Retraining an Artificial Intelligence Algorithm to Calculate Left Ventricular Ejection Fraction in Pediatrics |
| URI | https://www.clinicalkey.com/#!/content/1-s2.0-S1053077022003299 https://dx.doi.org/10.1053/j.jvca.2022.05.004 https://www.ncbi.nlm.nih.gov/pubmed/35641411 https://www.proquest.com/docview/2672320283 |
| Volume | 36 |
| WOSCitedRecordID | wos000863663200023&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: PRVESC databaseName: Elsevier SD Freedom Collection Journals 2021 customDbUrl: eissn: 1532-8422 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0011557 issn: 1053-0770 databaseCode: AIEXJ dateStart: 19950201 isFulltext: true titleUrlDefault: https://www.sciencedirect.com providerName: Elsevier |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV3db9MwELdKh9BeEN90wGQk3qpAYidx8lhNnQBpE4INVbxEjmOzlDad0rTaf8e_xjm20_LRwR54iRqrzofv5_NdfHc_hF5JpWgklPIoUQwclJR7XES-R_IgzyPQi1z4LdkEOz1NJpP0Q6_33eXCrGesqpKrq_Tyv4oa2kDYOnX2BuLuLgoN8BuEDkcQOxz_SfAfpaN9GMLcHdVtMJApqbFVfXM0-7qoy-Ziro3PIz7T4aiNzu5RzfCz_uJbmgDV8VQaMvHj2rKKl6A1Hb_HcodtK9oo1-YC8CVsQdgu5JWDcgWrc1l2C8KXlawddk5cDD-0n6tvRokZ6rXO-K8LlzHBV1upbJ9m-nZmy4jPZW0BZz9pgDfsYracFgbN4PnMMIo4NW3qpFg4pls6l8Y2MFa60_iPa4PfEnxMX0_XQhecIqQt2WrIj38uxP3LAtmFLbYb9hEFh0lfI9PXyPwoa-vR7hEWpUkf7Y3ejSfvu40sMNdafh_3RjZvC07f_P4ku2yjXb5PawOd3UN3rYDxyIDuPurJ6gG6c2LDMx6i-QZ7mFd4gz28jT3cYQ83C9xhD2vs4S3sYYc97LCHywpvsPcInR-Pz47eepbPwxOhzxpPCV_kYQgagKc0THgaFpTHNAgkYYViMihUDA4zZ4SqJJe0SNIiZkyQMGWkgD6PUb9aVPIpwknOKVineShkHEYFT0gg_TRXMVGcw7IyQIEbyUzYYvd6AGbZbhkO0LDrc2lKvVz7b-oElLkkZlh2M0Dbtb2irpc1cY3p-td-Lx0GMtD_elMPZutitcxIDMNFtJcwQE8MOLqnp1EcBmEQHNzozZ6h_c2kfI76Tb2SL9BtsW7KZX2IbrFJcmhh_gM_7-EE |
| linkProvider | Elsevier |
| 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=Retraining+an+Artificial+Intelligence+Algorithm+to+Calculate+Left+Ventricular+Ejection+Fraction+in+Pediatrics&rft.jtitle=Journal+of+cardiothoracic+and+vascular+anesthesia&rft.au=Zuercher%2C+Mael&rft.au=Ufkes%2C+Steven&rft.au=Erdman%2C+Lauren&rft.au=Slorach%2C+Cameron&rft.date=2022-09-01&rft.issn=1053-0770&rft.volume=36&rft.issue=9&rft.spage=3610&rft.epage=3616&rft_id=info:doi/10.1053%2Fj.jvca.2022.05.004&rft.externalDBID=n%2Fa&rft.externalDocID=10_1053_j_jvca_2022_05_004 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1053-0770&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1053-0770&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1053-0770&client=summon |