{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,13]],"date-time":"2026-10-13T11:32:31Z","timestamp":1791891151420,"version":"4.3.4"},"reference-count":32,"publisher":"Wiley","issue":"8","license":[{"start":{"date-parts":[[2022,7,11]],"date-time":"2022-07-11T00:00:00Z","timestamp":1657497600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["aapm.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["J Applied Clin Med Phys"],"published-print":{"date-parts":[[2022,8]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Purpose<\/jats:title><jats:p>The application of point of care ultrasound (PoCUS) in medical education is a relatively new course. There are still great differences in the existence, quantity, provision, and depth of bedside ultrasound education. The left ventricular outflow tract velocity time integral (LVOT\u2010VTI) has been successfully used in several studies as a parameter for hemodynamic management of critically ill patients, especially in the evaluation of fluid responsiveness. While LVOT\u2010VTI has been broadly used, valuable applications using artificial intelligence (AI) in PoCUS is still limited. We aimed to identify the degree of correlation between auto LVOT\u2010VTI and the manual LVOT\u2010VTI acquired by PoCUS trained ICU doctors.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>Among the 58 ICU doctors who attended PoCUS training from 1 September 2019 to 30 November 2020, 46 ICU doctors who trained for more than 3 months were enrolled. At the end of PoCUS training, each of the enrolled ICU doctors acquired echocardiography parameters of a new ICU patient in 2\u00a0h after new patient was admitted. One of the two bedside expert sonographers would take standard echocardiogram of new ICU patients within 24\u00a0h. For ICU doctors, manual LVOT\u2010VTI was obtained for reference and auto LVOT\u2010VTI was calculated instantly by using an AI software tool. Based on the image quality of the auto LVOT\u2010VTI, ICU patients was separated into ideal group (<jats:italic>n<\/jats:italic>\u00a0=\u00a031) and average group (<jats:italic>n<\/jats:italic>\u00a0=\u00a015).<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Left ventricular end\u2010diastolic dimension (LVEDd, <jats:italic>p<\/jats:italic>\u00a0=\u00a00.1028), left ventricular ejection fraction (LVEF, <jats:italic>p<\/jats:italic>\u00a0=\u00a00.3251), left atrial dimension (LA\u2010d, <jats:italic>p<\/jats:italic>\u00a0=\u00a00.0962), left ventricular E\/A ratio (<jats:italic>p<\/jats:italic>\u00a0=\u00a00.160), left ventricular wall motion (<jats:italic>p<\/jats:italic>\u00a0=\u00a00.317) and pericardial effusion (<jats:italic>p<\/jats:italic>\u00a0=\u00a01) had no significant difference between trained ICU doctors and expert sonographer. ICU patients in average group had greater sequential organ failure assessment (SOFA) score (7.33 \u00b1 1.58\u00a0vs. 4.09 \u00b1 0.57, <jats:italic>p<\/jats:italic>\u00a0=\u00a00.022) and lactic acid (3.67 \u00b1 0.86\u00a0mmol\/L vs. 1.46 \u00b1 0.12\u00a0mmol\/L, <jats:italic>p<\/jats:italic>\u00a0=\u00a00.0009) with greater value of LVEDd (51.93 \u00b1 1.07\u00a0vs. 47.57\u00a0\u00b1 0.89, <jats:italic>p<\/jats:italic>\u00a0=\u00a00.0053), LA\u2010d (39.06 \u00b1 1.47\u00a0vs. 35.22\u00a0\u00b1 0.98, <jats:italic>p<\/jats:italic>\u00a0=\u00a00.0334) and percentage of decreased wall motion (<jats:italic>p<\/jats:italic>\u00a0=\u00a00.0166) than ideal group. There were no significant differences of \u03b4LVOT\u2010VTI (|manual LVOT\u2010VTI \u2013 auto LVOT\u2010VTI|\/manual VTI*100%) between the two groups (8.8% \u00b1 1.3%\u00a0vs. 10% \u00b1 2%, <jats:italic>p<\/jats:italic>\u00a0=\u00a00.6517). Statistically, significant correlations between manual LVOT\u2010VTI and auto LVOT\u2010VTI were present in the ideal group (R<jats:sup>2<\/jats:sup>\u00a0=\u00a00.815, <jats:italic>p<\/jats:italic>\u00a0=\u00a00.00) and average group (R<jats:sup>2<\/jats:sup>\u00a0=\u00a00.741, <jats:italic>p<\/jats:italic>\u00a0=\u00a00.00).<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>ICU doctors could achieve the satisfied level of expertise as expert sonographers after 3 months of PoCUS training. Nearly two thirds of the enrolled ICU doctors could obtain the ideal view and one third of them could acquire the average view. ICU patients with higher SOFA scores and lactic acid were less likely to acquire the ideal view. Manual and auto LVOT\u2010VTI had statistically significant agreement in both ideal and average groups. Auto LVOT\u2010VTI in ideal view was more relevant with the manual LVOT\u2010VTI than the average view. AI might provide real\u2010time guidance among novice operators who lack expertise to acquire the ideal standard view.<\/jats:p><\/jats:sec>","DOI":"10.1002\/acm2.13724","type":"journal-article","created":{"date-parts":[[2022,7,11]],"date-time":"2022-07-11T17:13:55Z","timestamp":1657559635000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":27,"title":["Artificial intelligence (AI) versus expert: A comparison of left ventricular outflow tract velocity time integral (LVOT\u2010VTI) assessment between ICU doctors and an AI tool"],"prefix":"10.1002","volume":"23","author":[{"given":"Shanshan","family":"Zhai","sequence":"first","affiliation":[{"name":"Department of Surgery Intensive Care Unit China\u2013Japan Friendship Hospital  Beijing China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hui","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Surgery Intensive Care Unit China\u2013Japan Friendship Hospital  Beijing China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lichao","family":"Sun","sequence":"additional","affiliation":[{"name":"Department of Emergency Medicine China\u2013Japan Friendship Hospital  Beijing China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bo","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Ultrasound Medicine China\u2013Japan Friendship Hospital  Beijing China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Feng","family":"Huo","sequence":"additional","affiliation":[{"name":"Department of Emergency Medicine, National Center for Children's Health, Beijing Children's Hospital Capital Medical University  Beijing China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shuang","family":"Qiu","sequence":"additional","affiliation":[{"name":"Department of Intensive Care Unit The Sixth Clinical Medical College of Henan University of Traditional Chinese Medicine  Zhumadian Henan Province 463000 China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaoqing","family":"Wu","sequence":"additional","affiliation":[{"name":"Department of Surgery Intensive Care Unit China\u2013Japan Friendship Hospital  Beijing China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Junyu","family":"Ma","sequence":"additional","affiliation":[{"name":"Department of Surgery Intensive Care Unit China\u2013Japan Friendship Hospital  Beijing China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yina","family":"Wu","sequence":"additional","affiliation":[{"name":"Department of Surgery Intensive Care Unit China\u2013Japan Friendship Hospital  Beijing China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jun","family":"Duan","sequence":"additional","affiliation":[{"name":"Department of Surgery Intensive Care Unit China\u2013Japan Friendship Hospital  Beijing China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2022,7,11]]},"reference":[{"key":"e_1_2_9_2_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1365-2044.2012.07300.x"},{"key":"e_1_2_9_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jcrc.2014.04.008"},{"key":"e_1_2_9_4_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00134-004-2259-8"},{"key":"e_1_2_9_5_1","doi-asserted-by":"publisher","DOI":"10.1097\/01.ccx.0000224870.24324.cc"},{"key":"e_1_2_9_6_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00134-004-2233-5"},{"key":"e_1_2_9_7_1","doi-asserted-by":"publisher","DOI":"10.1111\/pan.13793"},{"key":"e_1_2_9_8_1","doi-asserted-by":"publisher","DOI":"10.1097\/EJA.0000000000000679"},{"key":"e_1_2_9_9_1","doi-asserted-by":"publisher","DOI":"10.1097\/MCC.0000000000000501"},{"key":"e_1_2_9_10_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00134-019-05618-w"},{"key":"e_1_2_9_11_1","doi-asserted-by":"publisher","DOI":"10.1002\/jcu.22559"},{"key":"e_1_2_9_12_1","doi-asserted-by":"publisher","DOI":"10.1053\/j.jvca.2020.08.016"},{"key":"e_1_2_9_13_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00134-015-3952-5"},{"key":"e_1_2_9_14_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00261-018-1517-0"},{"key":"e_1_2_9_15_1","doi-asserted-by":"publisher","DOI":"10.1002\/jum.15403"},{"key":"e_1_2_9_16_1","doi-asserted-by":"crossref","unstructured":"ShaikhF AwanO FriedmanO HeT QadirN BarjaktarevicI.Measuring the accuracy of cardiac output using POCUS: the introduction of artificial intelligence into routine care[C]\/American Thoracic Society 2020 International Conference May 15\u201020 2020 \u2010 Philadelphia PA.2020.","DOI":"10.1164\/ajrccm-conference.2020.201.1_MeetingAbstracts.A1775"},{"key":"e_1_2_9_17_1","unstructured":"ShahA.ASDOC: Stata module to create high\u2010quality tables in MS Word from Stata output[J]. 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