{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T13:53:14Z","timestamp":1787061194877,"version":"build-2736575974"},"reference-count":31,"publisher":"American Association for the Advancement of Science (AAAS)","license":[{"start":{"date-parts":[[2022,10,22]],"date-time":"2022-10-22T00:00:00Z","timestamp":1666396800000},"content-version":"vor","delay-in-days":294,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004608","name":"Natural Science Foundation of Jiangsu Province","doi-asserted-by":"crossref","award":["BK20200238"],"award-info":[{"award-number":["BK20200238"]}],"id":[{"id":"10.13039\/501100004608","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"crossref","award":["GK2240260006"],"award-info":[{"award-number":["GK2240260006"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Beijing Municipal Administration of Hospitals Youth Programme","award":["QML20191208"],"award-info":[{"award-number":["QML20191208"]}]},{"name":"Beijing Municipal Natural Science Foundation","award":["JQ21037"],"award-info":[{"award-number":["JQ21037"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Research"],"published-print":{"date-parts":[[2022,1]]},"abstract":"<jats:p>\n                    Automated echocardiogram interpretation with artificial intelligence (AI) has the potential to facilitate the serial diagnosis of heart defects by primary clinician. However, the fully automated and interpretable analysis pipeline for suggesting a treatment plan is largely underexplored. The present study targets to build an automatic and interpretable assistant for the transthoracic echocardiogram- (TTE-) based assessment of atrial septal defect (ASD) with deep learning (DL). We developed a novel deep keypoint stadiometry (DKS) model, which learns to precisely localize the keypoints, i.e., the endpoints of defects and followed by the absolute distance measurement with the scale. The closure plan and the size of the ASD occluder for transcatheter closure are derived based on the explicit clinical decision rules. A total of 3,474 2D and Doppler TTE from 579 patients were retrospectively collected from two clinical groups. The accuracy of closure classification using DKS (\n                    <jats:inline-formula>\n                      <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" overflow=\"scroll\">\n                        <mml:mn>0.9425<\/mml:mn>\n                        <mml:mo>\u00b1<\/mml:mo>\n                        <mml:mn>0.0052<\/mml:mn>\n                      <\/mml:math>\n                    <\/jats:inline-formula>\n                    ) outperforms the \u201cblack-box\u201d model (\n                    <jats:inline-formula>\n                      <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" overflow=\"scroll\">\n                        <mml:mn>0.7646<\/mml:mn>\n                        <mml:mo>\u00b1<\/mml:mo>\n                        <mml:mn>0.0068<\/mml:mn>\n                      <\/mml:math>\n                    <\/jats:inline-formula>\n                    ;\n                    <jats:inline-formula>\n                      <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" overflow=\"scroll\">\n                        <mml:mi>p<\/mml:mi>\n                        <mml:mo>&lt;<\/mml:mo>\n                        <mml:mn>0.0001<\/mml:mn>\n                      <\/mml:math>\n                    <\/jats:inline-formula>\n                    ) for within-center evaluation. The results in cross-center cases or using the quadratic weighted kappa as an evaluation metric are consistent. The fine-grained keypoint label provides more explicit supervision for network training. While DKS can be fully automated, clinicians can intervene and edit at different steps of the process as well. Our deep learning keypoint localization can provide an automatic and transparent way for assessing size-sensitive congenital heart defects, which has huge potential value for application in primary medical institutions in China. Also, more size-sensitive treatment planning tasks may be explored in the future.\n                  <\/jats:p>","DOI":"10.34133\/2022\/9790653","type":"journal-article","created":{"date-parts":[[2022,10,22]],"date-time":"2022-10-22T19:45:05Z","timestamp":1666467905000},"source":"Crossref","is-referenced-by-count":5,"title":["Assessment of Transcatheter or Surgical Closure of Atrial Septal Defect using Interpretable Deep Keypoint Stadiometry"],"prefix":"10.34133","volume":"2022","author":[{"given":"Jing","family":"Wang","sequence":"first","affiliation":[{"name":"School of Basic Medical Sciences, Capital Medical University, Beijing 10069, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wanqing","family":"Xie","sequence":"additional","affiliation":[{"name":"Department of Intelligent Medical Engineering, School of Biomedical Engineering, Anhui Medical University, Hefei 230032, China"},{"name":"Beth Israel Deaconess Medical Center, Harvard Medical School, Harvard University, Boston, MA 02215, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mingmei","family":"Cheng","sequence":"additional","affiliation":[{"name":"Department of Intelligent Medical Engineering, School of Biomedical Engineering, Anhui Medical University, Hefei 230032, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qun","family":"Wu","sequence":"additional","affiliation":[{"name":"Heart Center, Beijing Children\u2019s Hospital, Capital Medical University, National Center for Children\u2019s Health, Beijing 10045, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fangyun","family":"Wang","sequence":"additional","affiliation":[{"name":"Heart Center, Beijing Children\u2019s Hospital, Capital Medical University, National Center for Children\u2019s Health, Beijing 10045, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pei","family":"Li","sequence":"additional","affiliation":[{"name":"Heart Center, Beijing Children\u2019s Hospital, Capital Medical University, National Center for Children\u2019s Health, Beijing 10045, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bo","family":"Fan","sequence":"additional","affiliation":[{"name":"School of Basic Medical Sciences, Capital Medical University, Beijing 10069, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6533-0164","authenticated-orcid":false,"given":"Xin","family":"Zhang","sequence":"additional","affiliation":[{"name":"Heart Center, Beijing Children\u2019s Hospital, Capital Medical University, National Center for Children\u2019s Health, Beijing 10045, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1597-5930","authenticated-orcid":false,"given":"Binbin","family":"Wang","sequence":"additional","affiliation":[{"name":"Center for Genetics, National Research Institute for Family Planning, Beijing 100730, China"},{"name":"Graduated school, Peking Union Medical College, Beijing 100730, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4514-2016","authenticated-orcid":false,"given":"Xiaofeng","family":"Liu","sequence":"additional","affiliation":[{"name":"Gordon Center for Medical Imaging, Harvard Medical School, and Massachusetts General Hospital, Boston, MA 02114, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"221","published-online":{"date-parts":[[2022,10,22]]},"reference":[{"issue":"3","key":"e_1_3_6_1_2","doi-asserted-by":"crossref","first-page":"425","DOI":"10.1016\/j.ahj.2003.05.003","article-title":"Prevalence of congenital heart disease","volume":"147","author":"Hoffman J. 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