{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T15:08:11Z","timestamp":1777734491841,"version":"3.51.4"},"reference-count":24,"publisher":"Wiley","issue":"11","license":[{"start":{"date-parts":[[2025,8,13]],"date-time":"2025-08-13T00:00:00Z","timestamp":1755043200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2025,8,13]],"date-time":"2025-08-13T00:00:00Z","timestamp":1755043200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Cardiovasc electrophysiol"],"published-print":{"date-parts":[[2025,11]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Catheter ablation for atypical atrial flutter (AFL) is challenging owing to its complex circuit. Consequently, the success rate varies depending on the circuit complexity and the operator's level of experience.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>This study aimed to create software using artificial intelligence to identify the critical isthmus of atypical AFL on 3D mapping.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Raw data from 19 episodes of atypical AFL were extracted from the 3D mapping system. Local data, including location, activation timing, and voltage amplitude, were used to design software to identify areas where wavefronts converge and conduction slowing (which are characteristics of the optimal treatment area), that is, the critical isthmus, in patients with atypical AFL. Subsequently, the newly developed software was validated by evaluating the concordance between the software\u2010estimated critical isthmus location and the actual AFL termination area.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>This newly developed software identified the estimated critical isthmus in all cases in 24.9 (6.7\u201346.2) seconds. The concordance rate between Top 1 prediction area and actual AFL termination area was high of 79% (95% confidence interval: 56.7%\u201391.5%). Although 4 of the 19 episodes did not show concordance, it is assumed that ablation at the estimated critical isthmus may have resulted in arrhythmia termination in two cases.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>This newly developed software identified the optimal treatment area for the atypical AFL with high accuracy. Use of this software may enable faster and more precise treatments. Furthermore, it may allow less experienced operators to achieve results that are comparable to those of experienced operators.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/jce.70058","type":"journal-article","created":{"date-parts":[[2025,8,14]],"date-time":"2025-08-14T05:27:56Z","timestamp":1755149276000},"page":"2852-2860","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Can Artificial Intelligence Identify the Ideal Ablation Area for the Patients With Atypical Atrial Flutter? A Proof of Concept Study Using Developed Software"],"prefix":"10.1111","volume":"36","author":[{"given":"Motoki","family":"Amagasaki","sequence":"first","affiliation":[{"name":"Graduate School of Science and Technology Kumamoto University Kumamoto Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1315-5904","authenticated-orcid":false,"given":"Tadashi","family":"Hoshiyama","sequence":"additional","affiliation":[{"name":"Department of Cardiovascular Medicine, Graduate School of Medical Sciences Kumamoto University Kumamoto Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Takuma","family":"Meitoma","sequence":"additional","affiliation":[{"name":"Graduate School of Science and Technology Kumamoto University Kumamoto Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0094-8179","authenticated-orcid":false,"given":"Masato","family":"Kiyama","sequence":"additional","affiliation":[{"name":"Faculty of Advanced Science and Technology Kumamoto University Kumamoto Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kenji","family":"Morihisa","sequence":"additional","affiliation":[{"name":"Division of Cardiology Kumamoto Chuo Hospital Kumamoto Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuji","family":"Ogura","sequence":"additional","affiliation":[{"name":"Department of Cardiovascular Medicine Fukuoka Tokushukai Hospital Kasuga Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kenichi","family":"Tsujita","sequence":"additional","affiliation":[{"name":"Department of Cardiovascular Medicine, Graduate School of Medical Sciences Kumamoto University Kumamoto Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2025,8,13]]},"reference":[{"key":"e_1_2_10_2_1","doi-asserted-by":"publisher","DOI":"10.1161\/cir.0000000000001193"},{"key":"e_1_2_10_3_1","doi-asserted-by":"publisher","DOI":"10.1002\/ehf2.13526"},{"key":"e_1_2_10_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.hrthm.2015.07.031"},{"key":"e_1_2_10_5_1","doi-asserted-by":"publisher","DOI":"10.1093\/eurheartj\/ehw210"},{"key":"e_1_2_10_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/s0735-1097(00)00635-5"},{"key":"e_1_2_10_7_1","doi-asserted-by":"publisher","DOI":"10.1253\/circj.CJ-18-1131"},{"key":"e_1_2_10_8_1","doi-asserted-by":"publisher","DOI":"10.15420\/aer.2017.5.2"},{"key":"e_1_2_10_9_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.hrthm.2015.09.016"},{"key":"e_1_2_10_10_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00380-018-1307-1"},{"key":"e_1_2_10_11_1","doi-asserted-by":"publisher","DOI":"10.1111\/jce.13046"},{"key":"e_1_2_10_12_1","doi-asserted-by":"publisher","DOI":"10.1111\/pace.14826"},{"key":"e_1_2_10_13_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10840-022-01121-3"},{"key":"e_1_2_10_14_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.repc.2020.06.010"},{"key":"e_1_2_10_15_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1540-8159.2000.tb07051.x"},{"key":"e_1_2_10_16_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00380-023-02335-9"},{"key":"e_1_2_10_17_1","doi-asserted-by":"publisher","DOI":"10.1161\/circep.120.008518"},{"key":"e_1_2_10_18_1","doi-asserted-by":"crossref","unstructured":"J.Hellar R.Cosentino M.John et al. \u201cManifold Approximating Graph Interpolation of Cardiac Local Activation Time \u201d inIEEE Transactions on Bio\u2010Medical EngineeringVol.69 no.10(IEEE 2022) https:\/\/doi.org\/10.1109\/TBME.2022.3166447.","DOI":"10.1109\/TBME.2022.3166447"},{"key":"e_1_2_10_19_1","unstructured":"\u201ccKDTree \u2014 SciPy v1.14.1 Manual \u201d accessed October 10 2024 https:\/\/docs.scipy.org\/doc\/scipy\/reference\/generated\/scipy.spatial.cKDTree.html."},{"key":"e_1_2_10_20_1","unstructured":"M.Ester H.\u2010P.Kriegel J.Sander andX.Xu. \u201cA Density\u2010Based Algorithm for Discovering Clusters in Large Spatial Databases With Noise \u201d inProceedings of the Second International Conference on Knowledge Discovery and Data Mining; 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