Validation of ablation site classification accuracy and trends in the prediction of potential reconnection sites for atrial fibrillation using the CARTONET® R12.1 model

Background CARTONET® enables automatic ablation site classification and reconnection site prediction using machine learning. However, the accuracy of the site classification model and trends of the site prediction model for potential reconnection sites are uncertain. Methods We studied a total of 39...

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Published in:Journal of arrhythmia 2024-10, Vol.40 (5), p.1085-1092
Main Authors: Sasaki, Wataru, Tanaka, Naomichi, Matsumoto, Kazuhisa, Kawano, Daisuke, Narita, Masataka, Naganuma, Tsukasa, Tsutsui, Kenta, Mori, Hitoshi, Ikeda, Yoshifumi, Arai, Takahide, Matsumoto, Kazuo, Kato, Ritsushi
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Subjects:
Ablation
Annotations
Automatic classification
Cardiac arrhythmia
CARTONET
Catheters
Esophagus
external validation
Machine learning
Original
pulmonary vein isolation
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container_issue 5
container_start_page 1085
container_title Journal of arrhythmia
container_volume 40
creator Sasaki, Wataru
Tanaka, Naomichi
Matsumoto, Kazuhisa
Kawano, Daisuke
Narita, Masataka
Naganuma, Tsukasa
Tsutsui, Kenta
Mori, Hitoshi
Ikeda, Yoshifumi
Arai, Takahide
Matsumoto, Kazuo
Kato, Ritsushi
description Background CARTONET® enables automatic ablation site classification and reconnection site prediction using machine learning. However, the accuracy of the site classification model and trends of the site prediction model for potential reconnection sites are uncertain. Methods We studied a total of 396 cases. About 313 patients underwent pulmonary vein isolation (PVI), including a cavotricuspid isthmus (CTI) ablation (PVI group) and 83 underwent PVI and additional ablation (i.e., box isolation) (PVI+ group). We investigated the sensitivity and positive predictive value (PPV) for automatic site classification in the total cohort and compared these metrics for PV lesions versus non‐PV lesions. The distribution of potential reconnection sites and confidence level for each site was also investigated. Results A total of 29,422 points were analyzed (PV lesions [n = 22 418], non‐PV lesions [n = 7004]). The sensitivity and PPV of the total cohort were 71.4% and 84.6%, respectively. The sensitivity and PPV of PV lesions were significantly higher than those of non‐PV lesions (PV lesions vs. non‐PV lesions, %; sensitivity, 75.3 vs. 67.5, p 
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However, the accuracy of the site classification model and trends of the site prediction model for potential reconnection sites are uncertain. Methods We studied a total of 396 cases. About 313 patients underwent pulmonary vein isolation (PVI), including a cavotricuspid isthmus (CTI) ablation (PVI group) and 83 underwent PVI and additional ablation (i.e., box isolation) (PVI+ group). We investigated the sensitivity and positive predictive value (PPV) for automatic site classification in the total cohort and compared these metrics for PV lesions versus non‐PV lesions. The distribution of potential reconnection sites and confidence level for each site was also investigated. Results A total of 29,422 points were analyzed (PV lesions [n = 22 418], non‐PV lesions [n = 7004]). The sensitivity and PPV of the total cohort were 71.4% and 84.6%, respectively. The sensitivity and PPV of PV lesions were significantly higher than those of non‐PV lesions (PV lesions vs. non‐PV lesions, %; sensitivity, 75.3 vs. 67.5, p &lt; .05; PPV, 91.2 vs. 67.9, p &lt; .05). CTI and superior vena cava could not be recognized or analyzed. In the potential reconnection prediction model, the incidence of potential reconnections was highest in the posterior, while the confidence was the highest in the roof. Conclusion The automatic site classification of the CARTONET®R12.1 model demonstrates relatively high accuracy in pulmonary veins excluding the carina. The prediction of potential reconnection sites feature tends to anticipate areas with poor catheter stability as reconnection sites. The automatic site classification of the CARTONET®R12.1 model demonstrates relatively high accuracy in pulmonary veins excluding the carina. The prediction of potential reconnection sites feature tends to anticipate areas with poor catheter stability as reconnection sites.</description><identifier>ISSN: 1880-4276</identifier><identifier>EISSN: 1883-2148</identifier><identifier>DOI: 10.1002/joa3.13131</identifier><identifier>PMID: 39416247</identifier><language>eng</language><publisher>Japan: John Wiley &amp; Sons, Inc</publisher><subject>Ablation ; Annotations ; Automatic classification ; Cardiac arrhythmia ; CARTONET ; Catheters ; Esophagus ; external validation ; Machine learning ; Original ; pulmonary vein isolation</subject><ispartof>Journal of arrhythmia, 2024-10, Vol.40 (5), p.1085-1092</ispartof><rights>2024 The Author(s). published by John Wiley &amp; Sons Australia, Ltd on behalf of Japanese Heart Rhythm Society.</rights><rights>2024 The Author(s). Journal of Arrhythmia published by John Wiley &amp; Sons Australia, Ltd on behalf of Japanese Heart Rhythm Society.</rights><rights>2024. This work is published under http://creativecommons.org/licenses/by/4.0/ (the "License"). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><orcidid>0000-0002-3347-6772 ; 0000-0002-9989-8708 ; 0000-0002-5121-6974 ; 0000-0003-1418-6566 ; 0000-0002-5648-8135 ; 0000-0002-1172-2995</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.proquest.com/docview/3116543125/fulltextPDF?pq-origsite=primo$$EPDF$$P50$$Gproquest$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.proquest.com/docview/3116543125?pq-origsite=primo$$EHTML$$P50$$Gproquest$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,885,11562,25753,27924,27925,37012,37013,44590,46052,46476,53791,53793,75126</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/39416247$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Sasaki, Wataru</creatorcontrib><creatorcontrib>Tanaka, Naomichi</creatorcontrib><creatorcontrib>Matsumoto, Kazuhisa</creatorcontrib><creatorcontrib>Kawano, Daisuke</creatorcontrib><creatorcontrib>Narita, Masataka</creatorcontrib><creatorcontrib>Naganuma, Tsukasa</creatorcontrib><creatorcontrib>Tsutsui, Kenta</creatorcontrib><creatorcontrib>Mori, Hitoshi</creatorcontrib><creatorcontrib>Ikeda, Yoshifumi</creatorcontrib><creatorcontrib>Arai, Takahide</creatorcontrib><creatorcontrib>Matsumoto, Kazuo</creatorcontrib><creatorcontrib>Kato, Ritsushi</creatorcontrib><title>Validation of ablation site classification accuracy and trends in the prediction of potential reconnection sites for atrial fibrillation using the CARTONET® R12.1 model</title><title>Journal of arrhythmia</title><addtitle>J Arrhythm</addtitle><description>Background CARTONET® enables automatic ablation site classification and reconnection site prediction using machine learning. However, the accuracy of the site classification model and trends of the site prediction model for potential reconnection sites are uncertain. Methods We studied a total of 396 cases. About 313 patients underwent pulmonary vein isolation (PVI), including a cavotricuspid isthmus (CTI) ablation (PVI group) and 83 underwent PVI and additional ablation (i.e., box isolation) (PVI+ group). We investigated the sensitivity and positive predictive value (PPV) for automatic site classification in the total cohort and compared these metrics for PV lesions versus non‐PV lesions. The distribution of potential reconnection sites and confidence level for each site was also investigated. Results A total of 29,422 points were analyzed (PV lesions [n = 22 418], non‐PV lesions [n = 7004]). The sensitivity and PPV of the total cohort were 71.4% and 84.6%, respectively. The sensitivity and PPV of PV lesions were significantly higher than those of non‐PV lesions (PV lesions vs. non‐PV lesions, %; sensitivity, 75.3 vs. 67.5, p &lt; .05; PPV, 91.2 vs. 67.9, p &lt; .05). CTI and superior vena cava could not be recognized or analyzed. In the potential reconnection prediction model, the incidence of potential reconnections was highest in the posterior, while the confidence was the highest in the roof. Conclusion The automatic site classification of the CARTONET®R12.1 model demonstrates relatively high accuracy in pulmonary veins excluding the carina. The prediction of potential reconnection sites feature tends to anticipate areas with poor catheter stability as reconnection sites. The automatic site classification of the CARTONET®R12.1 model demonstrates relatively high accuracy in pulmonary veins excluding the carina. 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However, the accuracy of the site classification model and trends of the site prediction model for potential reconnection sites are uncertain. Methods We studied a total of 396 cases. About 313 patients underwent pulmonary vein isolation (PVI), including a cavotricuspid isthmus (CTI) ablation (PVI group) and 83 underwent PVI and additional ablation (i.e., box isolation) (PVI+ group). We investigated the sensitivity and positive predictive value (PPV) for automatic site classification in the total cohort and compared these metrics for PV lesions versus non‐PV lesions. The distribution of potential reconnection sites and confidence level for each site was also investigated. Results A total of 29,422 points were analyzed (PV lesions [n = 22 418], non‐PV lesions [n = 7004]). The sensitivity and PPV of the total cohort were 71.4% and 84.6%, respectively. The sensitivity and PPV of PV lesions were significantly higher than those of non‐PV lesions (PV lesions vs. non‐PV lesions, %; sensitivity, 75.3 vs. 67.5, p &lt; .05; PPV, 91.2 vs. 67.9, p &lt; .05). CTI and superior vena cava could not be recognized or analyzed. In the potential reconnection prediction model, the incidence of potential reconnections was highest in the posterior, while the confidence was the highest in the roof. Conclusion The automatic site classification of the CARTONET®R12.1 model demonstrates relatively high accuracy in pulmonary veins excluding the carina. The prediction of potential reconnection sites feature tends to anticipate areas with poor catheter stability as reconnection sites. The automatic site classification of the CARTONET®R12.1 model demonstrates relatively high accuracy in pulmonary veins excluding the carina. The prediction of potential reconnection sites feature tends to anticipate areas with poor catheter stability as reconnection sites.</abstract><cop>Japan</cop><pub>John Wiley &amp; Sons, Inc</pub><pmid>39416247</pmid><doi>10.1002/joa3.13131</doi><tpages>8</tpages><orcidid>https://orcid.org/0000-0002-3347-6772</orcidid><orcidid>https://orcid.org/0000-0002-9989-8708</orcidid><orcidid>https://orcid.org/0000-0002-5121-6974</orcidid><orcidid>https://orcid.org/0000-0003-1418-6566</orcidid><orcidid>https://orcid.org/0000-0002-5648-8135</orcidid><orcidid>https://orcid.org/0000-0002-1172-2995</orcidid><oa>free_for_read</oa></addata></record>
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subjects Ablation
Annotations
Automatic classification
Cardiac arrhythmia
CARTONET
Catheters
Esophagus
external validation
Machine learning
Original
pulmonary vein isolation
title Validation of ablation site classification accuracy and trends in the prediction of potential reconnection sites for atrial fibrillation using the CARTONET® R12.1 model
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