{"id":8701,"date":"2025-08-19T11:25:07","date_gmt":"2025-08-19T09:25:07","guid":{"rendered":"https:\/\/acmit.at\/?p=8701"},"modified":"2025-08-19T12:15:03","modified_gmt":"2025-08-19T10:15:03","slug":"acmit-and-its-partners-publish-new-paper-in-the-computer-methods-and-programs-in-biomedicine","status":"publish","type":"post","link":"https:\/\/acmit.at\/de\/acmit-and-its-partners-publish-new-paper-in-the-computer-methods-and-programs-in-biomedicine\/","title":{"rendered":"ACMIT and its partners publish new paper in the Computer Methods and Programs in Biomedicine"},"content":{"rendered":"\n

ACMIT<\/strong> and its partners<\/strong> have published a new article titled \u201cAn integrated optimization and deep learning pipeline for predicting live birth success in IVF using feature optimization and transformer-based models\u201d<\/strong>.<\/p>\n\n\n\n

In vitro fertilization (IVF) has transformed fertility treatment by offering millions of individuals and couples the chance to achieve parenthood. However, predicting the likelihood of a successful outcome\u2014especially live birth\u2014remains a significant clinical challenge due to the complicated relationship among demographic, clinical, and procedural factors. This work presents an innovative AI-based pipeline that is designed to predict live birth success in IVF. The study introduces a hybrid framework that combines Particle Swarm Optimization (PSO) for feature selection with a Tab transformer-based deep learning model. This approach achieved exceptional predictive performance (AUC: 98.4%, Accuracy: 97%) on the large-scale HFEA dataset. In addition to its high accuracy, the model emphasizes clinical transparency through SHAP-based interpretability and demonstrates robustness across various subgroups and preprocessing scenarios. This work marks a promising step toward integrating explainable AI tools into reproductive medicine to support personalized fertility care.<\/p>\n\n\n\n

For more information, please visit:<\/p>\n\n\n\n

Paper pdf<\/a><\/strong><\/p>\n\n\n\n

Paper online<\/a><\/strong><\/p>\n\n\n\n

<\/p>\n","protected":false},"excerpt":{"rendered":"

ACMIT and its partners have published a new article titled \u201cAn integrated optimization and deep learning pipeline for predicting live birth success in IVF using feature optimization and transformer-based models\u201d. In vitro fertilization (IVF) has transformed fertility treatment by offering […]<\/p>\n","protected":false},"author":3,"featured_media":7247,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"_uag_custom_page_level_css":"","site-sidebar-layout":"default","site-content-layout":"","ast-site-content-layout":"default","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"","footer-sml-layout":"","theme-transparent-header-meta":"","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"set","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"ast-content-background-meta":{"desktop":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"footnotes":""},"categories":[1],"tags":[78,31,148,32,324],"class_list":["post-8701","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-allgemein","tag-medicaldevice","tag-medicaltechnology","tag-publication","tag-researchanddevelopment","tag-science"],"acf":[],"uagb_featured_image_src":{"full":["https:\/\/acmit.at\/wp-content\/uploads\/2023\/02\/Journal-Pic.jpg",1920,1073,false],"thumbnail":["https:\/\/acmit.at\/wp-content\/uploads\/2023\/02\/Journal-Pic-600x600.jpg",600,600,true],"medium":["https:\/\/acmit.at\/wp-content\/uploads\/2023\/02\/Journal-Pic-700x391.jpg",700,391,true],"medium_large":["https:\/\/acmit.at\/wp-content\/uploads\/2023\/02\/Journal-Pic-768x429.jpg",768,429,true],"large":["https:\/\/acmit.at\/wp-content\/uploads\/2023\/02\/Journal-Pic-1200x671.jpg",1200,671,true],"1536x1536":["https:\/\/acmit.at\/wp-content\/uploads\/2023\/02\/Journal-Pic-1536x858.jpg",1536,858,true],"2048x2048":["https:\/\/acmit.at\/wp-content\/uploads\/2023\/02\/Journal-Pic.jpg",1920,1073,false],"woocommerce_thumbnail":["https:\/\/acmit.at\/wp-content\/uploads\/2023\/02\/Journal-Pic-500x279.jpg",500,279,true],"woocommerce_single":["https:\/\/acmit.at\/wp-content\/uploads\/2023\/02\/Journal-Pic-1000x559.jpg",1000,559,true],"woocommerce_gallery_thumbnail":["https:\/\/acmit.at\/wp-content\/uploads\/2023\/02\/Journal-Pic-100x100.jpg",100,100,true]},"uagb_author_info":{"display_name":"Noemi Kozma","author_link":"https:\/\/acmit.at\/de\/author\/kon\/"},"uagb_comment_info":0,"uagb_excerpt":"ACMIT and its partners have published a new article titled \u201cAn integrated optimization and deep learning pipeline for predicting live birth success in IVF using feature optimization and transformer-based models\u201d. 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