{"id":3287,"date":"2024-08-28T06:17:44","date_gmt":"2024-08-28T06:17:44","guid":{"rendered":"https:\/\/www.radisentech.com\/?post_type=publication&#038;p=3287"},"modified":"2025-03-27T09:29:17","modified_gmt":"2025-03-27T09:29:17","slug":"ai-software-for-measuring-cardiothoracic-ratio-on-chest-x-ray-radiographs-and-predicting-left-ventricular-structural-abnormalities","status":"publish","type":"publication","link":"https:\/\/www.radisentech.com\/zh-hant\/publication\/ai-software-for-measuring-cardiothoracic-ratio-on-chest-x-ray-radiographs-and-predicting-left-ventricular-structural-abnormalities\/","title":{"rendered":"AI Software for Measuring Cardiothoracic Ratio on Chest X-ray Radiographs and Predicting Left Ventricular Structural Abnormalities"},"content":{"rendered":"\n<div class=\"wp-block-columns is-layout-flex wp-container-core-columns-is-layout-1 wp-block-columns-is-layout-flex\">\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\" style=\"flex-basis:33.33%\">\n<h2 class=\"wp-block-heading\" id=\"h-published\">Published<\/h2>\n\n\n\n<p>The 80<sup>th<\/sup>&nbsp;Korean Congress of Radiology (KCR 2024)<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-authors\">Authors<\/h2>\n\n\n\n<p>Heejun Shin<sup>1<\/sup>, Taehee Kim<sup>1<\/sup>, Dongmyung Shin<sup>1<\/sup>, Joshua Ra<sup>2<\/sup><\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-affiliations\">Affiliations<\/h2>\n\n\n\n<p><em><sup>1<\/sup>Radisen, Korea, Republic of<br><sup>2<\/sup>UCSF Medical Center, USA<br>shinsae11@radisentech.com<\/em><\/p>\n<\/div>\n\n\n\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\" style=\"flex-basis:66.66%\">\n<h2 class=\"wp-block-heading\" id=\"h-purpose\">Purpose<\/h2>\n\n\n\n<p>This study aimed to evaluate a commercially available AI model that measures the cardiothoracic ratio (CTR) on chest x-ray radiographs (CXRs) and examines its association with echocardiographic diagnoses of severe left ventricular hypertrophy (SLVH) and dilated left ventricle (DLV).<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-materials-and-method\">Materials and Method<\/h2>\n\n\n\n<p>The study utilized 71,589 CXRs from 24,689 patients and employed the CheXchoNet dataset. Patients underwent echocardiograms within one year of their X-ray scans, which diagnosed them with either SLVH or DLV. A binary label was created based on the prese nce of either condition (9,861\/61,728 composite positive\/negative). Commercially available AI software (AXIR-CX; Radisen Co. Ltd.) measured CTRs on individual CXRs. Statistical analyses included examining CTR value distributions according to the composite labels using a t-test and calculating sensitivity, specificity, area under the curve (AUC), and the Youden index with ground truth to the composite labels. We also developed a separate classification AI (a multilayer perceptron with two fully connected layers) that took the CTR value, age, and sex of a patient as inputs to predict the binary composite label. 80% of CXRs were used for training and the remaining for evaluation.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-results\">Results<\/h2>\n\n\n\n<p>The average CTR value was significantly higher in composite SLVH\/DLV positive patients compared to negative patients (mean \u00b1 std: 0.56 \u00b1 0.07 for positive; 0.51 \u00b1 0.07 for negatives; p-value &lt; 0.001). The binary classification of the composite label showed an AU C of 0.69. The Youden index of 0.30 was maximized when CTR &gt; 0.53, reporting a sensitivity of 0.70 and specificity of 0.60. When sex, age, and CTR were used as inputs in the classification AI model, an AUC was 0.71, and the Youden index was 0.32, with a sensitivity of 0.74 and specificity of 0.58. The exclusion of CTR values as inputs significantly dropped the AUC to 0.54, highlighting the importance of CTR measurements for accurately predicting composite SLVH\/DLV labels.<\/p>\n<\/div>\n<\/div>\n\n\n\n<p><\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"_acf_changed":false},"categories":[],"class_list":["post-3287","publication","type-publication","status-publish","hentry"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v22.3 (Yoast SEO v22.3) - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>AI Software for Measuring Cardiothoracic Ratio on Chest X-ray Radiographs and Predicting Left Ventricular Structural Abnormalities - Radisen<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.radisentech.com\/zh-hant\/publication\/ai-software-for-measuring-cardiothoracic-ratio-on-chest-x-ray-radiographs-and-predicting-left-ventricular-structural-abnormalities\/\" \/>\n<meta property=\"og:locale\" content=\"zh_TW\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"AI Software for Measuring Cardiothoracic Ratio on Chest X-ray Radiographs and Predicting Left Ventricular Structural Abnormalities\" \/>\n<meta property=\"og:description\" content=\"Published The 80th&nbsp;Korean Congress of Radiology (K [&hellip;]\" \/>\n<meta property=\"og:url\" content=\"https:\/\/www.radisentech.com\/zh-hant\/publication\/ai-software-for-measuring-cardiothoracic-ratio-on-chest-x-ray-radiographs-and-predicting-left-ventricular-structural-abnormalities\/\" \/>\n<meta property=\"og:site_name\" content=\"Radisen\" \/>\n<meta property=\"article:modified_time\" content=\"2025-03-27T09:29:17+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/www.radisentech.com\/wp-content\/uploads\/2024\/06\/Site-image.png\" \/>\n\t<meta property=\"og:image:width\" content=\"1200\" \/>\n\t<meta property=\"og:image:height\" content=\"675\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/png\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"\u9810\u4f30\u95b1\u8b80\u6642\u9593\" \/>\n\t<meta name=\"twitter:data1\" content=\"2 \u5206\u9418\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\/\/www.radisentech.com\/zh-hant\/publication\/ai-software-for-measuring-cardiothoracic-ratio-on-chest-x-ray-radiographs-and-predicting-left-ventricular-structural-abnormalities\/\",\"url\":\"https:\/\/www.radisentech.com\/zh-hant\/publication\/ai-software-for-measuring-cardiothoracic-ratio-on-chest-x-ray-radiographs-and-predicting-left-ventricular-structural-abnormalities\/\",\"name\":\"AI Software for Measuring Cardiothoracic Ratio on Chest X-ray Radiographs and Predicting Left Ventricular Structural Abnormalities - 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