{"id":2824,"date":"2023-11-30T01:38:00","date_gmt":"2023-11-29T14:38:00","guid":{"rendered":"https:\/\/harrison.ai\/?p=2824"},"modified":"2025-10-04T20:06:57","modified_gmt":"2025-10-04T10:06:57","slug":"estimating-the-impact-of-chest-radiograph-triage-using-ai-a-real-life-multicenter-diagnostic-cohort-study","status":"publish","type":"post","link":"https:\/\/harrison.ai\/estimating-the-impact-of-chest-radiograph-triage-using-ai-a-real-life-multicenter-diagnostic-cohort-study\/","title":{"rendered":"Estimating the impact of chest radiograph triage using AI \u2013 a real-life multicenter diagnostic cohort study."},"content":{"rendered":"    <section id=\"evidence-block-block_36ce5991759807c3ba4885ca749bd7ef\" class=\"study-block   text-\" >\n        <div class=\"container  container--tab\">\n            <div class=\"container container--tab\">\n                <div class=\"connect__decor hide-md\">\n                    <span class=\"pixel-decor\" style=\"background-color: rgba(9, 114, 241, 0.75)\"><\/span>\n                    <span class=\"pixel-decor\" style=\"background-color: #0972f1\"><\/span>\n                    <span class=\"pixel-decor\" style=\"background-color: rgba(9, 114, 241, 0.5)\"><\/span>\n                    <span class=\"pixel-decor hide-sm\" style=\"background-color: rgba(9, 114, 241, 0.5)\"><\/span>\n                <\/div>\n                <div class=\"study__share hide-sm\" data-aos=\"fade-up\">\n                                    <\/div>\n                <div class=\"study__row\">\n                    <div class=\"study__left\" data-aos=\"fade-up\"><\/div>\n                    <div class=\"study__right\" data-aos=\"fade-up\">\n                        <div class=\"text b1\">\n                        <p><strong>Author <\/strong><\/p>\n<p>Plesner, Louis Lind | RegionH Denmark<\/p>\n<p>Scientific poster presentation (W2-SPIN-3) at RSNA 2023, 26 \u2013 30. November 2023 in Chicago, US<\/p>\n                        <\/div>\n                                            <\/div>\n                <\/div>\n                                                            <div class=\"study__row\">\n                            <div class=\"study__left\" data-aos=\"fade-up\">\n                                <h2 class=\"h5\">Purpose<\/h2>\n                            <\/div>\n                            <div class=\"study__right\" data-aos=\"fade-up\">\n                                <div class=\"text b1\"><p><span class=\"TextRun Highlight SCXW41445805 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"none\"><span class=\"NormalTextRun SCXW41445805 BCX0\">Can an AI tool effectively triage CXR cases into remarkable and unremarkable categories in clinical practice?<\/span><\/span><\/p>\n<\/div>                            <\/div>\n                        <\/div>\n                                            <div class=\"study__row\">\n                            <div class=\"study__left\" data-aos=\"fade-up\">\n                                <h2 class=\"h5\">Method<\/h2>\n                            <\/div>\n                            <div class=\"study__right\" data-aos=\"fade-up\">\n                                <div class=\"text b1\"><p><span class=\"TextRun Highlight SCXW117106848 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"none\"><span class=\"NormalTextRun SCXW117106848 BCX0\">Retrospective validation of an AI model (Annalise Enterprise CXR), post-processed to provide unremarkable and remarkable distinction on 1.990 consecutive CXR studies with dual thoracic radiologist\u2019s reference. The AI model was compared to binary classification extracted from RIS regarding various performance measures.<\/span><\/span><\/p>\n<\/div>                            <\/div>\n                        <\/div>\n                                            <div class=\"study__row\">\n                            <div class=\"study__left\" data-aos=\"fade-up\">\n                                <h2 class=\"h5\">Results<\/h2>\n                            <\/div>\n                            <div class=\"study__right\" data-aos=\"fade-up\">\n                                <div class=\"text b1\"><p><span class=\"TextRun Highlight SCXW244850336 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"none\"><span class=\"NormalTextRun SCXW244850336 BCX0\">The AI model <\/span><span class=\"NormalTextRun SCXW244850336 BCX0\">demonstrated<\/span><span class=\"NormalTextRun SCXW244850336 BCX0\"> an AUC of 0.926 and was statistically superior to routine classification across all evaluated measures.<\/span><\/span><span class=\"EOP SCXW244850336 BCX0\" data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<\/div>                            <\/div>\n                        <\/div>\n                                            <div class=\"study__row\">\n                            <div class=\"study__left\" data-aos=\"fade-up\">\n                                <h2 class=\"h5\">Key Takeaway<\/h2>\n                            <\/div>\n                            <div class=\"study__right\" data-aos=\"fade-up\">\n                                <div class=\"text b1\"><p><span class=\"TextRun Highlight SCXW184445990 BCX0\" lang=\"EN-US\" xml:lang=\"EN-US\" data-contrast=\"none\"><span class=\"NormalTextRun SCXW184445990 BCX0\">The AI model <\/span><span class=\"NormalTextRun SCXW184445990 BCX0\">achieved excellent discrimination between unremarkable and remarkable CXRs<\/span><span class=\"NormalTextRun SCXW184445990 BCX0\"> and was superior to clinically assigned priority levels.<\/span><\/span><\/p>\n<\/div>                            <\/div>\n                        <\/div>\n                                            <div class=\"study__row\">\n                            <div class=\"study__left\" data-aos=\"fade-up\">\n                                <h2 class=\"h5\">Disclaimer<\/h2>\n                            <\/div>\n                            <div class=\"study__right\" data-aos=\"fade-up\">\n                                <div class=\"text b1\"><p>Harrison.ai Radiology Solutions were previously marketed as Annalise.ai solutions.<\/p>\n<\/div>                            <\/div>\n                        <\/div>\n                                                                <\/div>\n        <\/div>\n    <\/section>\n\n","protected":false},"excerpt":{"rendered":"","protected":false},"author":2,"featured_media":2733,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[29],"tags":[],"region":[35],"product":[40],"class_list":["post-2824","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-evidence","region-emea","product-cxr"],"acf":[],"_links":{"self":[{"href":"https:\/\/harrison.ai\/wp-json\/wp\/v2\/posts\/2824","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/harrison.ai\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/harrison.ai\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/harrison.ai\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/harrison.ai\/wp-json\/wp\/v2\/comments?post=2824"}],"version-history":[{"count":4,"href":"https:\/\/harrison.ai\/wp-json\/wp\/v2\/posts\/2824\/revisions"}],"predecessor-version":[{"id":4906,"href":"https:\/\/harrison.ai\/wp-json\/wp\/v2\/posts\/2824\/revisions\/4906"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/harrison.ai\/wp-json\/wp\/v2\/media\/2733"}],"wp:attachment":[{"href":"https:\/\/harrison.ai\/wp-json\/wp\/v2\/media?parent=2824"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/harrison.ai\/wp-json\/wp\/v2\/categories?post=2824"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/harrison.ai\/wp-json\/wp\/v2\/tags?post=2824"},{"taxonomy":"region","embeddable":true,"href":"https:\/\/harrison.ai\/wp-json\/wp\/v2\/region?post=2824"},{"taxonomy":"product","embeddable":true,"href":"https:\/\/harrison.ai\/wp-json\/wp\/v2\/product?post=2824"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}