{"id":2853,"date":"2025-05-13T02:09:00","date_gmt":"2025-05-12T16:09:00","guid":{"rendered":"https:\/\/harrison.ai\/?p=2853"},"modified":"2025-10-04T20:02:48","modified_gmt":"2025-10-04T10:02:48","slug":"the-potential-clinical-utility-of-an-artificial-intelligence-model-for-identification-of-vertebral-compression-fractures-in-chest-radiographs","status":"publish","type":"post","link":"https:\/\/harrison.ai\/the-potential-clinical-utility-of-an-artificial-intelligence-model-for-identification-of-vertebral-compression-fractures-in-chest-radiographs\/","title":{"rendered":"The Potential Clinical Utility of an Artificial Intelligence Model for Identification of Vertebral Compression Fractures in Chest Radiographs"},"content":{"rendered":"    <section id=\"evidence-block-block_8332f439013e786ad84e62f325db173d\" 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                        <h5>Authors<\/h5>\n<p><span role=\"listitem\"><span class=\"dropBlock\">Ankita Ghatak,<\/span><\/span> <span role=\"listitem\"><span class=\"dropBlock\">James M. Hillis,<\/span><\/span>\u00a0<span role=\"listitem\"><span class=\"dropBlock\">Sarah F. Mercaldo, <\/span><\/span><span data-hidden-on=\"none\"><span role=\"listitem\"><span class=\"dropBlock\">Isabella Newbury-Chaet, <\/span><\/span><\/span><span data-hidden-on=\"none\"><span role=\"listitem\"><span class=\"dropBlock\">John K. Chin, <\/span><\/span><\/span><span data-hidden-on=\"none\"><span role=\"listitem\"><span class=\"dropBlock\">Subba R. Digumarthy, <\/span><\/span><\/span><span data-hidden-on=\"none\"><span role=\"listitem\"><span class=\"dropBlock\">Karen Rodriguez,\u00a0<\/span><\/span><\/span><span data-hidden-on=\"none\"><span role=\"listitem\"><span class=\"dropBlock\">Victorine V. Muse,\u00a0<\/span><\/span><\/span><span data-hidden-on=\"none\"><span role=\"listitem\"><span class=\"dropBlock\">Katherine P. Andriole,\u00a0<\/span><\/span><\/span><span role=\"listitem\"><span class=\"dropBlock\">Keith J. Dreyer,\u00a0<\/span><\/span><span role=\"listitem\"><span class=\"dropBlock\">Mannudeep K. Kalra,\u00a0<\/span><\/span><span class=\"corresponding-author\" role=\"listitem\"><span class=\"dropBlock\">Bernardo C. Bizzo.<\/span><\/span><\/p>\n<p><strong>Journal of the American College of Radiology<br \/>\n<\/strong>Published online September 17, 2024<\/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>To assess the ability of the Annalise Enterprise CXR Triage Trauma (Annalise AI Pty Ltd, Sydney, NSW, Australia) artificial intelligence model to identify vertebral compression fractures on chest radiographs and its potential to address undiagnosed osteoporosis and its treatment.<\/p>\n<\/div>                            <\/div>\n                        <\/div>\n                                            <div class=\"study__row\">\n                            <div class=\"study__left\" data-aos=\"fade-up\">\n                                <h2 class=\"h5\">Materials and methods<\/h2>\n                            <\/div>\n                            <div class=\"study__right\" data-aos=\"fade-up\">\n                                <div class=\"text b1\"><p role=\"paragraph\">This retrospective study used a consecutive cohort of 596 chest radiographs from four US hospitals between 2015 and 2021. Each radiograph included both frontal (anteroposterior or posteroanterior) and lateral projections. These radiographs were assessed for the presence of vertebral compression fracture in a consensus manner by up to three thoracic radiologists. The model then performed inference on the cases. A chart review was also performed for the presence of osteoporosis-related\u00a0<i>International Classification of Diseases<\/i>, 10th revision diagnostic codes and medication use for the study period and an additional year of follow-up.<\/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>The model successfully completed inference on 595 cases (99.8%); these cases included 272 positive cases and 323 negative cases. The model performed with area under the receiver operating characteristic curve of 0.955 (95% confidence interval [CI]: 0.939-0.968), sensitivity 89.3% (95% CI: 85.7%-92.7%) and specificity 89.2% (95% CI: 85.4%-92.3%). Out of the 236 true-positive cases (ie, correctly identified vertebral compression fractures by the model) with available chart information, only 86 (36.4%) had a diagnosis of vertebral compression fracture and 140 (59.3%) had a diagnosis of either osteoporosis or osteopenia; only 78 (33.1%) were receiving a disease-modifying medication for osteoporosis.<\/p>\n<\/div>                            <\/div>\n                        <\/div>\n                                            <div class=\"study__row\">\n                            <div class=\"study__left\" data-aos=\"fade-up\">\n                                <h2 class=\"h5\">Conclusion<\/h2>\n                            <\/div>\n                            <div class=\"study__right\" data-aos=\"fade-up\">\n                                <div class=\"text b1\"><p>The model identified vertebral compression fracture accurately with a sensitivity 89.3% (95% CI: 85.7%-92.7%) and specificity of 89.2% (95% CI: 85.4%-92.3%). Its automated use could help identify patients who have undiagnosed osteoporosis and who may benefit from taking disease-modifying medications.<\/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 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=\"study__btn\">\n                                <a href=\"https:\/\/www.jacr.org\/article\/S1546-1440(24)00766-X\/abstract\" target=\"_blank\" class=\"btn btn--xl font-rules\" rel=\"noopener\">Fully study <span class=\"icon-arrow-right-up\"><\/span><\/a>\n                            <\/div>\n                        <\/div>\n                    <\/div>\n                            <\/div>\n        <\/div>\n    <\/section>\n\n\n\n    <section id=\"cta-block-block_1dcae3a32655173c4d91a8693795a556\" class=\"cta-block   text-\" >\n        <div class=\"container  container-\">\n            <div class=\"cta__content\">\n                <div class=\"cta__decor cta__decor--alt\">\n                    <div class=\"pixel-md-holder\">\n                        <span class=\"pixel-md-decor pixel-blur\" style=\"background-color: rgba(9, 114, 241, 0.15)\"><\/span>\n                        <span class=\"pixel-md-decor 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