{"id":2808,"date":"2023-08-24T01:04:00","date_gmt":"2023-08-23T15:04:00","guid":{"rendered":"https:\/\/harrison.ai\/?p=2808"},"modified":"2025-10-04T20:09:44","modified_gmt":"2025-10-04T10:09:44","slug":"effects-of-a-comprehensive-brain-computed-tomography-deep-learning-model-on-radiologist-detection-accuracy","status":"publish","type":"post","link":"https:\/\/harrison.ai\/effects-of-a-comprehensive-brain-computed-tomography-deep-learning-model-on-radiologist-detection-accuracy\/","title":{"rendered":"Effects of a comprehensive brain computed tomography deep learning model on radiologist detection accuracy"},"content":{"rendered":"    <section id=\"evidence-block-block_801413ce8eda30577cacf6ce2daad2b4\" 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>Buchlak QD, Tang CHM, Seah JCY, Johnson A, Holt X, Bottrell GM, Wardman JB, Samarasinghe G, Dos Santos Pinheiro L, Xia H, Ahmad HK, Pham H, Chiang JI, Ektas N,\u00a0 Milne MR, Chiu CHY, Hachey B, Ryan MK, Johnston BP, Esmaili N, Bennett C, Goldschlager T, Hall J, Vo DT, Oakden-Rayner L, Leveque J-C, Farrokhi F, Abramson RG, Jones CM, Edelstein S &amp; Brotchie P<\/p>\n<p>European Radiology. First published online 22 August 2023.<\/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\">Abstract<\/h2>\n                            <\/div>\n                            <div class=\"study__right\" data-aos=\"fade-up\">\n                                                            <\/div>\n                        <\/div>\n                                            <div class=\"study__row\">\n                            <div class=\"study__left\" data-aos=\"fade-up\">\n                                <h2 class=\"h5\">Objectives<\/h2>\n                            <\/div>\n                            <div class=\"study__right\" data-aos=\"fade-up\">\n                                <div class=\"text b1\"><p><span class=\"s1\">Non-contrast computed tomography of the brain (NCCTB) is commonly used to detect intracranial pathology but is subject to interpretation errors. Machine learning can augment clinical decision-making and improve NCCTB scan interpretation. This retrospective detection accuracy study assessed the performance of radiologists assisted by a deep learning model and compared the standalone performance of the model with that of unassisted radiologists.<\/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\">Methods<\/h2>\n                            <\/div>\n                            <div class=\"study__right\" data-aos=\"fade-up\">\n                                <div class=\"text b1\"><p><span class=\"s1\">A deep learning model was trained on 212,484 NCCTB scans drawn from a private radiology group in Australia. Scans from inpatient, outpatient, and emergency settings were included. Scan inclusion criteria were age\u2009\u2265\u200918\u00a0years and series slice thickness\u2009\u2264\u20091.5\u00a0mm. Thirty-two radiologists reviewed 2848 scans with and without the assistance of the deep learning system and rated their confidence in the presence of each finding using a 7-point scale. Differences in AUC and Matthews correlation coefficient (MCC) were calculated using a ground-truth gold standard.<\/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=\"s1\">The model demonstrated an average area under the receiver operating characteristic curve (AUC) of 0.93 across 144 NCCTB findings and significantly improved radiologist interpretation performance. Assisted and unassisted radiologists demonstrated an average AUC of 0.79 and 0.73 across 22 grouped parent findings and 0.72 and 0.68 across 189 child findings, respectively. When assisted by the model, radiologist AUC was significantly improved for 91 findings (158 findings were non-inferior), and reading time was significantly reduced.<\/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\">Conclusions<\/h2>\n                            <\/div>\n                            <div class=\"study__right\" data-aos=\"fade-up\">\n                                <div class=\"text b1\"><p><span class=\"s1\">The assistance of a comprehensive deep learning model significantly improved radiologist detection accuracy across a wide range of clinical findings and demonstrated the potential to improve NCCTB interpretation.<\/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\">Clinical relevance statement<\/h2>\n                            <\/div>\n                            <div class=\"study__right\" data-aos=\"fade-up\">\n                                <div class=\"text b1\"><p><span class=\"s1\">This study evaluated a comprehensive CT brain deep learning model, which performed strongly, improved the performance of radiologists, and reduced interpretation time. The model may reduce errors, improve efficiency, facilitate triage, and better enable the delivery of timely patient care.<\/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 Points<\/h2>\n                            <\/div>\n                            <div class=\"study__right\" data-aos=\"fade-up\">\n                                <div class=\"text b1\"><p class=\"p2\"><span class=\"s1\"><i>\u2022 <\/i>This study demonstrated that the use of a comprehensive deep learning system assisted radiologists in the detection of a wide range of abnormalities on non-contrast brain computed tomography scans.<\/span><\/p>\n<p class=\"p2\"><span class=\"s1\">\u2022 The deep learning model demonstrated an average area under the receiver operating characteristic curve of 0.93 across 144 findings and significantly improved radiologist interpretation performance.<\/span><\/p>\n<p class=\"p2\"><span class=\"s1\">\u2022 The assistance of the comprehensive deep learning model significantly reduced the time required for radiologists to interpret computed tomography scans of the brain.<\/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               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