2021
DOI: 10.1016/j.ejmp.2021.03.015
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Performance of an artificial intelligence tool with real-time clinical workflow integration – Detection of intracranial hemorrhage and pulmonary embolism

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Cited by 20 publications
(11 citation statements)
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“…Our best model also had better specificity with almost similar sensitivity than a multimodal fusion model combining information from CT images and electronic health records, which had a specificity of 90.2% and a sensitivity of 87.3% [ 37 ]. We achieved only slightly worse specificity with our best model than Aidoc’s commercial model (93.5% vs. 95–95.5%, respectively), despite their model having been trained with a considerably larger dataset (600 vs. ~ 28,000 CTPAs) [ 24 , 38 ]. Models presented by Yang et al and Tajbaksh et al aimed to localize each distinct embolus accurately, and their performance was evaluated differently than our model, which is why the models cannot be directly compared [ 21 , 39 ].…”
Section: Discussionmentioning
confidence: 99%
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“…Our best model also had better specificity with almost similar sensitivity than a multimodal fusion model combining information from CT images and electronic health records, which had a specificity of 90.2% and a sensitivity of 87.3% [ 37 ]. We achieved only slightly worse specificity with our best model than Aidoc’s commercial model (93.5% vs. 95–95.5%, respectively), despite their model having been trained with a considerably larger dataset (600 vs. ~ 28,000 CTPAs) [ 24 , 38 ]. Models presented by Yang et al and Tajbaksh et al aimed to localize each distinct embolus accurately, and their performance was evaluated differently than our model, which is why the models cannot be directly compared [ 21 , 39 ].…”
Section: Discussionmentioning
confidence: 99%
“…The model achieved sensitivity and specificity of 92.7% and 95.5%, respectively [ 23 ]. Buls et al also tested the model (version 1.3) and achieved a similar specificity of 95% but a lower sensitivity of 73% [ 24 ]. However, creating an annotated training set of this magnitude for own model development may not be feasible for single hospitals or research teams.…”
Section: Introductionmentioning
confidence: 99%
“…Machine learning can effectively detect intracranial hemorrhage on CT and can reduce the false negative rate for the detection of intracranial hemorrhage [6][7][8][9][10][11]. Consequently, artificial intelligence software has begun to be incorporated into the clinical workflow.…”
Section: Discussionmentioning
confidence: 99%
“…In addition, no blood draw is required, and ionizing radiation is not used as it is in x-ray or CT. 18,24,27,32,33 Computer-aided diagnosis has become a component of clinical work at many hospitals for a variety of applications, such as breast cancer screening and identification of pulmonary embolism or stroke. 34,35 DL has shown human-level performances in many imaging-based tasks, such as classification, segmentation, and recognition. 36,37 These studies also show that DL has the potential for various automated ultrasound (US) image analysis tasks.…”
Section: Related Workmentioning
confidence: 99%