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For: Kainz B, Heinrich MP, Makropoulos A, Oppenheimer J, Mandegaran R, Sankar S, Deane C, Mischkewitz S, Al-Noor F, Rawdin AC, Ruttloff A, Stevenson MD, Klein-Weigel P, Curry N. Non-invasive diagnosis of deep vein thrombosis from ultrasound imaging with machine learning. NPJ Digit Med 2021;4:137. [PMID: 34526639 DOI: 10.1038/s41746-021-00503-7] [Cited by in Crossref: 9] [Cited by in F6Publishing: 9] [Article Influence: 9.0] [Reference Citation Analysis]
Number Citing Articles
1 Choudhury A, Asan O. Impact of cognitive workload and situation awareness on clinicians’ willingness to use an Artificial Intelligence system in clinical practice. IISE Transactions on Healthcare Systems Engineering 2022. [DOI: 10.1080/24725579.2022.2127035] [Reference Citation Analysis]
2 Bowness JS, Macfarlane AJR, Burckett-St Laurent D, Harris C, Margetts S, Morecroft M, Phillips D, Rees T, Sleep N, Vasalauskaite A, West S, Noble JA, Higham H. Evaluation of the impact of assistive artificial intelligence on ultrasound scanning for regional anaesthesia. Br J Anaesth 2022:S0007-0912(22)00437-8. [PMID: 36088136 DOI: 10.1016/j.bja.2022.07.049] [Reference Citation Analysis]
3 VanBerlo B, Wu D, Li B, Rahman MA, Hogg G, VanBerlo B, Tschirhart J, Ford A, Ho J, McCauley J, Wu B, Deglint J, Hargun J, Chaudhary R, Dave C, Arntfield R. Accurate assessment of the lung sliding artefact on lung ultrasonography using a deep learning approach. Comput Biol Med 2022;148:105953. [PMID: 35985186 DOI: 10.1016/j.compbiomed.2022.105953] [Reference Citation Analysis]
4 Seo JW, Park S, Kim YJ, Hwang JH, Yu SH, Kim JH, Kim KG. Artificial intelligence-based iliofemoral deep venous thrombosis detection using a clinical approach.. [DOI: 10.21203/rs.3.rs-1921650/v1] [Reference Citation Analysis]
5 Bowness JS, Burckett-St Laurent D, Hernandez N, Keane PA, Lobo C, Margetts S, Moka E, Pawa A, Rosenblatt M, Sleep N, Taylor A, Woodworth G, Vasalauskaite A, Noble JA, Higham H. Assistive artificial intelligence for ultrasound image interpretation in regional anaesthesia: an external validation study. Br J Anaesth 2022:S0007-0912(22)00351-8. [PMID: 35987706 DOI: 10.1016/j.bja.2022.06.031] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
6 Shodiq MN, Yuniarno EM, Nugroho J, Purnama IKE. Ultrasound Image Segmentation for Deep Vein Thrombosis using Unet-CNN based on Denoising Filter. 2022 IEEE International Conference on Imaging Systems and Techniques (IST) 2022. [DOI: 10.1109/ist55454.2022.9827731] [Reference Citation Analysis]
7 Contreras-luján EE, García-guerrero EE, López-bonilla OR, Tlelo-cuautle E, López-mancilla D, Inzunza-gonzález E. Evaluation of Machine Learning Algorithms for Early Diagnosis of Deep Venous Thrombosis. MCA 2022;27:24. [DOI: 10.3390/mca27020024] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
8 Graf L, Mischkewitz S, Hansen L, Heinrich MP. Spatiotemporal Attention for Realtime Segmentation of Corrupted Sequential Ultrasound Data. Informatik aktuell 2022. [DOI: 10.1007/978-3-658-36932-3_50] [Reference Citation Analysis]