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For: Antico M, Sasazawa F, Dunnhofer M, Camps S, Jaiprakash A, Pandey A, Crawford R, Carneiro G, Fontanarosa D. Deep Learning-Based Femoral Cartilage Automatic Segmentation in Ultrasound Imaging for Guidance in Robotic Knee Arthroscopy. Ultrasound in Medicine & Biology 2020;46:422-35. [DOI: 10.1016/j.ultrasmedbio.2019.10.015] [Cited by in Crossref: 15] [Cited by in F6Publishing: 16] [Article Influence: 7.5] [Reference Citation Analysis]
Number Citing Articles
1 Dunnhofer M, Martinel N, Micheloni C. Deep convolutional feature details for better knee disorder diagnoses in magnetic resonance images. Computerized Medical Imaging and Graphics 2022. [DOI: 10.1016/j.compmedimag.2022.102142] [Reference Citation Analysis]
2 Guo N, Tian J, Wang L, Sun K, Mi L, Ming H, Zhe Z, Sun F. Discussion on the possibility of multi-layer intelligent technologies to achieve the best recover of musculoskeletal injuries: Smart materials, variable structures, and intelligent therapeutic planning. Front Bioeng Biotechnol 2022;10:1016598. [DOI: 10.3389/fbioe.2022.1016598] [Reference Citation Analysis]
3 Harkey MS, Michel N, Kuenze C, Fajardo R, Salzler M, Driban JB, Hacihaliloglu I. Validating a Semi-Automated Technique for Segmenting Femoral Articular Cartilage on Ultrasound Images. CARTILAGE 2022;13:194760352210930. [DOI: 10.1177/19476035221093069] [Cited by in F6Publishing: 1] [Reference Citation Analysis]
4 Ross MT, Antico M, McMahon KL, Ren J, Powell SK, Pandey AK, Allenby MC, Fontanarosa D, Woodruff MA. Ultrasound Imaging Offers Promising Alternative to Create 3-D Models for Personalised Auricular Implants. Ultrasound Med Biol 2021:S0301-5629(21)00453-1. [PMID: 34848081 DOI: 10.1016/j.ultrasmedbio.2021.10.013] [Reference Citation Analysis]
5 Matsumoto M, Karube M, Nakagami G, Kitamura A, Tamai N, Miura Y, Kawamoto A, Kurita M, Miyake T, Hayashi C, Kawasaki A, Sanada H. Development of an Automatic Ultrasound Image Classification System for Pressure Injury Based on Deep Learning. Applied Sciences 2021;11:7817. [DOI: 10.3390/app11177817] [Reference Citation Analysis]
6 Marzola F, van Alfen N, Doorduin J, Meiburger KM. Deep learning segmentation of transverse musculoskeletal ultrasound images for neuromuscular disease assessment. Comput Biol Med 2021;135:104623. [PMID: 34252683 DOI: 10.1016/j.compbiomed.2021.104623] [Cited by in Crossref: 7] [Cited by in F6Publishing: 8] [Article Influence: 7.0] [Reference Citation Analysis]
7 Kim YH. Artificial intelligence in medical ultrasonography: driving on an unpaved road. Ultrasonography 2021;40:313-7. [PMID: 34053212 DOI: 10.14366/usg.21031] [Cited by in Crossref: 4] [Cited by in F6Publishing: 4] [Article Influence: 4.0] [Reference Citation Analysis]
8 Athavale AM, Hart PD, Itteera M, Cimbaluk D, Patel T, Alabkaa A, Arruda J, Singh A, Rosenberg A, Kulkarni H. Development and Validation of a Deep Learning Model to Quantify Interstitial Fibrosis and Tubular Atrophy From Kidney Ultrasonography Images. JAMA Netw Open 2021;4:e2111176. [PMID: 34028548 DOI: 10.1001/jamanetworkopen.2021.11176] [Cited by in Crossref: 4] [Cited by in F6Publishing: 4] [Article Influence: 4.0] [Reference Citation Analysis]
9 Piccialli F, Somma VD, Giampaolo F, Cuomo S, Fortino G. A survey on deep learning in medicine: Why, how and when? Information Fusion 2021;66:111-37. [DOI: 10.1016/j.inffus.2020.09.006] [Cited by in Crossref: 84] [Cited by in F6Publishing: 95] [Article Influence: 84.0] [Reference Citation Analysis]
10 Wang VM, Cheung CA, Kozar AJ, Huang B. Machine Learning Applications in Orthopaedic Imaging. J Am Acad Orthop Surg 2020;28:e415-7. [PMID: 32053527 DOI: 10.5435/JAAOS-D-19-00688] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
11 Shin Y, Yang J, Lee YH, Kim S. Artificial intelligence in musculoskeletal ultrasound imaging. Ultrasonography 2021;40:30-44. [PMID: 33242932 DOI: 10.14366/usg.20080] [Cited by in Crossref: 13] [Cited by in F6Publishing: 13] [Article Influence: 6.5] [Reference Citation Analysis]
12 Athavale AM, Hart PD, Itteera M, Cimbaluk D, Patel T, Alabka A, Dunea G, Arruda J, Singh A, Rosenberg A, Kulkarni H. DEEP LEARNING TO PREDICT DEGREE OF INTERSTITIAL FIBROSIS AND TUBULAR ATROPHY FROM KIDNEY ULTRASOUND IMAGES – AN ARTIFICIAL INTELLIGENCE APPROACH.. [DOI: 10.1101/2020.08.17.20176958] [Reference Citation Analysis]
13 Huang Y, Yan W, Xia M, Guo Y, Zhou G, Wang Y. Vessel membrane segmentation and calcification location in intravascular ultrasound images using a region detector and an effective selection strategy. Comput Methods Programs Biomed 2020;189:105339. [PMID: 31978806 DOI: 10.1016/j.cmpb.2020.105339] [Cited by in Crossref: 3] [Cited by in F6Publishing: 5] [Article Influence: 1.5] [Reference Citation Analysis]
14 Antico M, Sasazawa F, Takeda Y, Jaiprakash AT, Wille M, Pandey AK, Crawford R, Carneiro G, Fontanarosa D. Bayesian CNN for Segmentation Uncertainty Inference on 4D Ultrasound Images of the Femoral Cartilage for Guidance in Robotic Knee Arthroscopy. IEEE Access 2020;8:223961-75. [DOI: 10.1109/access.2020.3044355] [Cited by in Crossref: 5] [Cited by in F6Publishing: 4] [Article Influence: 2.5] [Reference Citation Analysis]
15 Antico M, Sasazawa F, Takeda Y, Jaiprakash AT, Wille M, Pandey AK, Crawford R, Fontanarosa D. 4D Ultrasound-Based Knee Joint Atlas for Robotic Knee Arthroscopy: A Feasibility Study. IEEE Access 2020;8:146331-41. [DOI: 10.1109/access.2020.3014999] [Cited by in Crossref: 4] [Cited by in F6Publishing: 4] [Article Influence: 2.0] [Reference Citation Analysis]