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Jan 14, 2022 (publication date) through Nov 28, 2024
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Publication Name
World Journal of Clinical Cases
ISSN
2307-8960
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Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA
| For: | Lyu SY, Zhang Y, Zhang MW, Zhang BS, Gao LB, Bai LT, Wang J. Diagnostic value of artificial intelligence automatic detection systems for breast BI-RADS 4 nodules. World J Clin Cases 2022; 10(2): 518-527 [PMID: 35097077 DOI: 10.12998/wjcc.v10.i2.518] |
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| URL: | https://www.wjgnet.com/1949-8470/full/v10/i2/518.htm |
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Sadam Hussain, Usman Naseem, Mansoor Ali, Daly Betzabeth Avendaño Avalos, Servando Cardona-Huerta, Beatriz Alejandra Bosques Palomo, Jose Gerardo Tamez-Peña. TECRR: a benchmark dataset of radiological reports for BI-RADS classification with machine learning, deep learning, and large language model baselines. BMC Medical Informatics and Decision Making 2024; 24(1) doi: 10.1186/s12911-024-02717-7
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Nicole Brunetti, Massimo Calabrese, Carlo Martinoli, Alberto Stefano Tagliafico. Artificial Intelligence in Breast Ultrasound: From Diagnosis to Prognosis—A Rapid Review. Diagnostics 2022; 13(1): 58 doi: 10.3390/diagnostics13010058
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Olga Guiban, Antonello Rubini, Gianfranco Vallone, Corrado Caiazzo, Marco Di Serafino, Federica Pediconi, Laura Ballesio, Federica Trenta, Corrado De Vito, Arenta Shkelqimi, Ludovica Costanzo, Daniele Fresilli, Veronica Rizzo, Vito Cantisani, Massimo Vergine. Can New Ultrasound Imaging Techniques Improve Breast Lesion Characterization? Prospective Comparison between Ultrasound BI-RADS and Semi-Automatic Software “SmartBreast”, Strain Elastography, and Shear Wave Elastography. Applied Sciences 2023; 13(11): 6764 doi: 10.3390/app13116764
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Belinda Lokaj, Marie-Thérèse Pugliese, Karen Kinkel, Christian Lovis, Jérôme Schmid. Barriers and facilitators of artificial intelligence conception and implementation for breast imaging diagnosis in clinical practice: a scoping review. European Radiology 2023; 34(3): 2096 doi: 10.1007/s00330-023-10181-6
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I. P. C. Buzatto, S. A. Recife, L. Miguel, R. M. Bonini, N. Onari, A. L. P. A. Faim, L. Silvestre, D. P. Carlotti, A. Fröhlich, D. G. Tiezzi. Machine learning can reliably predict malignancy of breast lesions based on clinical and ultrasonographic features. Breast Cancer Research and Treatment 2024; doi: 10.1007/s10549-024-07429-0
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