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Breast imaging, including breast MRI, has played an important role in the rapid improvement of breast cancer treatment.
Breast imaging, including breast MRI, has played an important role in the rapid improvement of breast cancer treatment.
A number of studies have developed computer vision and machine learning artificial intelligence (AI) systems, which can be used for computer-aided diagnosis on clinical images and quantitative characterization of breast lesions.
Recently, a study published in the Journal of Radiology evaluated whether the diagnostic performance of radiologists in distinguishing benign and malignant lesions on breast DCE MRI images was improved compared with traditional software when using the AI system, which is the further application of AI in the clinic.
A total of 111 women (mean age 52 years ± 13 years [standard deviation]) were enrolled in this study, and 111 groups of breast DCE MRI examinations were obtained (54 cases of malignant lesions and 57 cases of benign lesions) were obtained.
When using the AI system, the average AUC of all readers increased from 0.
Figure According to the breast imaging report and data system (BI-RADS) category 4a threshold in the diagnosis task of identifying benign and malignant lesions on the dynamically enhanced breast MRI image, the sensitivity and specificity of the first and second review by 19 readers (Expressed as a percentage) to compare.
Figure According to the breast imaging report and data system (BI-RADS) category 4a threshold in the diagnosis task of identifying benign and malignant lesions on the dynamically enhanced breast MRI image, the sensitivity and specificity of the first and second review by 19 readers (Expressed as a percentage) to compare.
This study shows that the use of artificial intelligence systems improves the diagnostic performance of radiologists in identifying benign and malignant lesions in breast MRI, provides technical support for further clinical development of more accurate treatment plans, and provides artificial intelligence in clinical and scientific research.
Original source:
Yulei Jiang , Alexandra V Edwards , Gillian M Newstead .
Jiang Yulei , Alexandra V Edwards , Gillian M Newstead .
Artificial Intelligence Applied to Breast MRI for Improved Diagnosis .
DOI: org/10.
1148/radiol.
2020200292">10.
1148 / radiol.
2020200292 10.
1148 / radiol.
2020200292 in this message
