Updates in Artificial Intelligence for Breast Imaging

2021 ◽  
Author(s):  
Manisha Bahl
Keyword(s):  
Artificial Intelligence ◽  
Breast Imaging

scholarly journals Application of ultrasound artificial intelligence in the differential diagnosis between benign and malignant breast lesions of BI-RADS 4A

BMC Cancer ◽  
2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Sihua Niu ◽  
Jianhua Huang ◽  
Jia Li ◽  
Xueling Liu ◽  
Dan Wang ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Differential Diagnosis ◽  
Breast Imaging ◽  
Texture Features ◽  
Matrix Analysis ◽  
Width Ratio ◽  
Malignant Group ◽  
Malignant Lesions ◽  
Occurrence Matrix ◽  
Benign Group

Abstract Background The classification of Breast Imaging Reporting and Data System 4A (BI-RADS 4A) lesions is mostly based on the personal experience of doctors and lacks specific and clear classification standards. The development of artificial intelligence (AI) provides a new method for BI-RADS categorisation. We analysed the ultrasonic morphological and texture characteristics of BI-RADS 4A benign and malignant lesions using AI, and these ultrasonic characteristics of BI-RADS 4A benign and malignant lesions were compared to examine the value of AI in the differential diagnosis of BI-RADS 4A benign and malignant lesions. Methods A total of 206 lesions of BI-RADS 4A examined using ultrasonography were analysed retrospectively, including 174 benign lesions and 32 malignant lesions. All of the lesions were contoured manually, and the ultrasonic morphological and texture features of the lesions, such as circularity, height-to-width ratio, margin spicules, margin coarseness, margin indistinctness, margin lobulation, energy, entropy, grey mean, internal calcification and angle between the long axis of the lesion and skin, were calculated using grey level gradient co-occurrence matrix analysis. Differences between benign and malignant lesions of BI-RADS 4A were analysed. Results Significant differences in margin lobulation, entropy, internal calcification and ALS were noted between the benign group and malignant group (P = 0.013, 0.045, 0.045, and 0.002, respectively). The malignant group had more margin lobulations and lower entropy compared with the benign group, and the benign group had more internal calcifications and a greater angle between the long axis of the lesion and skin compared with the malignant group. No significant differences in circularity, height-to-width ratio, margin spicules, margin coarseness, margin indistinctness, energy, and grey mean were noted between benign and malignant lesions. Conclusions Compared with the naked eye, AI can reveal more subtle differences between benign and malignant BI-RADS 4A lesions. These results remind us carefully observation of the margin and the internal echo is of great significance. With the help of morphological and texture information provided by AI, doctors can make a more accurate judgment on such atypical benign and malignant lesions.

Factors Influencing Trainees’ Interest in Breast Imaging

2021 ◽  
pp. 084653712110495
Author(s):  
Tong Wu ◽  
Wyanne Law ◽  
Nayaar Islam ◽  
Charlotte J. Yong-Hing ◽  
Supriya Kulkarni ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Medical Students ◽  
Breast Imaging ◽  
Current Level ◽  
Common Reason ◽  
Training Level ◽  
Chi Square ◽  
Factors Influencing ◽  
Radiology Residents ◽  
The Impact

Purpose: To gauge the level of interest in breast imaging (BI) and determine factors impacting trainees’ decision to pursue this subspecialty. Methods: Canadian radiology residents and medical students were surveyed from November 2020 to February 2021. Training level, actual vs preferred timing of breast rotations, fellowship choices, perceptions of BI, and how artificial intelligence (AI) will impact BI were collected. Chi-square, Fisher’s exact tests and univariate logistic regression were performed to determine the impact of trainees’ perceptions on interest in pursuing BI/women’s imaging (WI) fellowships. Results: 157 responses from 80 radiology residents and 77 medical students were collected. The top 3 fellowship subspecialties desired by residents were BI/WI (36%), abdominal imaging (35%), and interventional radiology (25%). Twenty-five percent of the medical students were unsure due to lack of exposure. The most common reason that trainees found BI unappealing was repetitiveness (20%), which was associated with lack of interest in BI/WI fellowships (OR = 3.9, 95% CI: 1.6-9.5, P = .002). The most common reason residents found BI appealing was procedures (59%), which was associated with interest in BI/WI fellowships (OR, 3.2, 95% CI, 1.2-8.6, P = .02). Forty percent of residents reported an earlier start of their first breast rotation (PGY1-2) would affect their fellowship choice. Conclusion: This study assessed the current level of Canadian trainees’ interest in BI and identified factors that influenced their decisions to pursue BI. Solutions for increased interest include earlier exposure to breast radiology and addressing inadequacies in residency training.

scholarly journals Artificial Intelligence: A Primer for Breast Imaging Radiologists

2020 ◽  
Vol 2 (4) ◽  
pp. 304-314
Author(s):  
Manisha Bahl
Keyword(s):  
Breast Cancer ◽  
Artificial Intelligence ◽  
Cancer Detection ◽  
Breast Imaging ◽  
Breast Cancer Detection ◽  
Computer Algorithms ◽  
Prevention Strategies ◽  
Future Directions ◽  
Optimal Patient Care ◽  
Screening And Prevention

Abstract Artificial intelligence (AI) is a branch of computer science dedicated to developing computer algorithms that emulate intelligent human behavior. Subfields of AI include machine learning and deep learning. Advances in AI technologies have led to techniques that could increase breast cancer detection, improve clinical efficiency in breast imaging practices, and guide decision-making regarding screening and prevention strategies. This article reviews key terminology and concepts, discusses common AI models and methods to validate and evaluate these models, describes emerging AI applications in breast imaging, and outlines challenges and future directions. Familiarity with AI terminology, concepts, methods, and applications is essential for breast imaging radiologists to critically evaluate these emerging technologies, recognize their strengths and limitations, and ultimately ensure optimal patient care.

Applications of Artificial Intelligence in Breast Imaging

2021 ◽  
Vol 59 (1) ◽  
pp. 139-148
Author(s):  
Matthew B. Morgan ◽  
Jonathan L. Mates
Keyword(s):  
Artificial Intelligence ◽  
Breast Imaging

scholarly journals Application of ultrasound artificial intelligence in the differential diagnosis between benign and malignant breast lesions of BI-RADS 4A

2020 ◽  
Author(s):  
Sihua Niu ◽  
Jianhua Huang ◽  
Jia Li ◽  
Xueling Liu ◽  
Dan Wang ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Differential Diagnosis ◽  
Breast Imaging ◽  
Texture Features ◽  
Matrix Analysis ◽  
Width Ratio ◽  
Malignant Group ◽  
Malignant Lesions ◽  
Occurrence Matrix ◽  
Benign Group

Abstract Background: The classification of Breast Imaging Reporting and Data System 4A (BI-RADS 4A) lesions is mostly based on the personal experience of doctors and lacks specific and clear classification standards. The development of artificial intelligence (AI) provides a new method for BI-RADS categorisation. We analysed the ultrasonic morphological and texture characteristics of BI-RADS 4A benign and malignant lesions using AI, and these ultrasonic characteristics of BI-RADS 4A benign and malignant lesions were compared to examine the value of AI in the differential diagnosis of BI-RADS 4A benign and malignant lesions.Methods: A total of 206 lesions of BI-RADS 4A examined using ultrasonography were analysed retrospectively, including 174 benign lesions and 32 malignant lesions. All of the lesions were contoured manually, and the ultrasonic morphological and texture features of the lesions, such as circularity, height-to-width ratio, margin spicules, margin coarseness, margin indistinctness, margin lobulation, energy, entropy, grey mean, internal calcification and angle between the long axis of the lesion and skin, were calculated using grey level gradient co-occurrence matrix analysis. Differences between benign and malignant lesions of BI-RADS 4A were analysed.Results: Significant differences in margin lobulation, entropy, internal calcification and ALS were noted between the benign group and malignant group (P=0.013, 0.045, 0.045, and 0.002, respectively). The malignant group had more margin lobulations and lower entropy compared with the benign group, and the benign group had more internal calcifications and a greater angle between the long axis of the lesion and skin compared with the malignant group. No significant differences in circularity, height-to-width ratio, margin spicules, margin coarseness, margin indistinctness, energy, and grey mean were noted between benign and malignant lesions.Conclusions: Compared with the naked eye, AI can reveal more subtle differences between benign and malignant BI-RADS 4A lesions. These results remind us carefully observation of the margin and the internal echo is of great significance. With the help of morphological and texture information provided by AI, doctors can make a more accurate judgment on such atypical benign and malignant lesions.

scholarly journals Application of Ultrasound Artificial Intelligence in the Differential Diagnosis Between Benign and Malignant Breast Lesions of BI-RADS 4A

2020 ◽  
Author(s):  
SIHUA Niu ◽  
Jianhua Huang ◽  
Jia Li ◽  
Xueling Liu ◽  
Dan Wang ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Differential Diagnosis ◽  
Breast Imaging ◽  
Matrix Analysis ◽  
Width Ratio ◽  
Grey Level ◽  
Edge Roughness ◽  
Malignant Group ◽  
Malignant Lesions ◽  
Benign Group

Abstract Background: The classification of Breast Imaging Reporting and Data System 4A (BI-RADS 4A) lesions is entirely based on the personal experience of doctors and lacks specific and clear classification standards. The development of artificial intelligence (AI) provides a new method for BI-RADS category. We analysed the morphological and texture characteristics of BI-RADS 4A benign and malignant lesions using AI, and the ultrasonic characteristics of BI-RADS 4A benign and malignant lesions were compared to examine the value of AI in the differential diagnosis of BI-RADS 4A benign and malignant lesions.Methods: A total of 206 lesions of BI-RADS 4A examined using ultrasonography were analysed retrospectively, including 174 benign lesions and 32 malignant lesions. All of the lesions were contoured manually, and the morphological and texture features of the lesions, such as circularity, depth-to-width ratio, number of spicules, edge roughness, edge fuzziness, margin lobules, energy, entropy, mean grey level, grey level variance, grey level similarity, internal calcification and angle between the long axis of the lesion and skin（ALS） of the ROI, were calculated using grey level gradient co-occurrence matrix analysis. The differences between benign and malignant lesions of BI-RADS 4A were analysed.Results: There were significant differences between the benign group and malignant group in margin lobules, entropy, internal calcification and ALS (P=0.013, 0.045, 0.045, 0.002, respectively). The malignant group had more margin lobules and lower entropy than the benign group, and the benign group had more internal calcification and a larger ALS than the malignant group. There were no significant differences in circularity, depth-to-width ratio, number of spicules, edge roughness, edge fuzziness, energy, mean of grey level, grey level variance, and grey level similarity between benign and malignant lesions.Conclusion: For benign and malignant lesions of BI-RADS 4A, margin lobules and internal echo uniformity are the critical points of differentiation. Some of the characteristics of atypical benign and malignant lesions are blurry or even inverted, which may lead to a deviation of the characteristics of benign and malignant lesions.

scholarly journals Current Status and Future Perspectives of Artificial Intelligence in Magnetic Resonance Breast Imaging

2020 ◽  
Vol 2020 ◽  
pp. 1-18
Author(s):  
Anke Meyer-Bäse ◽  
Lia Morra ◽  
Uwe Meyer-Bäse ◽  
Katja Pinker
Keyword(s):  
Artificial Intelligence ◽  
Magnetic Resonance ◽  
Breast Imaging ◽  
Clinical Decision Making ◽  
Therapy Monitoring ◽  
Breast Mri ◽  
Clinical Decision ◽  
Current Status ◽  
Imaging Biomarkers ◽  
Future Perspectives

Recent advances in artificial intelligence (AI) and deep learning (DL) have impacted many scientific fields including biomedical maging. Magnetic resonance imaging (MRI) is a well-established method in breast imaging with several indications including screening, staging, and therapy monitoring. The rapid development and subsequent implementation of AI into clinical breast MRI has the potential to affect clinical decision-making, guide treatment selection, and improve patient outcomes. The goal of this review is to provide a comprehensive picture of the current status and future perspectives of AI in breast MRI. We will review DL applications and compare them to standard data-driven techniques. We will emphasize the important aspect of developing quantitative imaging biomarkers for precision medicine and the potential of breast MRI and DL in this context. Finally, we will discuss future challenges of DL applications for breast MRI and an AI-augmented clinical decision strategy.

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