Artificial Intelligence in Breast Ultrasound: From Diagnosis to Prognosis—A Rapid Review

Brunetti, Nicole; Calabrese, Massimo; Martinoli, Carlo; Tagliafico, Alberto

doi:10.3390/diagnostics13010058

Cited by 22 publications

(16 citation statements)

References 70 publications

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“…The proficiency of the technician or radiologist performing the ultrasound examination can influence the quality and accuracy of the results. Recent research highlights the necessity of standardized training and practices to address this constraint and guarantee consistent and dependable outcomes in various healthcare environments [ 27 , 48 , 49 ].…”

Section: Disadvantages/limitations Of Breast Ultrasound In Breast Can...mentioning

confidence: 99%

Evaluating the Role of Breast Ultrasound in Early Detection of Breast Cancer in Low- and Middle-Income Countries: A Comprehensive Narrative Review

Iacob,

Stoicescu

et al. 2024

Bioengineering

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Breast cancer, affecting both genders, but mostly females, exhibits shifting demographic patterns, with an increasing incidence in younger age groups. Early identification through mammography, clinical examinations, and breast self-exams enhances treatment efficacy, but challenges persist in low- and medium-income countries due to limited imaging resources. This review assesses the feasibility of employing breast ultrasound as the primary breast cancer screening method, particularly in resource-constrained regions. Following the PRISMA guidelines, this study examines 52 publications from the last five years. Breast ultrasound, distinct from mammography, offers advantages like radiation-free imaging, suitability for repeated screenings, and preference for younger populations. Real-time imaging and dense breast tissue evaluation enhance sensitivity, accessibility, and cost-effectiveness. However, limitations include reduced specificity, operator dependence, and challenges in detecting microcalcifications. Automatic breast ultrasound (ABUS) addresses some issues but faces constraints like potential inaccuracies and limited microcalcification detection. The analysis underscores the need for a comprehensive approach to breast cancer screening, emphasizing international collaboration and addressing limitations, especially in resource-constrained settings. Despite advancements, notably with ABUS, the primary goal is to contribute insights for optimizing breast cancer screening globally, improving outcomes, and mitigating the impact of this debilitating disease.

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Section: Disadvantages/limitations Of Breast Ultrasound In Breast Can...mentioning

confidence: 99%

Evaluating the Role of Breast Ultrasound in Early Detection of Breast Cancer in Low- and Middle-Income Countries: A Comprehensive Narrative Review

Iacob,

Stoicescu

et al. 2024

Bioengineering

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Section: Clinical Potential Challenges and Impact Of Ai-assisted Brea...mentioning

confidence: 99%

“…However, the potential impact of AI can be measured only when more multicentric and prospective studies are performed to analyze the feasibility of incorporating AI in the clinical diagnosis of radiologists. 42 AI-based approaches in BC diagnosis have reduced the misidentification and diagnosis error caused by radiologists. AI provides objective analysis by considering texture, internal structural details, discriminative and unique features for effective classification and diagnosis.…”

Section: Clinical Potential Challenges and Impact Of Ai-assisted Brea...mentioning

confidence: 99%

Review on Deep Learning-Based CAD Systems for Breast Cancer Diagnosis

Kumar¹,

Sasikala²

2023

Technol Cancer Res Treat

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Breast Cancer (BC) is a major health issue in women of the age group above 45. Identification of BC at an earlier stage is important to reduce the mortality rate. Image-based noninvasive methods are used for early detection and for providing appropriate treatment. Computer-Aided Diagnosis (CAD) schemes can support radiologists in making correct decisions. Computational intelligence paradigms such as Machine Learning (ML) and Deep Learning (DL) have been used in the recent past in CAD systems to accelerate diagnosis. ML techniques are feature driven and require a high amount of domain expertise. However, DL approaches make decisions directly from the image. The current advancement in DL approaches for early diagnosis of BC is the motivation behind this review. This article throws light on various types of CAD approaches used in BC detection and diagnosis. A survey on DL, Transfer Learning, and DL-based CAD approaches for the diagnosis of BC is presented in detail. A comparative study on techniques, datasets, and performance metrics used in state-of-the-art literature in BC diagnosis is also summarized. The proposed work provides a review of recent advancements in DL techniques for enhancing BC diagnosis.

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“…However, despite its numerous benefits, US also presents certain challenges. These include a lower image quality due to noise and artifacts, a high dependence on the operator or diagnostician’s experience, and significant variations in performance across different institutions and manufacturers’ US systems [ 3 ].…”

Section: Introductionmentioning

confidence: 99%

Ultrasound Image Analysis with Vision Transformers—Review

Vafaeezadeh,

Behnam,

Gifani

2024

Diagnostics

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Ultrasound (US) has become a widely used imaging modality in clinical practice, characterized by its rapidly evolving technology, advantages, and unique challenges, such as a low imaging quality and high variability. There is a need to develop advanced automatic US image analysis methods to enhance its diagnostic accuracy and objectivity. Vision transformers, a recent innovation in machine learning, have demonstrated significant potential in various research fields, including general image analysis and computer vision, due to their capacity to process large datasets and learn complex patterns. Their suitability for automatic US image analysis tasks, such as classification, detection, and segmentation, has been recognized. This review provides an introduction to vision transformers and discusses their applications in specific US image analysis tasks, while also addressing the open challenges and potential future trends in their application in medical US image analysis. Vision transformers have shown promise in enhancing the accuracy and efficiency of ultrasound image analysis and are expected to play an increasingly important role in the diagnosis and treatment of medical conditions using ultrasound imaging as technology progresses.

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Artificial Intelligence in Breast Ultrasound: From Diagnosis to Prognosis—A Rapid Review

Cited by 22 publications

References 70 publications

Evaluating the Role of Breast Ultrasound in Early Detection of Breast Cancer in Low- and Middle-Income Countries: A Comprehensive Narrative Review

Evaluating the Role of Breast Ultrasound in Early Detection of Breast Cancer in Low- and Middle-Income Countries: A Comprehensive Narrative Review

Review on Deep Learning-Based CAD Systems for Breast Cancer Diagnosis

Ultrasound Image Analysis with Vision Transformers—Review

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