Breast MRI is the most sensitive imaging modality for detecting breast cancer. However, its widespread use is limited by factors such as extended examination times, need for contrast agents, and susceptibility to motion artifacts. Artificial intelligence (AI) has emerged as a promising solution for these challenges by enhancing the efficiency and accuracy of breast MRI in multiple domains. AI-driven image reconstruction techniques have significantly reduced scan times while preserving image quality. This method outperforms traditional parallel imaging and compressed sensing. AI has also shown great promise for lesion classification and segmentation, with convolutional neural networks and U-Net architectures improving the differentiation between benign and malignant lesions. AI-based segmentation methods enable accurate tumor detection and characterization, thereby aiding personalized treatment planning. An AI triaging system has demonstrated the potential to streamline workflow efficiency by identifying low-suspicion cases and reducing the workload of radiologists. Another promising application is synthetic breast MR image generation, which aims to generate contrast enhanced images from non-contrast sequences, thereby improving accessibility and patient safety. Further research is required to validate AI models across diverse populations and imaging protocols. As AI continues to evolve, it is expected to play an important role in the optimization of breast MRI.

Breast MRI is the most sensitive imaging modality for detecting breast cancer. However, its widespread use is limited by factors such as extended examination times, need for contrast agents, and susceptibility to motion artifacts. Artificial intelligence (AI) has emerged as a promising solution for these challenges by enhancing the efficiency and accuracy of breast MRI in multiple domains. AI-driven image reconstruction techniques have significantly reduced scan times while preserving image quality. This method outperforms traditional parallel imaging and compressed sensing. AI has also shown great promise for lesion classification and segmentation, with convolutional neural networks and U-Net architectures improving the differentiation between benign and malignant lesions. AI-based segmentation methods enable accurate tumor detection and characterization, thereby aiding personalized treatment planning. An AI triaging system has demonstrated the potential to streamline workflow efficiency by identifying low-suspicion cases and reducing the workload of radiologists. Another promising application is synthetic breast MR image generation, which aims to generate contrast enhanced images from non-contrast sequences, thereby improving accessibility and patient safety. Further research is required to validate AI models across diverse populations and imaging protocols. As AI continues to evolve, it is expected to play an important role in the optimization of breast MRI.

Breast MRI is the most sensitive imaging modality for detecting breast cancer. However, its widespread use is limited by factors such as extended examination times, need for contrast agents, and susceptibility to motion artifacts. Artificial intelligence (AI) has emerged as a promising solution for these challenges by enhancing the efficiency and accuracy of breast MRI in multiple domains. AI-driven image reconstruction techniques have significantly reduced scan times while preserving image quality. This method outperforms traditional parallel imaging and compressed sensing. AI has also shown great promise for lesion classification and segmentation, with convolutional neural networks and U-Net architectures improving the differentiation between benign and malignant lesions. AI-based segmentation methods enable accurate tumor detection and characterization, thereby aiding personalized treatment planning. An AI triaging system has demonstrated the potential to streamline workflow efficiency by identifying low-suspicion cases and reducing the workload of radiologists. Another promising application is synthetic breast MR image generation, which aims to generate contrast enhanced images from non-contrast sequences, thereby improving accessibility and patient safety. Further research is required to validate AI models across diverse populations and imaging protocols. As AI continues to evolve, it is expected to play an important role in the optimization of breast MRI.

Coronavirus disease 2019 (COVID-19) vaccine-induced lymphadenopathy is a critical side effect that should be a concern to clinicians, patients, radiologists, and oncologists. Vaccine-induced lymphadenopathy causes a diagnostic dilemma, especially for breast radiologists who examine both axillary regions during breast ultrasound examinations. Appropriate imaging guidelines are needed to manage vaccine-induced lymphadenopathy for patients undergoing screening examinations or symptomatic patients, including cancer patients.Current Concepts: For patients with axillary lymphadenopathy in the setting of recent ipsilateral vaccination, clinical follow-up is recommended. In other scenarios, short-term follow-up axillary ultrasound examinations are recommended if the clinical concerns persist for more than 6 weeks after vaccination. To mitigate the diagnostic dilemma of vaccine-induced lymphadenopathy, patients should schedule screening imaging examinations before the first vaccination or at least six weeks following the second vaccination. For clinicians and radiologists, documenting the patients’ vaccination status is critical to decreasing unnecessary follow-up imaging, biopsies, and patient’s anxiety.Discussion and Conclusion: Our proposal can help reduce patient anxiety, provider burden, and costs of unnecessary evaluation of enlarged lymph nodes in the setting of recent COVID-19 vaccination. Further, it can avoid delays in vaccination and breast cancer screening during the COVID-19 pandemic.

No abstract available.
Hair ; Humans ; Piedra ; Scalp ; Trichosporon

No abstract available.
Nevus

No abstract available.
Candida

No abstract available.
Onychomycosis

No abstract available.
Candida ; Onychomycosis