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Item type:Publication, Breast ultrasound volume sweep imaging: A new horizon in expanding imaging access for breast cancer detection(Wiley, 2022-07-08)The majority of people in the world lack basic access to breast diagnostic imaging resulting in delay to diagnosis of breast cancer. In this study, we tested a volume sweep imaging (VSI) ultrasound protocol for evaluation of palpable breast lumps that can be performed by operators after minimal training without prior ultrasound experience as a means to increase accessibility to breast ultrasound. METHODS: Medical students without prior ultrasound experience were trained for less than 2 hours on the VSI breast ultrasound protocol. Patients presenting with palpable breast lumps for standard of care ultrasound examination were scanned by a trained medical student with the VSI protocol using a Butterfly iQ handheld ultrasound probe. Video clips of the VSI scan imaging were later interpreted by an attending breast imager. Results of VSI scan interpretation were compared to the same-day standard of care ultrasound examination. RESULTS: Medical students scanned 170 palpable lumps with the VSI protocol. There was 97% sensitivity and 100% specificity for a breast mass on VSI corresponding to 97.6% agreement with standard of care (Cohen's κ = 0.95, P < .0001). There was a detection rate of 100% for all cancer presenting as a sonographic mass. High agreement for mass characteristics between VSI and standard of care was observed, including 87% agreement on Breast Imaging-Reporting and Data System assessments (Cohen's κ = 0.82, P < .0001). CONCLUSIONS: Breast ultrasound VSI for palpable lumps offers a promising means to increase access to diagnostic imaging in underserved areas. This approach could decrease delay to diagnosis for breast cancer, potentially improving morbidity and mortality. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Deep Learning-Based Computer-Aided Diagnosis Model for the Identification and Classification of Mammography Images(Springer, 2023-09-01)Cancer of the breast is an illness that has the potential to be fatal for females all over the world. Even with the advancements that have been made in treatment, breast cancer cannot be prevented or cured; however, with early identification, one's life expectancy can be increased. A woman's overall health can be improved, which can add years to her life expectancy, if breast cancer is detected at an earlier stage. Radiological screening is a well-known method that is utilised for cancer prevention and detection in significant amounts. Mammograms have the ability to detect breast cancer as well as tumours that may be present in the breast. Recent study has demonstrated that DL-based CAD models can assist radiologists in establishing automated diagnosis of breast cancer in patients. The DL-based CAD model helps radiologists diagnose breast cancer automatically, according to recent research. DL techniques utilising convolutional neural network have gained interest because to their effectiveness in automating data feature representation and maximising accuracy by merging classification and feature representations. It successfully diagnoses clinical pictures. The research aims to build DL-based breast cancer diagnosis models and to review state-of-the-art ML and DL models for breast cancer diagnosis and classification. The research also examines the performance of the proposed models on the benchmark dataset. Sensitivity, specificity, accuracy, and F-measure measure performance. The experimental results showed that the proposed models are effective compared to modern methods. The proposed models are effective for breast cancer diagnosis and categorization. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, An AI-enabled comprehensive breast ultrasound diagnostic system for low-resource settings without a sonographer or a radiologist(Nature Portfolio, 2026-12-01)Breast cancer is the most common non-skin related malignancy and the leading cause of cancer death in women. Mammography remains the gold standard for early detection; however, its accessibility is limited in low-resource settings due to cost and technical complexity. Ultrasound (US) is a viable alternative, but its implementation is hindered by the scarcity of trained radiologists and sonographers. Volume sweep imaging (VSI) has addressed the issue of US acquisition by enabling non-specialists to perform standardized scans. However, these still require expert interpretation, limiting their impact. To overcome this barrier, we propose a fully automated Breast VSI (VSI-B) system integrating artificial intelligence (AI) for segmentation and classification of breast lesions, aiming to provide an accessible diagnostic tool for low-resource environments. This study developed an AI-driven diagnostic system for VSI-B, combining a segmentation model (Attention U-Net 3D) with a classification model for lesion detection. A total of 98 patients with palpable breast lumps were included in the study. The dataset consisted of 392 VSI-B US videos and 2,100 classified frames. A new method was implemented to enhance mass identification by selecting key frames for analysis. A majority voting algorithm was used to optimize lesion classification. The system's performance was assessed based on sensitivity, specificity, and accuracy. Following a detection step that achieved 100% sensitivity and 93.6% specificity for cancer and no cancer patients, as well as 95.0% sensitivity and 63.0% specificity for mass and no mass patients, the DenseNet classification model reached 87% accuracy, 100% sensitivity, and 83% specificity. A majority voting algorithm optimized classification, yielding an AUC of 0.91. This study highlights the potential of an AI-enabled VSI-B system as a reliable diagnostic tool in low-resource settings. By integrating multi-modal segmentation and classification, the system automates breast lesion detection and stratification, reducing reliance on radiologists. The results suggest that this approach could enhance early breast cancer diagnosis and guide clinical decision-making, particularly in underserved regions.1