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R. Mes
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AI-Assisted Point-of-Care Ultrasound for Breast Lesion Detection
Feasibility of a Standardized Blind-Sweep Workflow and Deep Learning-Based Lesion Detection
Introduction: Breast cancer remains one of the most common cancers among women worldwide, and early detection is essential for improving clinical outcomes. Patients presenting with breast complaints in primary care are often referred to hospital-based breast imaging because clinical assessment alone cannot reliably exclude malignancy. However, many referred patients are ultimately found to have benign findings or no focal abnormality. Point-of-care ultrasound (POCUS), particularly when combined with artificial intelligence (AI), may provide an accessible first-line assessment tool to support referral decisions before specialist breast imaging.
Aim: This thesis investigated the feasibility of an AI-assisted POCUS workflow for breast lesion detection using prospectively acquired blind-sweep ultrasound data. The objectives were as follows: to evaluate whether standardized breast POCUS blind sweeps could be acquired by a non-expert operator within an existing clinical diagnostic workflow, and to assess the performance of an AI model for breast lesion detection in the resulting image sequences.
Method: Prospective POCUS data were acquired from 86 participants referred for breast diagnostic assessment. Using a POCUS probe, a non-expert operator performed standardized partly overlapping blind sweeps of the selected breast. In total, 833 sweeps comprising more than 185,000 frames were acquired. The data were systematically stored, processed, and retrospectively annotated using the clinical imaging findings as reference. Public breast ultrasound datasets and POCUS breast phantom acquisitions were additionally used during model development. A You Only Look Once (YOLO)-based object detection model was trained to localize breast lesions, and performance was evaluated on independently held-out POCUS study, public, and phantom test data. Temporal post-processing was applied to retain detections supported by spatially corresponding predictions in nearby frames within the same sweep.
Results: Standardized breast POCUS blind-sweep acquisition was successfully integrated into the clinical workflow, with whole-breast acquisition according to protocol completed in 85 of 86 participants. On the prospective POCUS test set comprising seven participants, the final model achieved a framelevel sensitivity of 79.0% and specificity of 98.7% after temporal post-processing. At sweep level, all 16 lesion-positive sweeps contained at least one correct detection, while 27 of 28 lesion observations were detected in at least one frame. Performance was higher on public and phantom ultrasound data than on prospectively acquired POCUS data, highlighting the greater variability and complexity of non-targeted blind-sweep imaging. Nevertheless, the results demonstrate that automated lesion localization within prospectively acquired blind sweeps is feasible.
Conclusion: This thesis provides proof of concept for combining standardized non-expert breast POCUS blind-sweep acquisition with automated lesion detection in a clinical setting. The findings support further development of AI-assisted POCUS blind sweeps as a potential breast assessment or triage approach, while emphasizing that acquisition quality is an essential prerequisite for successful downstream analysis. Future work should focus on a larger prospective dataset, validation across multiple operators and centers, automated acquisition quality control, improved use of temporal sweep information, and extension of the pipeline towards lesion segmentation and benign–malignant classification. ...
Aim: This thesis investigated the feasibility of an AI-assisted POCUS workflow for breast lesion detection using prospectively acquired blind-sweep ultrasound data. The objectives were as follows: to evaluate whether standardized breast POCUS blind sweeps could be acquired by a non-expert operator within an existing clinical diagnostic workflow, and to assess the performance of an AI model for breast lesion detection in the resulting image sequences.
Method: Prospective POCUS data were acquired from 86 participants referred for breast diagnostic assessment. Using a POCUS probe, a non-expert operator performed standardized partly overlapping blind sweeps of the selected breast. In total, 833 sweeps comprising more than 185,000 frames were acquired. The data were systematically stored, processed, and retrospectively annotated using the clinical imaging findings as reference. Public breast ultrasound datasets and POCUS breast phantom acquisitions were additionally used during model development. A You Only Look Once (YOLO)-based object detection model was trained to localize breast lesions, and performance was evaluated on independently held-out POCUS study, public, and phantom test data. Temporal post-processing was applied to retain detections supported by spatially corresponding predictions in nearby frames within the same sweep.
Results: Standardized breast POCUS blind-sweep acquisition was successfully integrated into the clinical workflow, with whole-breast acquisition according to protocol completed in 85 of 86 participants. On the prospective POCUS test set comprising seven participants, the final model achieved a framelevel sensitivity of 79.0% and specificity of 98.7% after temporal post-processing. At sweep level, all 16 lesion-positive sweeps contained at least one correct detection, while 27 of 28 lesion observations were detected in at least one frame. Performance was higher on public and phantom ultrasound data than on prospectively acquired POCUS data, highlighting the greater variability and complexity of non-targeted blind-sweep imaging. Nevertheless, the results demonstrate that automated lesion localization within prospectively acquired blind sweeps is feasible.
Conclusion: This thesis provides proof of concept for combining standardized non-expert breast POCUS blind-sweep acquisition with automated lesion detection in a clinical setting. The findings support further development of AI-assisted POCUS blind sweeps as a potential breast assessment or triage approach, while emphasizing that acquisition quality is an essential prerequisite for successful downstream analysis. Future work should focus on a larger prospective dataset, validation across multiple operators and centers, automated acquisition quality control, improved use of temporal sweep information, and extension of the pipeline towards lesion segmentation and benign–malignant classification. ...
Introduction: Breast cancer remains one of the most common cancers among women worldwide, and early detection is essential for improving clinical outcomes. Patients presenting with breast complaints in primary care are often referred to hospital-based breast imaging because clinical assessment alone cannot reliably exclude malignancy. However, many referred patients are ultimately found to have benign findings or no focal abnormality. Point-of-care ultrasound (POCUS), particularly when combined with artificial intelligence (AI), may provide an accessible first-line assessment tool to support referral decisions before specialist breast imaging.
Aim: This thesis investigated the feasibility of an AI-assisted POCUS workflow for breast lesion detection using prospectively acquired blind-sweep ultrasound data. The objectives were as follows: to evaluate whether standardized breast POCUS blind sweeps could be acquired by a non-expert operator within an existing clinical diagnostic workflow, and to assess the performance of an AI model for breast lesion detection in the resulting image sequences.
Method: Prospective POCUS data were acquired from 86 participants referred for breast diagnostic assessment. Using a POCUS probe, a non-expert operator performed standardized partly overlapping blind sweeps of the selected breast. In total, 833 sweeps comprising more than 185,000 frames were acquired. The data were systematically stored, processed, and retrospectively annotated using the clinical imaging findings as reference. Public breast ultrasound datasets and POCUS breast phantom acquisitions were additionally used during model development. A You Only Look Once (YOLO)-based object detection model was trained to localize breast lesions, and performance was evaluated on independently held-out POCUS study, public, and phantom test data. Temporal post-processing was applied to retain detections supported by spatially corresponding predictions in nearby frames within the same sweep.
Results: Standardized breast POCUS blind-sweep acquisition was successfully integrated into the clinical workflow, with whole-breast acquisition according to protocol completed in 85 of 86 participants. On the prospective POCUS test set comprising seven participants, the final model achieved a framelevel sensitivity of 79.0% and specificity of 98.7% after temporal post-processing. At sweep level, all 16 lesion-positive sweeps contained at least one correct detection, while 27 of 28 lesion observations were detected in at least one frame. Performance was higher on public and phantom ultrasound data than on prospectively acquired POCUS data, highlighting the greater variability and complexity of non-targeted blind-sweep imaging. Nevertheless, the results demonstrate that automated lesion localization within prospectively acquired blind sweeps is feasible.
Conclusion: This thesis provides proof of concept for combining standardized non-expert breast POCUS blind-sweep acquisition with automated lesion detection in a clinical setting. The findings support further development of AI-assisted POCUS blind sweeps as a potential breast assessment or triage approach, while emphasizing that acquisition quality is an essential prerequisite for successful downstream analysis. Future work should focus on a larger prospective dataset, validation across multiple operators and centers, automated acquisition quality control, improved use of temporal sweep information, and extension of the pipeline towards lesion segmentation and benign–malignant classification.
Aim: This thesis investigated the feasibility of an AI-assisted POCUS workflow for breast lesion detection using prospectively acquired blind-sweep ultrasound data. The objectives were as follows: to evaluate whether standardized breast POCUS blind sweeps could be acquired by a non-expert operator within an existing clinical diagnostic workflow, and to assess the performance of an AI model for breast lesion detection in the resulting image sequences.
Method: Prospective POCUS data were acquired from 86 participants referred for breast diagnostic assessment. Using a POCUS probe, a non-expert operator performed standardized partly overlapping blind sweeps of the selected breast. In total, 833 sweeps comprising more than 185,000 frames were acquired. The data were systematically stored, processed, and retrospectively annotated using the clinical imaging findings as reference. Public breast ultrasound datasets and POCUS breast phantom acquisitions were additionally used during model development. A You Only Look Once (YOLO)-based object detection model was trained to localize breast lesions, and performance was evaluated on independently held-out POCUS study, public, and phantom test data. Temporal post-processing was applied to retain detections supported by spatially corresponding predictions in nearby frames within the same sweep.
Results: Standardized breast POCUS blind-sweep acquisition was successfully integrated into the clinical workflow, with whole-breast acquisition according to protocol completed in 85 of 86 participants. On the prospective POCUS test set comprising seven participants, the final model achieved a framelevel sensitivity of 79.0% and specificity of 98.7% after temporal post-processing. At sweep level, all 16 lesion-positive sweeps contained at least one correct detection, while 27 of 28 lesion observations were detected in at least one frame. Performance was higher on public and phantom ultrasound data than on prospectively acquired POCUS data, highlighting the greater variability and complexity of non-targeted blind-sweep imaging. Nevertheless, the results demonstrate that automated lesion localization within prospectively acquired blind sweeps is feasible.
Conclusion: This thesis provides proof of concept for combining standardized non-expert breast POCUS blind-sweep acquisition with automated lesion detection in a clinical setting. The findings support further development of AI-assisted POCUS blind sweeps as a potential breast assessment or triage approach, while emphasizing that acquisition quality is an essential prerequisite for successful downstream analysis. Future work should focus on a larger prospective dataset, validation across multiple operators and centers, automated acquisition quality control, improved use of temporal sweep information, and extension of the pipeline towards lesion segmentation and benign–malignant classification.