Assessing the feasibility of AI-enhanced portable ultrasound for improved early detection of breast cancer in remote areas

The objective of our study was to explore the feasibility of integrating artificial intelligence (AI) algorithms for breast cancer detection into a portable, point-of-care ultrasound device (POCUS). This proof-of-concept implementation is to demonstrate the platform for integrating AI algorithms into a POCUS device to achieve a performance benchmark of at least 15 frames/second. Our methodology involved the application of five AI models (FasterRCNN+MobileNetV3, FasterRCNN+ResNet50, RetinaNet+ResNet50, SSD300+VGG16, and SSDLite320+MobileNetV3), pretrained on public datasets of natural images, fine-tuned using a dataset of gelatin-based breast phantom images with both anechoic and hyperechoic lesions, mimicking real tissue characteristics. We created various gelatin-based ultrasound phantoms containing ten simulated lesions, ranging from 4-20 mm in size. Our experimental setup used the Clarius L15 scanning probe, which was connected via Wi-Fi to both a tablet and a laptop, forming the core of our development platform. The phantom data was divided into training, validation, and held-out testing sets on a per-video basis. We executed 200 timing trials for each finetuned AI model, streaming scanning video from the ultrasound probe in real-time. SSDLite320+MobileNetV3 emerged as a standout, showing a mean frame-to-frame timing of 0.068 seconds (SD=0.005), which is approximately 14.71 FPS, closely followed by FasterRCNN+MobileNetV3, with a mean timing of 0.123 seconds (SD=0.016), or about 8.13 FPS. Both models show acceptable performance in lesion localization. Compared to our goal of 15 frames/second, only the SSDLite320+MobileNetV3 architecture performed with sufficient evaluation speed to be used in real-time. Our findings show the necessity of using AI architectures designed for edge devices for real-time use, as well as the potential need for hardware acceleration to encode AI models for use in POCUS.