Deep learning methods for point-of-care ultrasound examination

Ignesti G., Deri C., D'Angelo G., Pratali L., Bruno A., Benassi A., Salvetti O., Moroni D., Martinelli M.

Point-of-care testing  Ultrasound  Telemedicine  Multi-pathology  Artificial Intelligence  Explainable Artificial In telligence  Optical Character Recognition  Machine Learning  Decision Support System 

Point-of-care Test (POCT) is the delivery of medical care at or near the patient's bedside. Primarily employed in emergencies, where rapid diagnosis and treatment are critical, POCT is now being used in domestic telehealth solutions, as in the TiAssisto project, thanks to technological advances such as the development of portable and affordable devices, high-speed Internet connections, video conferencing, and Artificial Intelligence (AI). Ultrasound (US) images of internal organs and structures are valuable tools in POCT medicine since this examination is portable, quick, and cost-effective. USs can help diagnose different conditions, including heart problems, abdominal pain, and pneumonia. Deep learning algorithms have proven to be highly effective in image recognition, enabling physicians to make informed decisions on-site. This article presents and investigates a decision support system based on deep learning algorithms. The primary aim of this research is to detect various signs in US images using cutting-edge classification methods. The proposed pipeline initially adopts an optical character recognition (OCR) algorithm: this technique inspects and cleans the US image, ensuring privacy and better classification potential. The collected images are forwarded to a state-of-the-art (SOTA) deep learning network, a fine-tuned EfficientNET-b0, able to detect any signs potentially related to pathology in a rapid way. The network classification is then assessed in the pipeline using a visual explanation method, i.e. Grad-CAM, to evaluate if the proper medical signs were identified, offering a quick and effective second opinion. The involved physician's feedback remarks that this system can detect important signs in pulmonary US imaging, although the dataset is not yet the final one since the TiAssisto project is still ongoing, with a planned conclusion in February 2024. Our ultimate goal is not merely to develop a classification system but to create an effective healthcare support system that can be used beyond primary healthcare facilities.

Source: SITIS 2023 - 17th International Conference on Signal-Image Technology & Internet-Based Systems, pp. 436–441, Bangkok, Thailand, 8-10/11/2023

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