OxyForce - Oxygen and Force Sensing Solution for Early Diagnosis of Breast Cancer

Breast cancer is the most common cancer among women worldwide, and early detection is critical for improving patient outcomes. However, current screening methods (e.g. mammography or ultrasound) have limitations in terms of accessibility, cost, accuracy, and reliability. Measuring oxygen saturation levels in cancerous tissue can provide valuable information about the tumour microenvironment. To address this hypothesis, we propose a novel approach that combines multiple technologies inspired by the metabolic and mechanical properties of the tumour: oxygen saturation sensing, force sensing, and artificial intelligence (AI). This proposal aims to develop a new diagnostic tool that can detect tumours at an early stage with high sensitivity and reliability, while also providing a non-invasive, user-friendly, and cost-effective solution for breast cancer screening. Ultimately, incorporating these technologies in breast cancer detection has the potential to improve patient outcomes, screening frequency and mortality rate associated with breast cancer.

Oxygen saturation sensing is a non-invasive tool that measures the percentage of haemoglobin in the blood that is bound to oxygen. Tumors have a higher oxygen consumption rate compared to surrounding normal tissue, therefore, low oxygen saturation levels have been associated with the progression of the tumor cells. Several studies have demonstrated that hypoxia (low oxygen levels) is a common feature of tumours and is associated with a more aggressive phenotype and increased risk of metastasis. Measuring oxygen saturation levels in breast tissue can provide valuable information about the tumour microenvironment, including its metabolic activity and angiogenesis. Therefore, oxygen saturation levels can be used as a biomarker to distinguish between malignant and benign breast lesions. Moreover, integrating force sensing technology into oxygen saturation sensing can be beneficial to improve accuracy and reliability, as it is a necessary first step for physical examination. In this context,  by combining oxygen saturation and force information, in conjunction with a deep learning algorithm,  we aim to develop a non-invasive, accurate, and cost-effective device for early diagnosis of breast cancer.

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