A Reliable and Sensitive Framework for Simultaneous Type and Stage Detection of Colorectal Cancer Polyps

With the goal of enhancing the early diagnosis of colorectal cancer (CRC) polyps and reducing the risk of mortality in cancer patients, in this article, we present a unique diagnosis framework including a Vision-based Surface Tactile Sensor (VS-TS) and complementary Artificial Intelligence algorithms. Leveraging the morphological characteristics (i.e., shape and texture) and stiffness features of the CRC polyps, the proposed framework is able to reliably and sensitively identify their type and stage. To thoroughly characterize and identify the required VS-TS sensitivity for reliable identification of polyps, we first fabricated three different VS-TSs and qualitatively evaluated their performances on 48 different types of polyp phantoms fabricated based on four different types of realistic CRC polyps and three different materials. Next, to quantitatively compare the performance and sensitivity of the fabricated VS-TSs, we used Support Vector Machine (SVM) algorithm and employed various statistical metrics (i.e., accuracy, reliability, and sensitivity). Next, using the most sensitive VS-TS, we classified the type of tumors using the SVM algorithm and applied the t-Distributed Stochastic Neighbor Embedding algorithm to successfully identify the stiffness of classified polyp phantoms solely based on the output images of the VS-TS sensor. Results demonstrated that an SVM algorithm applied on the image outputs of a VS-TS with a Shore hardness of 00–40 scale is able to classify all types of polyps with > 90% accuracy, sensitivity, and reliability. We also repeated experiments on samples of ex-vivo lamb tripe tissues and successfully verified the high sensitivity and reliability of the proposed framework (i.e., > 94%).