Towards non-visual bowel cancer diagnosis: A certainty-aware data-driven method of lesion characterisation using a vibrating capsule

Recent advances in miniaturised, dynamically actuated robots have opened new pathways for non-visual, in-situ disease diagnosis. This study explores a novel method for early bowel cancer detection using a self-propelled robotic capsule that navigates the bowel and detects lesions based on variations in tissue stiffness. The approach capitalises on the sensitivity of the capsule’s dynamic responses to surrounding tissue properties. A dual-phase machine learning framework is proposed. The first phase uses regression models including multilayer perceptron (MLP), support vector regression (SVR), and Gaussian process regression (GPR) to predict tissue stiffness from displacement signal features. The second phase uses a Gaussian mixture model (GMM) to cluster the predicted stiffness values into different categories. Unlike our previous work, this study emphasises the robustness of the models under varying data conditions using both accuracy and reliability-oriented metrics. Based on our studies, MLP provided the most reliable regression results for simulated data and downstream clustering, though GPR performed better on experimental datasets. SVR consistently underperformed, especially on experimental data. The GMM achieved over 89% clustering accuracy across both simulated and experimental datasets, with improved results when predictions from more accurate regression models are used as the inputs. This work demonstrates a promising step toward dynamic, in-situ lesion characterisation and highlights the potential for integrating lesion biomechanics into future endoscopic diagnosis.