In this study, we introduce a simulation-based modeling framework for the optimal design of our recently developed inflatable endoscopic vision-based tactile sensing balloon (E-VTSB). Of note, E-VTSB is designed for providing a safe and high-resolution textural mapping and morphology characterization of colorectal cancer (CRC) polyps to enhance the early diagnosis of cancerous polyps. Leveraging the Simulation Open Framework Architecture (SOFA) software and by performing complementary experimental validation, we thoroughly analyzed and investigated the impact of the elastic modulus of the material constitution of E-VTSB on its deformation behavior under different applied pressures. Our findings revealed a close correlation between the simulated outcomes and experimental data performed on two different E-VTSBs. In particular, with the maximum absolute deformation error of <12%, our results clearly validated the proposed framework’s accuracy in predicting the E-VTSB’s deformation trend and its potential use for optimizing the design parameters.

In this paper, with the goal of enhancing the safety of current colonoscopic procedures and providing the pressure and location of the contact between the colonoscope and the colon’s surface, we propose design and development of a unique Soft Pressure Sensing Sleeve (SPSS). SPSS can seamlessly be integrated with the existing colonoscopic devices and would not change the existing diagnosis workflow. The pressure sensing of SPSS is performed based on the resistance change of a liquid metal (i.e., Gallium) embedded into several micro-channels located within SPSS’s deformable sleeve when it interacts with the colon surface. To demonstrate functionality of the SPSS, without loss of generality, in this paper, we designed and fabricated a SPSS with 4 sensing regions. We also proposed and experimentally evaluated an empirical calibration function for this sensor. Results demonstrate high accuracy (RMSE=2.45 and mean absolute error <3%) of the proposed calibration function compared with the evaluation experiments.

In this paper, with the goal of addressing the high early-detection miss rate of colorectal cancer (CRC) polyps during a colonoscopy procedure, we propose the design and fabrication of a unique inflatable vision-based tactile sensing balloon (VTSB). The proposed soft VTSB can readily be integrated with the existing colonoscopes and provide a radiation-free, safe, and high-resolution textural mapping and morphology characterization of CRC polyps. The performance of the proposed VTSB has been thoroughly characterized and evaluated on four different types of additively manufactured CRC polyp phantoms with three different stiffness levels. Additionally, we integrated the VTSB with a colonoscope and successfully performed a simulated colonoscopic procedure inside a tube with a few CRC polyp phantoms attached to its internal surface.

This paper proposes a smart handheld textural sensing medical device with complementary Machine Learning (ML) algorithms to enable on-site Colorectal Cancer (CRC) polyp diagnosis and pathology of excised tumors. The proposed unique handheld edge device benefits from a unique tactile sensing module and a dual-stage machine learning algorithms (composed of a dilated residual network and a t-SNE engine) for polyp type and stiffness characterization. Solely utilizing the occlusion-free, illumination-resilient textural images captured by the proposed tactile sensor, the framework is able to sensitively and reliably identify the type and stage of CRC polyps by classifying their texture and stiffness, respectively. Moreover, the proposed handheld medical edge device benefits from internet connectivity for enabling remote digital pathology (boosting the diagnosis in operating rooms and promoting accessibility and equity in medical diagnosis).

This study proposes a novel diagnosis framework to decrease the early detection miss rate of colorectal cancer (CRC) polyps by using a hypersensitive vision-based tactile sensor (HySenSe) and a deep residual neural network. The HySenSe generates high-resolution 3D textural images of 160 realistic polyp phantoms for accurate classification via the proposed deep learning (DL) architecture. The DL module explores lightweight dilated convolutions, residual neural network architecture, and transfer learning to overcome the challenge of a small dataset of 229 images. Results show that the proposed architecture outperforms state-of-the-art DL models (i.e., EfficientNet and DenseNet) with a 94% accuracy, offering a promising solution for improving early detection of CRC polyps. The proposed framework can be used as a diagnostic module within tele-assessment medical robots, highlighting the potential of advanced technology and deep learning to revolutionize the early detection and treatment of CRC.