An endoscopic tactile robotic capsule, embedding miniaturized MEMS force sensors, is presented. The capsule is conceived to provide automatic palpation of non-polypoid colorectal tumours during colonoscopy, since it is characterized by high degree of dysplasia, higher invasiveness and lower detection rates with respect to polyps. A first test was performed employing a silicone phantom that embedded inclusions with variable hardness and curvature. A hardness-based classification was implemented, demonstrating detection robustness to curvature variation. By comparing a set of supervised classification algorithms, a weighted 3-nearest neighbor classifier was selected. A bias force normalization model was introduced in order to make different acquisition sets consistent. Parameters of this model were chosen through a particle swarm optimization method. Additionally, an ex-vivo test was performed to assess the capsule detection performance when magnetically-driven along a colonic tissue. Lumps were identified as voltage peaks with a prominence depending on the total magnetic force applied to the capsule. Accuracy of 94 % in hardness classification was achieved, while a 100 % accuracy is obtained for the lump detection within a tolerance of 5 mm from the central path described by the capsule. In real application scenario, we foresee our device aiding physicians to detect tumorous tissues.

Camboni, D., Massari, L., Chiurazzi, M., Calio, R., Alcaide, J., Drabbraccio, J., et al. (2021). Endoscopic tactile capsule for non-polypoid colorectal tumour detection. IEEE TRANSACTIONS ON MEDICAL ROBOTICS AND BIONICS, 3(1), 64-73 [10.1109/TMRB.2020.3037255].

Endoscopic tactile capsule for non-polypoid colorectal tumour detection

Carrozza, Maria Chiara;
2021

Abstract

An endoscopic tactile robotic capsule, embedding miniaturized MEMS force sensors, is presented. The capsule is conceived to provide automatic palpation of non-polypoid colorectal tumours during colonoscopy, since it is characterized by high degree of dysplasia, higher invasiveness and lower detection rates with respect to polyps. A first test was performed employing a silicone phantom that embedded inclusions with variable hardness and curvature. A hardness-based classification was implemented, demonstrating detection robustness to curvature variation. By comparing a set of supervised classification algorithms, a weighted 3-nearest neighbor classifier was selected. A bias force normalization model was introduced in order to make different acquisition sets consistent. Parameters of this model were chosen through a particle swarm optimization method. Additionally, an ex-vivo test was performed to assess the capsule detection performance when magnetically-driven along a colonic tissue. Lumps were identified as voltage peaks with a prominence depending on the total magnetic force applied to the capsule. Accuracy of 94 % in hardness classification was achieved, while a 100 % accuracy is obtained for the lump detection within a tolerance of 5 mm from the central path described by the capsule. In real application scenario, we foresee our device aiding physicians to detect tumorous tissues.
Articolo in rivista - Articolo scientifico
Abnormal tissue localization; ex-vivo phantom; MEMS tactile sensors; particle swarm optimization; robotic endoscopic capsule; robotic tissue palpation of non-polypoid tumours;
English
10-nov-2020
2021
3
1
64
73
reserved
Camboni, D., Massari, L., Chiurazzi, M., Calio, R., Alcaide, J., Drabbraccio, J., et al. (2021). Endoscopic tactile capsule for non-polypoid colorectal tumour detection. IEEE TRANSACTIONS ON MEDICAL ROBOTICS AND BIONICS, 3(1), 64-73 [10.1109/TMRB.2020.3037255].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/521711
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