Towards early bowel cancer detection: A data driven dynamic method of lesions characterization using a robotic capsule
Recent advances in miniaturised, dynamically actuated micro-robots have opened new possibilities for non-visual, in-situ disease diagnosis. This study introduces a method for early bowel cancer detection using a self-propelled robotic capsule autonomously navigating the bowel and identifying maligna...
Gespeichert in:
| Veröffentlicht in: | 2025 30th International Conference on Automation and Computing (ICAC) S. 1 - 6 |
|---|---|
| Hauptverfasser: | , , , |
| Format: | Tagungsbericht |
| Sprache: | Englisch |
| Veröffentlicht: |
IEEE
27.08.2025
|
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| Abstract | Recent advances in miniaturised, dynamically actuated micro-robots have opened new possibilities for non-visual, in-situ disease diagnosis. This study introduces a method for early bowel cancer detection using a self-propelled robotic capsule autonomously navigating the bowel and identifying malignant lesions based on localised variations in tissue stiffness. The approach leverages the sensitivity of the capsule's dynamic responses to biomechanical changes in the surrounding tissue. A dual-phase machine learning framework is proposed. In the first phase, the capsule's displacement signals are processed into features which are then used to predict tissue stiffness (Young's modulus, E) using multilayer perceptron (MLP), support vector regression (SVR), and Gaussian process regression (GPR). In the second phase, a Gaussian mixture model (GMM) is used to cluster the predicted E-values into discrete stiffness categories. Both accuracy and uncertainty Oriented metrics were used to evaluate the performances of the models. MLP offered the most robust regression on simulated data, while GPR outperformed it on experimental data. The GMM achieved over 89% clustering accuracy across both datasets. This study highlights a promising route for dynamic, non-visual bowel cancer detection and sets the stage for reliable deep tissue biopsy, while also addressing the current drawbacks of visual-based bowel cancer diagnostic methods. |
|---|---|
| AbstractList | Recent advances in miniaturised, dynamically actuated micro-robots have opened new possibilities for non-visual, in-situ disease diagnosis. This study introduces a method for early bowel cancer detection using a self-propelled robotic capsule autonomously navigating the bowel and identifying malignant lesions based on localised variations in tissue stiffness. The approach leverages the sensitivity of the capsule's dynamic responses to biomechanical changes in the surrounding tissue. A dual-phase machine learning framework is proposed. In the first phase, the capsule's displacement signals are processed into features which are then used to predict tissue stiffness (Young's modulus, E) using multilayer perceptron (MLP), support vector regression (SVR), and Gaussian process regression (GPR). In the second phase, a Gaussian mixture model (GMM) is used to cluster the predicted E-values into discrete stiffness categories. Both accuracy and uncertainty Oriented metrics were used to evaluate the performances of the models. MLP offered the most robust regression on simulated data, while GPR outperformed it on experimental data. The GMM achieved over 89% clustering accuracy across both datasets. This study highlights a promising route for dynamic, non-visual bowel cancer detection and sets the stage for reliable deep tissue biopsy, while also addressing the current drawbacks of visual-based bowel cancer diagnostic methods. |
| Author | Afebu, Kenneth Omokhagbo Liu, Yang Prasad, Shyam Papatheou, Evangelos |
| Author_xml | – sequence: 1 givenname: Kenneth Omokhagbo surname: Afebu fullname: Afebu, Kenneth Omokhagbo email: k.afebu@exeter.ac.uk organization: University of Exeter,Engineering Department,Exeter,UK,EX4 4QF – sequence: 2 givenname: Yang surname: Liu fullname: Liu, Yang email: y.liu2@exeter.ac.uk organization: University of Exeter,Engineering Department,Exeter,UK,EX4 4QF – sequence: 3 givenname: Evangelos surname: Papatheou fullname: Papatheou, Evangelos email: e.papatheou@exeter.ac.uk organization: University of Exeter,Engineering Department,Exeter,UK,EX4 4QF – sequence: 4 givenname: Shyam surname: Prasad fullname: Prasad, Shyam email: shyamprasad@nhs.net organization: RDUH NHS Foundation Trust,Endoscopy Department,Exeter,UK,EX2 5DW |
| BookMark | eNo1UMFKAzEUjKAHrf0DwfcDrZuk2d14K4taoeCl9_I2ebGBbVKyaUv9eiPqZQaGmWGYO3YdYiDGHnk157zST-_dsquVbPRcVEIVjeu6wBWb6ka3UnIl1ELJW3baxDMmOwJhGi7QxzMNYDAYSmApk8k-hmdYgsWMYJM_UQB7Cbj3BvaUd9FCdDDQWHwjmB0mNJmS_8KfJBxHHz4BIcU-5hIxeBiPA92zG4fDSNM_nrDN68umW83WH29l-3rmtcwz6km2LW9dtdCSC1c2U-2MrK1DarSujSbe9EIRKuUMoiJR26rlxdUK7OWEPfzWeiLaHpLfY7ps_9-Q338kXK8 |
| ContentType | Conference Proceeding |
| DBID | 6IE 6IL CBEJK RIE RIL |
| DOI | 10.1109/ICAC65379.2025.11196111 |
| DatabaseName | IEEE Electronic Library (IEL) Conference Proceedings IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume IEEE Xplore All Conference Proceedings IEEE Electronic Library (IEL) IEEE Proceedings Order Plans (POP All) 1998-Present |
| DatabaseTitleList | |
| Database_xml | – sequence: 1 dbid: RIE name: IEEE Electronic Library (IEL) url: https://ieeexplore.ieee.org/ sourceTypes: Publisher |
| DeliveryMethod | fulltext_linktorsrc |
| EISBN | 9798331525453 |
| EndPage | 6 |
| ExternalDocumentID | 11196111 |
| Genre | orig-research |
| GroupedDBID | 6IE 6IL CBEJK RIE RIL |
| ID | FETCH-LOGICAL-i93t-ebe38818f049312f254e6fc36dfae7996c9e17b25ea55fcaa5e26d081e6f82ab3 |
| IEDL.DBID | RIE |
| IngestDate | Sat Oct 25 03:16:25 EDT 2025 |
| IsPeerReviewed | false |
| IsScholarly | false |
| Language | English |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-i93t-ebe38818f049312f254e6fc36dfae7996c9e17b25ea55fcaa5e26d081e6f82ab3 |
| PageCount | 6 |
| ParticipantIDs | ieee_primary_11196111 |
| PublicationCentury | 2000 |
| PublicationDate | 2025-Aug.-27 |
| PublicationDateYYYYMMDD | 2025-08-27 |
| PublicationDate_xml | – month: 08 year: 2025 text: 2025-Aug.-27 day: 27 |
| PublicationDecade | 2020 |
| PublicationTitle | 2025 30th International Conference on Automation and Computing (ICAC) |
| PublicationTitleAbbrev | ICAC |
| PublicationYear | 2025 |
| Publisher | IEEE |
| Publisher_xml | – name: IEEE |
| Score | 1.9196302 |
| Snippet | Recent advances in miniaturised, dynamically actuated micro-robots have opened new possibilities for non-visual, in-situ disease diagnosis. This study... |
| SourceID | ieee |
| SourceType | Publisher |
| StartPage | 1 |
| SubjectTerms | Accuracy Artificial networks Biomechanical evaluation Biomechanics Bowel cancer Cancer detection Capsule robot Computational modeling Data models Early diagnosis Lesions Support vector machines Uncertainty Vibrations Visualization |
| Title | Towards early bowel cancer detection: A data driven dynamic method of lesions characterization using a robotic capsule |
| URI | https://ieeexplore.ieee.org/document/11196111 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3PS8MwFA46PHhSceJv3sFrt6VpmsbbGIqCjB2G7Dba5EUGo5Wum_--eek28eDBSyghtJCX9Mt7-b73GHvwiMItch1JkvUkMhFRlsokcsYWHt-sG4Q45PubGo-z2UxPtmL1oIVBxEA-wx49hrt8W5k1hcr6fl_qlJOS91CptBVrbTlbfKD7r6PhKJVCkf4klr3d6F91UwJsPJ_884OnrPsjwIPJHlrO2AGW52wzDRTXFSAlJYai-sIlGLJaDRabwKkqH2EIxPoEW9N_DGxbcR7aStFQOVgiBchWYPapmlslJhAF_gNyqKui8svJv9p70Evssunz03T0Em3LJkQLLZrIW0VkHoadP_sLHjvvAWLqjEity1F598Zo5KqIJeZSOpPnEuPU-pOBH5XFeSEuWKesSrxkIK2ke0ehjPVudCG1Q8tFYrTwjTR4xbo0Z_PPNjHGfDdd13_037BjsgyFZGN1yzpNvcY7dmQ2zWJV3wdzfgPCPKQ1 |
| linkProvider | IEEE |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3PS8MwFA4yBT2pOPG37-C129o0beNtDMeGc-xQZLfRJi8yGKt03fz3fUm3iQcPXkIJoYW8pF_ey_e9x9gTIYqv0ZeesLKeUITcSyIRekbpnPBNm46LQ76P4vE4mU7lZCtWd1oYRHTkM2zZR3eXrwu1tqGyNu1LGflWyXsowjDo1HKtLWvL78j2sNftRYLHVoESiNZu_K_KKQ44-qf__OQZa_5I8GCyB5dzdoDLC7ZJHcl1BWjTEkNefOEClLVbCRorx6paPkMXLO8TdGn_ZKDrmvNQ14qGwsACbYhsBWqfrLnWYoIlwX9ABmWRF7Sg6NXkQy-wydL-S9obeNvCCd5c8soju_CEgNjQ6Z_7gSEfECOjeKRNhjE5OEqiH-eBwEwIo7JMYBBpOhvQqCTIcn7JGstiiVcMhBb25pHHSpMjnQtpUPs8VJJTIxRes6ads9lnnRpjtpuumz_6H9nxIH0bzUbD8estO7FWsgHaIL5jjapc4z07UptqviofnGm_AXXep3w |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Abook&rft.genre=proceeding&rft.title=2025+30th+International+Conference+on+Automation+and+Computing+%28ICAC%29&rft.atitle=Towards+early+bowel+cancer+detection%3A+A+data+driven+dynamic+method+of+lesions+characterization+using+a+robotic+capsule&rft.au=Afebu%2C+Kenneth+Omokhagbo&rft.au=Liu%2C+Yang&rft.au=Papatheou%2C+Evangelos&rft.au=Prasad%2C+Shyam&rft.date=2025-08-27&rft.pub=IEEE&rft.spage=1&rft.epage=6&rft_id=info:doi/10.1109%2FICAC65379.2025.11196111&rft.externalDocID=11196111 |