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Guiding measurement protocols of connected medical devices using digital twins: A statistical methodology applied to detecting and monitoring lymphedema

Abstract : This article describes the preparation of a clinical study on a connected medical device dedicated to facilitate the prevention of lymphedema for patients treated for breast cancer after axillary lymph node dissection (ALND), and to alert them of any manifestation or resurgence in order to prevent its aggravation. A simulator of the entire physical process called digital twin has been developed for this purpose, in a hardware-in-the-loop framework. Statistical calibration of its input parameters, by stochastic inversion and using sensitivity studies, led to establish one or more measurement protocols allowing to capture the signal on a mobile device (phone or tablet) and to detect signal breaks that are physically signicant. The measured signal makes it possible to report quickly on the worsening of the patient's condition and to warn the therapists within a very reasonable period of time. The general methodology of this work seems to us to be easily reproducible in the preparation of clinical studies of other types for connected devices, which aim to develop measurement protocols limiting the often signicant cost of such studies. One of the immediate prospects of this work is the initiation of a clinical study on different patients who have been treated by surgery for breast cancer, after improving the robustness of the design of the prototype to take into account the uncertainties affecting the measurements.
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https://www.hal.inserm.fr/inserm-03167145
Contributor : Nicolas Bousquet <>
Submitted on : Thursday, March 11, 2021 - 6:13:56 PM
Last modification on : Thursday, April 15, 2021 - 3:08:12 PM

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Loic Bethencourt, Walid Dabachine, Vincent Dejouy, Zakaria Lalmiche, Karl Neuberger, et al.. Guiding measurement protocols of connected medical devices using digital twins: A statistical methodology applied to detecting and monitoring lymphedema. IEEE Access, IEEE, 2021, Online ahead of print. ⟨10.1109/ACCESS.2021.3063786⟩. ⟨inserm-03167145⟩

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