SCAR-LIGHT MANIPULATOR
    22.
    发明公开

    公开(公告)号:EP4385431A1

    公开(公告)日:2024-06-19

    申请号:EP22213099.9

    申请日:2022-12-13

    摘要: The invention relates to a uterine transillumination device (1) for detecting scars on an uterus (100), particularly a human uterus, the device comprising at least the following components:
    a longitudinal body (1B) extending along a longitudinal axis (z), the longitudinal body (1B) having at least a tip portion (10) for being introduced in the cervix and the uterus cavity, wherein the longitudinal body (1B) further comprises a sheath portion (20) for the tip portion (10), wherein the tip portion (10)
    a) is movable along the longitudinal axis (z) inward and outward the sheath portion (20),
    b) comprises a first side (10-1) facing along a first radial direction (r1) away from the longitudinal axis (z) and a second side (10-2) facing toward the opposite direction of the first radial direction (r1),
    c) comprises an illumination assembly (30) arranged on the first side (10-1) of the tip portion (10),
    wherein the illumination assembly (30) is configured to emit light radially away from the longitudinal axis (z) along the first radial direction (r1).

    METHOD, SYSTEM AND COMPUTER PROGRAM FOR COMPUTATION OF OPTIMAL INDIVIDUAL DOSING REGIMEN, PARTICULARLY SUBJECT TO CLINICAL CONSTRAINTS

    公开(公告)号:EP4177902A8

    公开(公告)日:2024-05-29

    申请号:EP21206570.0

    申请日:2021-11-04

    IPC分类号: G16H20/10 G16H50/50

    CPC分类号: G16H50/50 G16H20/10

    摘要: The present invention relates to a method for automatically determining an optimal individual dosing regimen of at least one drug for a patient suffering from a known disease, the optimal individual dosing regimen being optionally subject to at least one clinical constraint, wherein the method comprises the steps of: providing a mathematical model adapted to model a progression of said disease and an effect of the at least one drug on the progression of the disease, the model comprising individual model parameters associated with the patient; utilizing an empirical Bayesian estimation to automatically and numerically estimate the individual model parameters of the mathematical model utilizing patient data associated with the patient; automatically calculating an optimal individual dosing regimen for the mathematical model by solving an optimal control problem based on a desired progression of the disease, the estimated individual model parameters, and an initial guess for the dosing regimen; and adjusting the optimal individual dosing regimen to optionally account for at least one clinical constraint to yield the optimal individual dosing regimen subject to said at least one clinical constraint.