Computer implemented method for training a multilayer neural network in view of obtaining an irradiation map of an intervention area of a patient during interventional radiology using an interventional radiology device , said method comprising: - a learning phase consisting in submitting to the multilayer neural network a learning set comprising first input tensors associated with labels, each of said first input tensors comprising data of a first 3D radiology image of a patient in the intervention area and data of first acquisition parameters of the interventional radiology device, said first acquisition parameters comprising three-axis coordinates (x,y,z) of the interventional radiology device, angles, α, β, of orientation of the device, and voltage (KV) of a tube of the interventional radiology device, α representing rotation about a longitudinal axis of the patient, β a rotation about an horizontal axis, each of said labels consisting of an irradiation map obtained by simulation by a simulation module from the said first 3D radiology image and the said first acquisition parameters , said irradiation map comprising a 3D image of the intervention area, said 3D image comprising voxels associated with a simulated irradiation dose per unit volume of the intervention area, a predictive model specific to the intervention area being constituted, - storing the predictive model, for use by said trained multilayer neural network for obtaining as output an irradiation map prediction of said given patient in said intervention area during interventional radiology, in response to submitting to said neural network a second input tensor, said second input tensor comprising data of a second 3D radiology image of said patient in the intervention area, and data of second acquisition parameters acquired during the interventional radiology from the interventional radiology device , said second 3D radiology image and said second acquisition parameters being of a same type as those used in the learning phase, said irradiation map prediction being of a same type as the labels of the learning set. - a learning phase consisting in submitting to the multilayer neural network a learning set comprising first input tensors associated with labels, each of said first input tensors comprising data of a first 3D radiology image of a patient in the intervention area and data of first acquisition parameters of the interventional radiology device, said first acquisition parameters comprising three-axis coordinates (x,y,z) of the interventional radiology device, angles, α, β, of orientation of the device, and voltage (KV) of a tube of the interventional radiology device, α representing rotation about a longitudinal axis of the patient, β a rotation about an horizontal axis, each of said labels consisting of an irradiation map obtained by simulation by a simulation module from the said first 3D radiology image and the said first acquisition parameters , said irradiation map comprising a 3D image of the intervention area, said 3D image comprising voxels associated with a simulated irradiation dose per unit volume of the intervention area, a predictive model specific to the intervention area being constituted, - storing the predictive model, for use by said trained multilayer neural network for obtaining as output an irradiation map prediction of said given patient in said intervention area during interventional radiology, in response to submitting to said neural network a second input tensor, said second input tensor comprising data of a second 3D radiology image of said patient in the intervention area, and data of second acquisition parameters acquired during the interventional radiology from the interventional radiology device , said second 3D radiology image and said second acquisition parameters being of a same type as those used in the learning phase, said irradiation map prediction being of a same type as the labels of the learning set. <EOS>