Patent ID: 11931208
Assignee: BEIJING FRIENDSHIP HOSPITAL, CAPITAL MEDICAL UNIVERSITY
Field: Medical technology (Instruments)
Classification: CPC A  G | IPC A  G

Claim 7:
8. A non-transitory computer storage medium, configured to store a computer program that, when executed on a computer, performs the following method:
acquiring cervical blood flow data from an ultrasound data collecting device;
determining cerebral perfusion data corresponding to the cervical blood flow data based on the cervical blood flow data and a mapping relationship between the cervical blood flow data and the cerebral perfusion data; and
classifying cerebral perfusion states of a plurality of brain regions based on blood perfusion characteristics of the plurality of brain regions in the cerebral perfusion data, wherein classifying cerebral perfusion states of a plurality of brain regions based on blood perfusion characteristics of the plurality of brain regions in the cerebral perfusion data comprises extracting blood perfusion characteristics of a plurality of brain regions from the cerebral perfusion data; and
determining a cerebral perfusion state type to which each of the plurality of brain regions belongs based on the blood perfusion characteristics and blood perfusion characteristic thresholds corresponding to cerebral perfusion state types; and
wherein when determining cerebral perfusion data corresponding to the cervical blood flow data based on the cervical blood flow data and a mapping relationship between the cervical blood flow data and the cerebral perfusion data, the method further comprises:
extracting cervical blood flow characteristics from the cervical blood flow data; and
inputting the cervical blood flow characteristics into a pre-trained network model to obtain cerebral perfusion data corresponding to the cervical blood flow characteristics,
wherein the network model is trained based on cervical blood flow characteristic samples and cerebral perfusion data samples;
wherein the network model comprises: a Seq2Seq model comprising an encoder and a decoder constructed based on long short-term memory (LSTM);
the network model further comprises: a forget gate (ft), an input gate (it), and an output gate (ot);
the forget gate (ft) is realized as: ft=σ(wf·ht-1+uf·xt+bf); xt is a current input, ht-1 is a previous output,
the input gate (it) is realized as: it=σ(wi·ht-1+ui·xt+bi), {tilde over (c)}t=tan h(wc·ht-1+uc·xt+bc), and ct=ft⊙Ct+it⊙{tilde over (C)}t; Ct is a current cell state, {tilde over (C)}t is a new information; and
the output gate ot is realized as: ot=σ(wo·ht-1+uo·xt+bo), and ht=ot tan h⊙(Ct).