Cerebral apoplexy diagnosis device and method based on artificial intelligence
A technology of artificial intelligence and diagnostic devices, which is applied to radiological diagnostic instruments, diagnostics, computerized tomography scanners, etc., and can solve problems such as differences, differences in diagnostic results, and high probability of errors
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no. 1 example
[0279] Figure 13 To illustrate it can be Figure 5 A flowchart of an embodiment of screening patients with sudden large vessel occlusion based on the artificial intelligence algorithm in the process described in .
[0280] refer to Figure 13 Then, the mechanical thrombectomy judging unit 45 executes the step of constructing a regression-structure artificial intelligence model based on multi-numeric data (step S111 ).
[0281] Afterwards, in the case of a patient whose dense arterial sign is detected at the subganglionic level, the mechanical thrombectomy judging unit 45 can perform an emergency large vessel occlusion (ELVO, Emergent large vessel occlusion) based on an artificial intelligence model. Step (step S90 ), the output of the ischemia classification step (step S70 ), and the Alberta stroke project early electronic computed tomography evaluation judged by step S100 to distinguish whether to operate or not (step S112 ).
[0282] That is, when the estimated early com...
no. 2 example
[0284] Figure 14 To illustrate it can be Figure 5 A flow chart of yet another embodiment of screening patients with sudden large vessel occlusion based on the artificial intelligence algorithm in the process described in .
[0285] refer to Figure 14 The mechanical thrombectomy judging unit 45 may execute the step of detecting the volume of the lesion (early ischemic signal, obvious hypodensity) caused by sudden large vessel occlusion by artificial intelligence (step S113 ).
[0286] Afterwards, the mechanical thrombectomy judging unit 45 may machine learning-logistic regression the volume of the detected lesion (step S114 ).
[0287] In addition, the mechanical thrombectomy judging unit 45 judges whether to perform mechanical thrombectomy based on the model of machine learning and the Alberta Stroke Program early computerized tomography score (step S115 ).
[0288] That is, it is possible to determine whether the at least one patient is a patient applicable to mechanica...
no. 3 example
[0290] Figure 15 To illustrate it can be Figure 5A flow chart of another embodiment of screening patients with sudden large vessel occlusion based on the artificial intelligence algorithm in the process described in .
[0291] refer to Figure 15 , the mechanical thrombectomy judging unit 45 executes the step of collecting the absolute time, early ischemic signal, and obviously low-density volume of plain-scan electronic computed tomography after the occurrence of large vessel occlusion (LVO, large vesselocclusion) (step S116) .
[0292] Thereafter, the mechanical thrombectomy judging unit 45 calculates a tissue clock based on the collected information (step S117).
[0293] Furthermore, the mechanical thrombectomy judging unit 45 judges whether to perform mechanical thrombectomy and the available time based on the tissue clock and the Alberta Stroke Program early computerized tomography score (step S118 ).
[0294] As described above, if it is identified as a patient sui...
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