WO2023017889A1 - Méthode et dispositif pour diagnostiquer un accident vasculaire cérébral ischémique aigu à l'aide d'un modèle d'apprentissage profond basé sur un réseau neuronal à convolution (cnn) tridimensionnel (3d) - Google Patents
Méthode et dispositif pour diagnostiquer un accident vasculaire cérébral ischémique aigu à l'aide d'un modèle d'apprentissage profond basé sur un réseau neuronal à convolution (cnn) tridimensionnel (3d) Download PDFInfo
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- WO2023017889A1 WO2023017889A1 PCT/KR2021/011277 KR2021011277W WO2023017889A1 WO 2023017889 A1 WO2023017889 A1 WO 2023017889A1 KR 2021011277 W KR2021011277 W KR 2021011277W WO 2023017889 A1 WO2023017889 A1 WO 2023017889A1
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- ischemic stroke
- acute ischemic
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Definitions
- the present applicant has come to the present invention by recognizing the limitations of the prior art and conducting research that can help prognosis treatment and secondary prevention by predicting a more accurate mechanism of occurrence for patients with acute ischemic stroke.
- the present invention is the first technology to classify subtypes of stroke mechanisms by analyzing acute ischemic stroke lesion patterns through deep learning based on 3D-CNN using DWI and ADC of patients with acute ischemic stroke.
- the main results according to the present invention are as follows. First, the 3D-CNN-based segmentation accuracy for acute ischemic stroke lesions was found to be 0.843 in the Dyce score. Second, in terms of subtype classification to classify causes of acute ischemic stroke, the predictive value of cause classification according to TOAST classification was 81.3% for LAA, 84.6% for SVO, and 73.0% for CE, respectively.
- FIG. 7 is a block diagram illustrating a diagnosis device 700 according to the present invention
- FIG. 8 is a flowchart illustrating a diagnosis method performed by the diagnosis device 700.
- a diagnosis apparatus and method according to the present invention will be described with reference to FIGS. 7 and 8 , but details overlapping with those described above will be omitted.
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Abstract
La présente invention concerne une méthode pour diagnostiquer un accident vasculaire cérébral ischémique aigu à l'aide d'un modèle d'apprentissage profond basé sur un réseau neuronal à convolution (CNN) tridimensionnel (3D), laquelle méthode consiste à : collecter des données de volume 3D qui sont des données générées par imagerie du cerveau d'un patient et qui comprennent une image pondérée en fonction de la diffusion (DWI) et une carte de coefficient de diffusion apparent (ADC); utiliser un modèle de segmentation de lésion appris comme modèle d'apprentissage profond prédéfini pour segmenter et délivrer une zone de lésion associée à un accident vasculaire cérébral ischémique aigu à partir des données de volume 3D; et utiliser un modèle de classification de type inférieur appris comme modèle d'apprentissage profond prédéfini pour classifier et délivrer un type inférieur correspondant au mécanisme causal d'accident vasculaire cérébral ischémique aigu sur la base des données de volume 3D et de la zone de lésion segmentée.
Applications Claiming Priority (2)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| KR1020210105131A KR102586853B1 (ko) | 2021-08-10 | 2021-08-10 | 3d-cnn 기반의 딥러닝 모델을 이용한 급성 허혈성 뇌졸중 진단 정보 제공 장치 및 방법 |
| KR10-2021-0105131 | 2021-08-10 |
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| Publication Number | Publication Date |
|---|---|
| WO2023017889A1 true WO2023017889A1 (fr) | 2023-02-16 |
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| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| PCT/KR2021/011277 WO2023017889A1 (fr) | 2021-08-10 | 2021-08-24 | Méthode et dispositif pour diagnostiquer un accident vasculaire cérébral ischémique aigu à l'aide d'un modèle d'apprentissage profond basé sur un réseau neuronal à convolution (cnn) tridimensionnel (3d) |
Country Status (2)
| Country | Link |
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| KR (1) | KR102586853B1 (fr) |
| WO (1) | WO2023017889A1 (fr) |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR101854071B1 (ko) * | 2017-01-13 | 2018-05-03 | 고려대학교 산학협력단 | 딥러닝을 사용하여 관심 부위 이미지를 생성하는 방법 및 장치 |
| KR101894722B1 (ko) * | 2018-03-12 | 2018-10-04 | 미디어젠(주) | 언어 장애 발생 진단을 이용한 건강 이상 예측시스템 및 예측방법 |
| KR102015473B1 (ko) * | 2017-06-22 | 2019-08-28 | 연세대학교 산학협력단 | 신경계 질환 관리를 위한 시스템 및 방법 |
| KR102211050B1 (ko) * | 2020-12-01 | 2021-02-04 | (주)엔브레인 | 의료 영상 정보를 이용한 인공지능 기반 뇌 정보 제공 장치 |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR102015224B1 (ko) | 2018-09-18 | 2019-10-21 | (주)제이엘케이인스펙션 | 딥러닝 기반의 뇌출혈 및 뇌종양 병변 진단 방법 및 장치 |
-
2021
- 2021-08-10 KR KR1020210105131A patent/KR102586853B1/ko active IP Right Grant
- 2021-08-24 WO PCT/KR2021/011277 patent/WO2023017889A1/fr unknown
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR101854071B1 (ko) * | 2017-01-13 | 2018-05-03 | 고려대학교 산학협력단 | 딥러닝을 사용하여 관심 부위 이미지를 생성하는 방법 및 장치 |
| KR102015473B1 (ko) * | 2017-06-22 | 2019-08-28 | 연세대학교 산학협력단 | 신경계 질환 관리를 위한 시스템 및 방법 |
| KR101894722B1 (ko) * | 2018-03-12 | 2018-10-04 | 미디어젠(주) | 언어 장애 발생 진단을 이용한 건강 이상 예측시스템 및 예측방법 |
| KR102211050B1 (ko) * | 2020-12-01 | 2021-02-04 | (주)엔브레인 | 의료 영상 정보를 이용한 인공지능 기반 뇌 정보 제공 장치 |
Non-Patent Citations (1)
| Title |
|---|
| PARK SEUNG, KIM BAIK-KYUN, HAN MOON-KU, HONG JEONG-HO, YUM KYU SUN, LEE DAE-IN: "Deep Learning for Prediction of Mechanism in Acute Ischemic Stroke Using Brain MRI", RESEARCH SQUARE, 11 June 2021 (2021-06-11), XP093034346, DOI: 10.21203/rs.3.rs-604141/v1 * |
Also Published As
| Publication number | Publication date |
|---|---|
| KR20230024446A (ko) | 2023-02-21 |
| KR102586853B1 (ko) | 2023-10-12 |
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