Separability of Acute Cerebral Infarction Lesions in CT Based Radiomics: Toward Artificial Intelligence-Assisted Diagnosis
المؤلفون المشاركون
Guan, Yun
Wang, Peng
Wang, Qi
Li, Peihao
Zeng, Jianchao
Qin, Pinle
Meng, Yanfeng
المصدر
العدد
المجلد 2020، العدد 2020 (31 ديسمبر/كانون الأول 2020)، ص ص. 1-8، 8ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2020-11-16
دولة النشر
مصر
عدد الصفحات
8
التخصصات الرئيسية
الملخص EN
This study aims at analyzing the separability of acute cerebral infarction lesions which were invisible in CT.
38 patients, who were diagnosed with acute cerebral infarction and performed both CT and MRI, and 18 patients, who had no positive finding in either CT or MRI, were enrolled.
Comparative studies were performed on lesion and symmetrical regions, normal brain and symmetrical regions, lesion, and normal brain regions.
MRI was reconstructed and affine transformed to obtain accurate lesion position of CT.
Radiomic features and information gain were introduced to capture efficient features.
Finally, 10 classifiers were established with selected features to evaluate the effectiveness of analysis.
1301 radiomic features were extracted from candidate regions after registration.
For lesion and their symmetrical regions, there were 280 features with information gain greater than 0.1 and 2 features with information gain greater than 0.3.
The average classification accuracy was 0.6467, and the best classification accuracy was 0.7748.
For normal brain and their symmetrical regions, there were 176 features with information gain greater than 0.1, 1 feature with information gain greater than 0.2.
The average classification accuracy was 0.5414, and the best classification accuracy was 0.6782.
For normal brain and lesions, there were 501 features with information gain greater than 0.1 and 1 feature with information gain greater than 0.5.
The average classification accuracy was 0.7480, and the best classification accuracy was 0.8694.
In conclusion, the study captured significant features correlated with acute cerebral infarction and confirmed the separability of acute lesions in CT, which established foundation for further artificial intelligence-assisted CT diagnosis.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Guan, Yun& Wang, Peng& Wang, Qi& Li, Peihao& Zeng, Jianchao& Qin, Pinle…[et al.]. 2020. Separability of Acute Cerebral Infarction Lesions in CT Based Radiomics: Toward Artificial Intelligence-Assisted Diagnosis. BioMed Research International،Vol. 2020, no. 2020, pp.1-8.
https://search.emarefa.net/detail/BIM-1137790
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Guan, Yun…[et al.]. Separability of Acute Cerebral Infarction Lesions in CT Based Radiomics: Toward Artificial Intelligence-Assisted Diagnosis. BioMed Research International No. 2020 (2020), pp.1-8.
https://search.emarefa.net/detail/BIM-1137790
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Guan, Yun& Wang, Peng& Wang, Qi& Li, Peihao& Zeng, Jianchao& Qin, Pinle…[et al.]. Separability of Acute Cerebral Infarction Lesions in CT Based Radiomics: Toward Artificial Intelligence-Assisted Diagnosis. BioMed Research International. 2020. Vol. 2020, no. 2020, pp.1-8.
https://search.emarefa.net/detail/BIM-1137790
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1137790
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
تقوم هذه الخدمة بالتحقق من التشابه أو الانتحال في الأبحاث والمقالات العلمية والأطروحات الجامعية والكتب والأبحاث باللغة العربية، وتحديد درجة التشابه أو أصالة الأعمال البحثية وحماية ملكيتها الفكرية. تعرف اكثر