التفاصيل البيبلوغرافية
| العنوان: |
Establishment and validation of an AI-aid method in the diagnosis of myocardial perfusion imaging |
| المؤلفون: |
Ruyi Zhang, Peng Wang, Yanzhu Bian, Yan Fan, Jianming Li, Xuehui Liu, Jie Shen, Yujing Hu, Xianghe Liao, He Wang, Chengyu Song, Wangxiao Li, Xiaojie Wang, Momo Sun, Jianping Zhang, Miao Wang, Shen Wang, Yiming Shen, Xuemei Zhang, Qiang Jia, Jian Tan, Ning Li, Sen Wang, Lingyun Xu, Weiming Wu, Wei Zhang, Zhaowei Meng |
| المصدر: |
BMC Medical Imaging, Vol 23, Iss 1, Pp 1-10 (2023) |
| بيانات النشر: |
BMC, 2023. |
| سنة النشر: |
2023 |
| المجموعة: |
LCC:Medical technology |
| مصطلحات موضوعية: |
Artificial intelligence (AI), Machine learning, Coronary artery disease (CAD), Myocardial perfusion imaging (MPI), SPECT/CT, Medical technology, R855-855.5 |
| الوصف: |
Abstract Background This study aimed to develop and validate an AI (artificial intelligence)-aid method in myocardial perfusion imaging (MPI) to differentiate ischemia in coronary artery disease. Methods We retrospectively selected 599 patients who had received gated-MPI protocol. Images were acquired using hybrid SPECT-CT systems. A training set was used to train and develop the neural network and a validation set was used to test the predictive ability of the neural network. We used a learning technique named “YOLO” to carry out the training process. We compared the predictive accuracy of AI with that of physician interpreters (beginner, inexperienced, and experienced interpreters). Results Training performance showed that the accuracy ranged from 66.20% to 94.64%, the recall rate ranged from 76.96% to 98.76%, and the average precision ranged from 80.17% to 98.15%. In the ROC analysis of the validation set, the sensitivity range was 88.9 ~ 93.8%, the specificity range was 93.0 ~ 97.6%, and the AUC range was 94.1 ~ 96.1%. In the comparison between AI and different interpreters, AI outperformed the other interpreters (most P-value |
| نوع الوثيقة: |
article |
| وصف الملف: |
electronic resource |
| اللغة: |
English |
| تدمد: |
1471-2342 |
| Relation: |
https://doaj.org/toc/1471-2342 |
| DOI: |
10.1186/s12880-023-01037-y |
| URL الوصول: |
https://doaj.org/article/256fd777c59a4b7dbcd23a7b4be25030 |
| رقم الانضمام: |
edsdoj.256fd777c59a4b7dbcd23a7b4be25030 |
| قاعدة البيانات: |
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