School of Information Science & Engineering, Lanzhou University, Lanzhou, 730000, China.
College of Physics and Electronic Engineering, Northwest Normal University, Lanzhou, 730070, China.
BMC Pregnancy Childbirth. 2024 Nov 16;24(1):758. doi: 10.1186/s12884-024-06916-y.
Developing a combined artificial intelligence (AI) and ultrasound imaging to provide an accurate, objective, and efficient adjunctive diagnostic approach for fetal heart ventricular septal defects (VSD).
1,451 fetal heart ultrasound images from 500 pregnant women were comprehensively analyzed between January 2016 and June 2022. The fetal heart region was manually labeled and the presence of VSD was discriminated by experts. The principle of five-fold cross-validation was followed in the training set to develop the AI model to assist in the diagnosis of VSD. The model was evaluated in the test set using metrics such as mAP@0.5, precision, recall, and F1 score. The diagnostic accuracy and inference time were also compared with junior doctors, intermediate doctors, and senior doctors.
The mAP@0.5, precision, recall, and F1 scores for the AI model diagnosis of VSD were 0.926, 0.879, 0.873, and 0.88, respectively. The accuracy of junior doctors and intermediate doctors improved by 6.7% and 2.8%, respectively, with the assistance of this system.
This study reports an AI-assisted diagnostic method for VSD that has a high agreement with manual recognition. It also has a low number of parameters and computational complexity, which can also improve the diagnostic accuracy and speed of some physicians for VSD.
开发一种结合人工智能（AI）和超声成像的方法，为胎儿心脏室间隔缺损（VSD）提供准确、客观和高效的辅助诊断方法。
2016 年 1 月至 2022 年 6 月，对 500 名孕妇的 1451 例胎儿心脏超声图像进行了综合分析。手动标记胎儿心脏区域，由专家区分 VSD 的存在。在训练集中遵循五重交叉验证原则开发 AI 模型以辅助 VSD 诊断。使用 mAP@0.5、精度、召回率和 F1 分数等指标在测试集中评估模型。还将诊断准确性和推理时间与初级医生、中级医生和高级医生进行了比较。
AI 模型对 VSD 的诊断的 mAP@0.5、精度、召回率和 F1 分数分别为 0.926、0.879、0.873 和 0.88。该系统辅助下，初级医生和中级医生的准确率分别提高了 6.7%和 2.8%。
本研究报告了一种 VSD 的 AI 辅助诊断方法，与手动识别具有高度一致性。它的参数和计算复杂度也较低，还可以提高一些医生对 VSD 的诊断准确性和速度。
