用于胎盘多普勒图像自动分类和分析的人工智能工作流程。

IF 3.2 Q1 HEALTH CARE SCIENCES & SERVICES Frontiers in digital health Pub Date : 2024-10-16 eCollection Date: 2024-01-01 DOI:10.3389/fdgth.2024.1455767
Ainhoa M Aguado, Guillermo Jimenez-Perez, Devyani Chowdhury, Josa Prats-Valero, Sergio Sánchez-Martínez, Zahra Hoodbhoy, Shazia Mohsin, Roberta Castellani, Lea Testa, Fàtima Crispi, Bart Bijnens, Babar Hasan, Gabriel Bernardino
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引用次数: 0

摘要

简介从胎儿-胎盘多普勒图像中提取基于多普勒的测量值对于产前识别易受伤害的新生儿至关重要。然而,这一过程耗时长、依赖操作人员且容易出错:为了解决这个问题,我们的研究引入了一种人工智能(AI)工作流程,用于自动测量四个部位(即脐动脉(UA)、大脑中动脉(MCA)、主动脉峡部(AoI)和左心室流入和流出(LVIO))的胎盘多普勒,涉及分类和波形划分任务。我们的方法源自一个中低收入国家的数据,并使用一个高收入国家的数据集对其多功能性进行了测试和验证,展示了其在不同医疗环境下进行标准化和准确分析的潜力:多普勒视图的分类通过三个不同的模块进行:(i) 基于多普勒速度振幅的模型,准确率为 94%;(ii) 两个卷积神经网络 (CNN),准确率分别为 89.2% 和 67.3%;(iii) 多普勒视图和数据集相关置信模型,用于检测错误分类,准确率高于 85%。多普勒指数的提取利用了与多普勒视图相关的 CNN 和后处理技术。结果显示,LVIO、UA、AoI 和 MCA 视图中收缩期峰值大小位置的平均绝对百分比误差分别为 6.1 ± 4.9% (n = 682)、1.8 ± 1.5% (n = 1,480 )、4.7 ± 4.0% (n = 717)、3.5 ± 3.1% (n = 1,318 ):事实证明,所开发的模型在对多普勒视图进行分类以及从多普勒图像中提取基本测量值方面具有很高的准确性。这种人工智能工作流程的整合在减少人工工作量和提高胎盘多普勒图像分析效率方面大有可为,即使是非训练有素的读者也不例外。
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AI-enabled workflow for automated classification and analysis of feto-placental Doppler images.

Introduction: Extraction of Doppler-based measurements from feto-placental Doppler images is crucial in identifying vulnerable new-borns prenatally. However, this process is time-consuming, operator dependent, and prone to errors.

Methods: To address this, our study introduces an artificial intelligence (AI) enabled workflow for automating feto-placental Doppler measurements from four sites (i.e., Umbilical Artery (UA), Middle Cerebral Artery (MCA), Aortic Isthmus (AoI) and Left Ventricular Inflow and Outflow (LVIO)), involving classification and waveform delineation tasks. Derived from data from a low- and middle-income country, our approach's versatility was tested and validated using a dataset from a high-income country, showcasing its potential for standardized and accurate analysis across varied healthcare settings.

Results: The classification of Doppler views was approached through three distinct blocks: (i) a Doppler velocity amplitude-based model with an accuracy of 94%, (ii) two Convolutional Neural Networks (CNN) with accuracies of 89.2% and 67.3%, and (iii) Doppler view- and dataset-dependent confidence models to detect misclassifications with an accuracy higher than 85%. The extraction of Doppler indices utilized Doppler-view dependent CNNs coupled with post-processing techniques. Results yielded a mean absolute percentage error of 6.1 ± 4.9% (n = 682), 1.8 ± 1.5% (n = 1,480), 4.7 ± 4.0% (n = 717), 3.5 ± 3.1% (n = 1,318) for the magnitude location of the systolic peak in LVIO, UA, AoI and MCA views, respectively.

Conclusions: The developed models proved to be highly accurate in classifying Doppler views and extracting essential measurements from Doppler images. The integration of this AI-enabled workflow holds significant promise in reducing the manual workload and enhancing the efficiency of feto-placental Doppler image analysis, even for non-trained readers.

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