Purpose

The purpose of this study is to evaluate the efficacy of an artificial intelligence (AI) model designed to identify active bleeding in digital subtraction angiography images for upper gastrointestinal bleeding.

Methods

Angiographic images were retrospectively collected from mesenteric and celiac artery embolization procedures performed between 2018 and 2022. This dataset included images showing both active bleeding and non-bleeding phases from the same patients. The images were labeled as normal versus images that contain active bleeding. A convolutional neural network was trained and validated to automatically classify the images. Algorithm performance was tested in terms of area under the curve, accuracy, sensitivity, specificity, F1 score, positive and negative predictive value.

Results

The dataset included 587 pre-labeled images from 142 patients. Of these, 302 were labeled as normal angiogram and 285 as containing active bleeding. The model’s performance on the validation cohort was area under the curve 85.0 ± 10.9% (standard deviation) and average classification accuracy 77.43 ± 4.9%. For Youden’s index cutoff, sensitivity and specificity were 85.4 ± 9.4% and 81.2 ± 8.6%, respectively.

Conclusion

In this study, we explored the application of AI in mesenteric and celiac artery angiography for detecting active bleeding. The results of this study show the potential of an AI-based algorithm to accurately classify images with active bleeding. Further studies using a larger dataset are needed to improve accuracy and allow segmentation of the bleeding.

Graphical abstract

目的

本研究的目的是评估人工智能 (AI) 模型的功效，该模型旨在识别数字减影血管造影图像中的活动性出血，以治疗上消化道出血。

方法

血管造影图像是从 2018 年至 2022 年期间进行的肠系膜和腹腔动脉栓塞手术中回顾性收集的。该数据集包括显示同一患者的活动性出血和非出血期的图像。这些图像被标记为正常图像与包含活动性出血的图像。卷积神经网络经过训练和验证，可以自动对图像进行分类。算法性能通过曲线下面积、准确性、敏感性、特异性、F1 评分、阳性和阴性预测值进行测试。

结果

该数据集包括来自 142 名患者的 587 张预先标记的图像。其中，302 例被标记为正常血管造影，285 例被标记为含有活动性出血。该模型在验证队列上的性能曲线下面积为 85.0 ± 10.9%（标准差），平均分类准确度为 77.43 ± 4.9%。对于约登指数截止值，敏感性和特异性分别为 85.4 ± 9.4% 和 81.2 ± 8.6%。

结论

在这项研究中，我们探索了人工智能在肠系膜和腹腔动脉血管造影中检测活动性出血的应用。这项研究的结果显示了基于人工智能的算法对活动性出血图像进行准确分类的潜力。需要使用更大的数据集进行进一步的研究，以提高准确性并允许对出血进行分割。

图形概要