Bellomo Tiffany R, Goudot Guillaume, Lella Srihari K, Landau Eric, Sumetsky Natalie, Zacharias Nikolaos, Fischetti Chanel, Dua Anahita
Division of Vascular and Endovascular Surgery, Massachusetts General Hospital, Boston, MA 02114, USA.
Harvard Medical School, Massachusetts General Hospital, Boston, MA 02114, USA.
Diagnostics (Basel). 2023 Dec 25;14(1):46. doi: 10.3390/diagnostics14010046.
DUS measurements for popliteal artery aneurysms (PAAs) specifically can be time-consuming, error-prone, and operator-dependent. To eliminate this subjectivity and provide efficient segmentation, we applied artificial intelligence (AI) to accurately delineate inner and outer lumen on DUS. DUS images were selected from a cohort of patients with PAAs from a multi-institutional platform. Encord is an easy-to-use, readily available online AI platform that was used to segment both the inner lumen and outer lumen of the PAA on DUS images. A model trained on 20 images and tested on 80 images had a mean Average Precision of 0.85 for the outer polygon and 0.23 for the inner polygon. The outer polygon had a higher recall score than precision score at 0.90 and 0.85, respectively. The inner polygon had a score of 0.25 for both precision and recall. The outer polygon false-negative rate was the lowest in images with the least amount of blur. This study demonstrates the feasibility of using the widely available Encord AI platform to identify standard features of PAAs that are critical for operative decision making.
腘动脉瘤（PAA）的超声（DUS）测量尤其可能耗时、容易出错且依赖操作者。为了消除这种主观性并提供高效的分割，我们应用人工智能（AI）在DUS上准确描绘内腔和外腔。DUS图像选自一个多机构平台的PAA患者队列。Encord是一个易于使用、随时可用的在线AI平台，用于在DUS图像上分割PAA的内腔和外腔。在20幅图像上训练并在80幅图像上测试的模型，其外多边形的平均平均精度为0.85，内多边形为0.23。外多边形的召回率分别高于精度得分，分别为0.90和0.85。内多边形的精度和召回率均为0.25。在模糊程度最低的图像中，外多边形的假阴性率最低。本研究证明了使用广泛可用的Encord AI平台识别对手术决策至关重要的PAA标准特征的可行性。
