■马凡氏综合症(MFS),影响结缔组织的遗传疾病,表现在一系列可以影响许多身体系统的表型中,尤其是胸主动脉.该综合征通常表现出独特的面部特征,可能允许诊断性临床识别。在这里,我们从普通的面部图像中探索人工智能(AI)在诊断马凡氏综合征中的潜力,根据总体准确性评估,F1得分,和ROC曲线下面积。
■本研究通过面部图像探索利用卷积神经网络(CNN)进行MFS识别,提供一本小说,非侵入性,自动化,和计算机化诊断方法。该研究从普通的在线面部图像中检验了神经网络在马凡病诊断中的准确性。该模型在672张面部图像中的80%(182张Marfan和490张对照)上进行了训练。其他20%的图像用作测试集。
■总体准确率为98.5%(0%假阳性,2%假阴性)。马凡相的F1评分为97%,非马凡相的F1评分为99%。ROC曲线下面积为100%。
■人工智能(AI)程序能够以极高的准确度将Marfan与非Marfan面部图像(来自普通的在线照片)区分开。预计该计划的临床实用性。然而,由于这项工作的局限性和初步性,这应该被视为只是一项试点研究。
UNASSIGNED: Marfan Syndrome (MFS), a genetic disorder impacting connective tissue, manifests in a wide array of phenotypes which can affect numerous bodily systems, especially the thoracic aorta. The syndrome often presents distinct facial features that potentially allow for diagnostic clinical recognition. Herein, we explore the potential of Artificial Intelligence (AI) in diagnosing Marfan syndrome from ordinary facial images, as assessed by overall accuracy, F1 score, and area under the ROC curve.
UNASSIGNED: This study explores the utilization of Convolutional Neural Networks (CNN) for MFS identification through facial images, offering a novel, non-invasive, automated, and computerized diagnostic approach. The research examines the accuracy of Neural Networks in the diagnosis of Marfan Disease from ordinary on-line facial images. The model was trained on 80 % of 672 facial images (182 Marfan and 490 control). The other 20 % of images were used as the test set.
UNASSIGNED: Overall accuracy was 98.5 % (0 % false positive, 2 % false negative). F1 score was 97 % for Marfan facies and 99 % for non-Marfan facies. Area under the ROC curve was 100 %.
UNASSIGNED: An Artificial Intelligence (AI) program was able to distinguish Marfan from non-Marfan facial images (from ordinary on-line photographs) with an extremely high degree of accuracy. Clinical usefulness of this program is anticipated. However, due to the limited and preliminary nature of this work, this should be viewed as only a pilot study.