Deep learning model for automated diagnosis of moyamoya disease based on magnetic resonance angiography.

IF 9.6 1区医学 Q1 MEDICINE, GENERAL & INTERNAL

EClinicalMedicine Pub Date : 2024-11-05 eCollection Date: 2024-11-01 DOI:10.1016/j.eclinm.2024.102888

Mingming Lu, Yijia Zheng, Shitong Liu, Xiaolan Zhang, Jiahui Lv, Yuan Liu, Baobao Li, Fei Yuan, Peng Peng, Cong Han, Chune Ma, Chao Zheng, Hongtao Zhang, Jianming Cai

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引用次数: 0

Abstract

Background: This study explores the potential of the deep learning-based convolutional neural network (CNN) to automatically recognize MMD using MRA images from atherosclerotic disease (ASD) and normal control (NC).

Methods: In this retrospective study in China, 600 participants (200 MMD, 200 ASD and 200 NC) were collected from one institution as an internal dataset for training and 60 from another institution were collected as external testing set for validation. All participants were divided into training (N = 450) and validation sets (N = 90), internal testing set (N = 60), and external testing set (N = 60). The input to the CNN models comprised preprocessed MRA images, while the output was a tripartite classification label that identified the patient's diagnostic group. The performances of 3D CNN models were evaluated using a comprehensive set of metrics such as area under the curve (AUC) and accuracy. Gradient-weighted Class Activation Mapping (Grad-CAM) was used to visualize the CNN's decision-making process in MMD diagnosis by highlighting key areas. Finally, the diagnostic performances of the CNN models were compared with those of two experienced radiologists.

Findings: DenseNet-121 exhibited superior discrimination capabilities, achieving a macro-average AUC of 0.977 (95% CI, 0.928-0.995) in the internal test sets and 0.880 (95% CI, 0.786-0.937) in the external validation sets, thus exhibiting comparable diagnostic capabilities to those of human radiologists. In the binary classification where ASD and NC were group together, with MMD as the separate group for targeted detection, DenseNet-121 achieved an accuracy of 0.967 (95% CI, 0.886-0.991). Additionally, the Grad-CAM results for the MMD, with areas of intense redness indicating critical areas identified by the model, reflected decision-making similar to human experts.

Interpretation: This study highlights the efficacy of CNN model in the automated diagnosis of MMD on MRA images, easing the workload on radiologists and promising integration into clinical workflows.

Funding: National Natural Science Foundation of China, Tianjin Science and Technology Project and Beijing Natural Science Foundation.

查看原文本刊更多论文

基于磁共振血管造影的莫亚莫亚病自动诊断深度学习模型。

背景：本研究探讨了基于深度学习的卷积神经网络（CNN）利用动脉粥样硬化疾病（ASD）和正常对照（NC）的MRA图像自动识别MMD的潜力：在这项中国的回顾性研究中，从一家机构收集了600名参与者（200名MMD、200名ASD和200名NC）作为内部数据集进行训练，从另一家机构收集了60名参与者作为外部测试集进行验证。所有参与者被分为训练集（450 人）和验证集（90 人）、内部测试集（60 人）和外部测试集（60 人）。CNN 模型的输入包括预处理的 MRA 图像，而输出则是一个三方分类标签，用于识别患者的诊断组别。使用曲线下面积（AUC）和准确率等综合指标对 3D CNN 模型的性能进行了评估。梯度加权类激活图谱（Grad-CAM）通过突出关键区域，将 CNN 在 MMD 诊断中的决策过程可视化。最后，将 CNN 模型的诊断性能与两位经验丰富的放射科医生的诊断性能进行了比较：DenseNet-121表现出卓越的分辨能力，在内部测试集上的宏观平均AUC达到0.977（95% CI，0.928-0.995），在外部验证集上的宏观平均AUC达到0.880（95% CI，0.786-0.937），因此表现出与人类放射科医生相当的诊断能力。在二元分类中，ASD 和 NC 被归为一组，MMD 作为单独的一组进行定向检测，DenseNet-121 的准确率达到了 0.967（95% CI，0.886-0.991）。此外，Grad-CAM 对 MMD 的检测结果显示，模型识别出的关键区域呈现出强烈的红色，这反映了与人类专家相似的决策结果：本研究强调了 CNN 模型在 MRA 图像 MMD 自动诊断中的功效，减轻了放射科医生的工作量，有望融入临床工作流程：国家自然科学基金、天津市科技计划项目和北京市自然科学基金。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

EClinicalMedicine Medicine-Medicine (all)

CiteScore

18.90

自引率

1.30%

发文量

506

审稿时长

22 days

期刊介绍： eClinicalMedicine is a gold open-access clinical journal designed to support frontline health professionals in addressing the complex and rapid health transitions affecting societies globally. The journal aims to assist practitioners in overcoming healthcare challenges across diverse communities, spanning diagnosis, treatment, prevention, and health promotion. Integrating disciplines from various specialties and life stages, it seeks to enhance health systems as fundamental institutions within societies. With a forward-thinking approach, eClinicalMedicine aims to redefine the future of healthcare.