医学
腰椎
分级(工程)
腰椎管狭窄症
磁共振成像
放射科
医学诊断
狭窄
数据集
椎管狭窄
卷积神经网络
人工智能
计算机科学
工程类
土木工程
作者
Kaiyu Li,Junjie Weng,Hua-Lin Li,Hao-Bo Ye,Jianwei Xiang,Naifeng Tian
出处
期刊:Spine
[Ovid Technologies (Wolters Kluwer)]
日期:2023-12-19
卷期号:49 (12): 884-891
被引量:2
标识
DOI:10.1097/brs.0000000000004903
摘要
Study Design. Retrospective study. Objectives. This study aimed to develop an initial deep-learning (DL) model based on computerized tomography (CT) scans for diagnosing lumbar spinal stenosis. Summary of Background Data. Magnetic resonance imaging is commonly used for diagnosing lumbar spinal stenosis due to its high soft tissue resolution, but CT is more portable, cost-effective, and has wider regional coverage. Using DL models to improve the accuracy of CT diagnosis can effectively reduce missed diagnoses and misdiagnoses in clinical practice. Materials and Methods. Axial lumbar spine CT scans obtained between March 2022 and September 2023 were included. The data set was divided into a training set (62.3%), a validation set (22.9%), and a control set (14.8%). All data were labeled by two spine surgeons using the widely accepted grading system for lumbar spinal stenosis. The training and validation sets were used to annotate the regions of interest by the two spine surgeons. First, a region of interest detection model and a convolutional neural network classifier were trained using the training set. After training, the model was preliminarily evaluated using a validation set. Finally, the performance of the DL model was evaluated on the control set, and a comparison was made between the model and the classification performance of specialists with varying levels of experience. Results. The central stenosis grading accuracies of DL Model Version 1 and DL Model Version 2 were 88% and 83%, respectively. The lateral recess grading accuracies of DL Model Version 1 and DL Model Version 2 were 75% and 71%, respectively. Conclusions. Our preliminarily developed DL system for assessing the degree of lumbar spinal stenosis in CT, including the central canal and lateral recess, has shown similar accuracy to experienced specialist physicians. This holds great value for further development and clinical application.
科研通智能强力驱动
Strongly Powered by AbleSci AI