Improved ResNet18 lightweight deep learning models for automatically detecting gouty arthritis lesions based on ultrasonogram of the first metatarsophalangeal joint
10.13929/j.issn.1003-3289.2025.05.019
- VernacularTitle:改良ResNet18轻量深度学习模型基于第一跖趾关节声像图自动检测痛风性关节炎病变
- Author:
Lishan XIAO
1
;
Yizhe ZHAO
;
Yuchen LI
;
Mengmeng YAN
;
Meixia DU
;
Cheng ZHAO
;
Manhua LIU
;
Chunping NING
Author Information
1. 青岛大学附属医院腹部超声科,山东青岛 266000
- Publication Type:Journal Article
- Keywords:
arthritis,gouty;
ultrasonography;
deep learning;
metatarsophalangeal joint
- From:
Chinese Journal of Medical Imaging Technology
2025;41(5):783-787
- CountryChina
- Language:Chinese
-
Abstract:
Objective To explore the value of improved ResNet18 lightweight deep learning(DL)models for automatically detecting gouty arthritis(GA)based on ultrasonogram of the first metatarsophalangeal joint(MTP1).Methods A total of 2 401 ultrasonograms obtained from 260 patients with suspected gout who underwent MTP1 ultrasound examination were included and divided into training set(1 910 ultrasonograms from 209 cases)and test set(491 ultrasonograms from 51 cases)at the ratio of 4∶1.GA lesions on ultrasonograms were manually labeled.After preprocessing,ResNet18 lightweight network was used to construct DL models for identifying the ultrasonogram category was normal or abnormal(with any manifestation of GA).Five-fold cross-validation method was adopted to evaluate the efficacy of the DL models constructed with 2,3,4 or 6 residual blocks,i.e.model 1,2,3 and 4,respectively,and the computational cost and the amount of parameters of each model were recorded.The efficacy of the models were verified using test set,and the best DL model was screened.Results The computational cost of model 1,2,3 and 4 was 7 558.27,2 963.73,4 012.33 and 6 093.39 M,respectively,while the amount of parameters was 4.61,4.91,4.91 and 5.28 M,respectively.Model 2 had the least computational cost with parameters only slightly more than model 1.In test set,no significant difference of accuracy nor the area under the curve was found among 4 models(all P＞0.05).The sensitivity of model 2 was higher than that of model 3,while its specificity was lower only than that of model 3(both P＜0.05),hence model 2 was the best DL model.Conclusion Improved ResNet18 lightweight DL models could be used for automatically detecting GA based on ultrasonogram of MTP1,among which model 2 was the best one.
