Diagnosis of Developmental Dysplasia of the Hip by Ultrasound Imaging Using Deep Learning

医学 深度学习 人工智能 混淆矩阵 超声波 机器学习 放射科 计算机科学
作者
Maki Kinugasa,Atsuyuki Inui,Shinichi Satsuma,Daisuke Kobayashi,Ryosuke Sakata,Masayuki Morishita,Izumi Komoto,Ryosuke Kuroda
出处
期刊:Journal of Pediatric Orthopaedics [Lippincott Williams & Wilkins]
卷期号:43 (7): e538-e544 被引量:8
标识
DOI:10.1097/bpo.0000000000002428
摘要

A timely diagnosis of developmental dysplasia of the hip (DDH) is important for satisfactory clinical outcomes. Ultrasonography is a useful tool for DDH screening; however, it is technically demanding. We hypothesized that deep learning could assist in the diagnosis of DDH. In this study, several deep-learning models were assessed to diagnose DDH on ultrasonograms. This study aimed to evaluate the accuracy of diagnoses made by artificial intelligence (AI) using deep learning on ultrasound images of DDH.Infants who were up to 6 months old with suspected DDH were included. DDH diagnosis using ultrasonography was performed according to the Graf classification. Data on 60 infants (64 hips) with DDH and 131 healthy infants (262 hips) obtained from 2016 to 2021 were retrospectively reviewed. For deep learning, a MATLAB deep learning toolbox (MathWorks, Natick, MA, US) was used, and 80% of the images were used as training data, with the rest as validation data. Training images were augmented to increase data variation. In addition, 214 ultrasound images were used as test data to evaluate the AI's accuracy. Pre-trained models (SqueezeNet, MobileNet_v2, and EfficientNet) were used for transfer learning. Model accuracy was evaluated using a confusion matrix. The region of interest of each model was visualized using gradient-weighted class activation mapping (Grad-CAM), occlusion sensitivity, and image LIME.The best scores for accuracy, precision, recall, and F-measure were all 1.0 in each model. In DDH hips, the region of interest for deep learning models was the area lateral to the femoral head, including the labrum and joint capsule. However, for normal hips, the models highlighted the medial and proximal areas where the lower margin of the os ilium and the normal femoral head exist.Ultrasound imaging with deep learning can assess DDH with high accuracy. This system could be refined for a convenient and accurate diagnosis of DDH.Level-Ⅳ.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
mingnan完成签到 ,获得积分10
刚刚
希望天下0贩的0应助23333采纳,获得10
1秒前
余Y发布了新的文献求助10
1秒前
科研通AI5应助失眠小鸭子采纳,获得30
1秒前
1秒前
英吉利25发布了新的文献求助10
1秒前
坤123完成签到,获得积分20
1秒前
小松松完成签到,获得积分10
2秒前
芒果完成签到,获得积分10
2秒前
2秒前
雪落你看不见完成签到,获得积分10
2秒前
2秒前
流萤完成签到,获得积分10
2秒前
3秒前
斯文龙猫完成签到,获得积分10
3秒前
3秒前
seven完成签到,获得积分10
4秒前
wdwa发布了新的文献求助30
4秒前
由醉香完成签到 ,获得积分20
5秒前
aodilee完成签到,获得积分10
5秒前
yx阿聪发布了新的文献求助10
6秒前
哒哒哒发布了新的文献求助10
6秒前
qingg完成签到,获得积分10
6秒前
拼搏的不评完成签到,获得积分10
7秒前
超人Steiner完成签到 ,获得积分10
7秒前
Katsuya完成签到,获得积分10
7秒前
科研通AI5应助香菜采纳,获得10
7秒前
完美世界应助JHY采纳,获得10
7秒前
zhr发布了新的文献求助10
7秒前
努力学习才能找到工作完成签到 ,获得积分10
8秒前
伶俐的苡发布了新的文献求助10
8秒前
8秒前
jane发布了新的文献求助10
8秒前
靳士金完成签到,获得积分10
8秒前
杨氏发布了新的文献求助10
8秒前
高大一一发布了新的文献求助10
9秒前
风生完成签到,获得积分10
9秒前
CCrain应助庄严采纳,获得10
9秒前
9秒前
小赵发布了新的文献求助10
9秒前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
Acute Mountain Sickness 2000
Handbook of Milkfat Fractionation Technology and Application, by Kerry E. Kaylegian and Robert C. Lindsay, AOCS Press, 1995 1000
A novel angiographic index for predicting the efficacy of drug-coated balloons in small vessels 500
Textbook of Neonatal Resuscitation ® 500
The Affinity Designer Manual - Version 2: A Step-by-Step Beginner's Guide 500
Affinity Designer Essentials: A Complete Guide to Vector Art: Your Ultimate Handbook for High-Quality Vector Graphics 500
热门求助领域 (近24小时)
化学 医学 生物 材料科学 工程类 有机化学 内科学 生物化学 物理 计算机科学 纳米技术 遗传学 基因 复合材料 化学工程 物理化学 病理 催化作用 免疫学 量子力学
热门帖子
关注 科研通微信公众号,转发送积分 5067449
求助须知:如何正确求助?哪些是违规求助? 4289266
关于积分的说明 13362795
捐赠科研通 4108762
什么是DOI,文献DOI怎么找? 2249909
邀请新用户注册赠送积分活动 1255368
关于科研通互助平台的介绍 1187865