Novel Technique for the Identification of Hip Implants Using Artificial Intelligence

人工智能 计算机科学 可扩展性 机器学习 卷积神经网络 人工神经网络 鉴定(生物学) 数据库 植物 生物
作者
Neil W Antonson,Brandt C. Buckner,Beau S. Konigsberg,Curtis W. Hartman,Kevin L. Garvin,Beau J. Kildow
出处
期刊:Journal of Arthroplasty [Elsevier BV]
被引量:2
标识
DOI:10.1016/j.arth.2024.02.001
摘要

Abstract

Background

The anticipated growth of total hip arthroplasty will result in an increased need for revision total hip arthroplasty. Preoperative planning, including identifying current implants, is critical for successful revision surgery. Artificial intelligence (AI) is promising for aiding clinical decision-making, including hip implant identification. However, previous studies have limitations such as small datasets, dissimilar stem designs, limited scalability, and the need for AI expertise. To address these limitations, we developed a novel technique to generate large datasets, tested radiographically similar stems, and demonstrated scalability utilizing a no-code machine learning solution.

Methods

We trained, validated, and tested an automated machine learning-implemented convolutional neural network to classify 9 radiographically similar femoral implants with a metaphyseal-fitting wedge taper design. Our novel technique uses computed tomography-derived projections of a 3-dimensional scanned implant model superimposed within a computed tomography pelvis volume. We employed computer-aided design modeling and MATLAB to process and manipulate the images. This generated 27,020 images for training (22,957) and validation (4,063) sets. We obtained 786 test images from various sources. The performance of the model was evaluated by calculating sensitivity, specificity, and accuracy.

Results

Our machine learning model discriminated the 9 implant models with a mean accuracy of 97.4%, sensitivity of 88.4%, and specificity of 98.5%.

Conclusions

Our novel hip implant detection technique accurately identified 9 radiographically similar implants. The method generates large datasets, is scalable, and can include historic or obscure implants. The no-code machine learning model demonstrates the feasibility of obtaining meaningful results without AI expertise, encouraging further research in this area.
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
slin_sjtu发布了新的文献求助10
1秒前
周周发布了新的文献求助20
1秒前
小党完成签到,获得积分10
1秒前
2秒前
昏睡的白桃完成签到,获得积分10
2秒前
小宇OvO发布了新的文献求助10
3秒前
jiaolulu发布了新的文献求助10
7秒前
量子星尘发布了新的文献求助10
7秒前
真的不想干活了完成签到,获得积分10
7秒前
美丽的依琴完成签到,获得积分10
8秒前
Xin完成签到,获得积分10
14秒前
Aurora.H完成签到,获得积分10
17秒前
17秒前
FashionBoy应助科研通管家采纳,获得10
18秒前
打打应助科研通管家采纳,获得10
18秒前
Jasper应助科研通管家采纳,获得10
18秒前
Ava应助科研通管家采纳,获得10
18秒前
顾矜应助科研通管家采纳,获得10
18秒前
上官若男应助科研通管家采纳,获得10
18秒前
duckspy发布了新的文献求助10
20秒前
20秒前
20秒前
xiaowan完成签到,获得积分10
21秒前
Terry完成签到,获得积分10
22秒前
张张张哈哈哈完成签到,获得积分10
22秒前
Research完成签到 ,获得积分10
22秒前
称心采枫完成签到 ,获得积分0
23秒前
23秒前
新新新新新发顶刊完成签到 ,获得积分10
24秒前
L3完成签到,获得积分10
25秒前
我是科研小能手完成签到,获得积分10
25秒前
风中的小丸子完成签到,获得积分10
26秒前
26秒前
时尚俊驰发布了新的文献求助10
27秒前
27秒前
27秒前
Grin完成签到,获得积分10
28秒前
周周完成签到,获得积分20
28秒前
29秒前
liufan完成签到 ,获得积分10
31秒前
高分求助中
【提示信息,请勿应助】关于scihub 10000
Les Mantodea de Guyane: Insecta, Polyneoptera [The Mantids of French Guiana] 3000
徐淮辽南地区新元古代叠层石及生物地层 3000
The Mother of All Tableaux: Order, Equivalence, and Geometry in the Large-scale Structure of Optimality Theory 3000
Handbook of Industrial Diamonds.Vol2 1100
Global Eyelash Assessment scale (GEA) 1000
Picture Books with Same-sex Parented Families: Unintentional Censorship 550
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 遗传学 基因 物理化学 催化作用 冶金 细胞生物学 免疫学
热门帖子
关注 科研通微信公众号,转发送积分 4038201
求助须知:如何正确求助?哪些是违规求助? 3575940
关于积分的说明 11373987
捐赠科研通 3305747
什么是DOI,文献DOI怎么找? 1819274
邀请新用户注册赠送积分活动 892662
科研通“疑难数据库(出版商)”最低求助积分说明 815022