BREAST+: An augmented reality interface that speeds up perforator marking for DIEAP flap reconstruction surgery

IF 2.8 Q3 ENGINEERING, BIOMEDICAL Healthcare Technology Letters Pub Date : 2024-12-10 DOI:10.1049/htl2.12095
Rafaela Timóteo, David Pinto, Pedro Matono, Carlos Mavioso, Maria-João Cardoso, Pedro Gouveia, Tiago Marques, Daniel Simões Lopes
{"title":"BREAST+: An augmented reality interface that speeds up perforator marking for DIEAP flap reconstruction surgery","authors":"Rafaela Timóteo,&nbsp;David Pinto,&nbsp;Pedro Matono,&nbsp;Carlos Mavioso,&nbsp;Maria-João Cardoso,&nbsp;Pedro Gouveia,&nbsp;Tiago Marques,&nbsp;Daniel Simões Lopes","doi":"10.1049/htl2.12095","DOIUrl":null,"url":null,"abstract":"<p>Deep inferior epigastric artery perforator flap reconstruction is a common technique for breast reconstruction surgery in cancer patients. Preoperative planning typically depends on radiological reports and 2D images to help surgeons locate abdominal perforator vessels before surgery. Here, BREAST+, an augmented reality interface for the HoloLens 2, designed to facilitate accurate marking of perforator locations on the patients' skin and to seamlessly access relevant clinical data in the operating room is proposed. The system is evaluated in a controlled setting by conducting a user study with 27 medical students and 2 breast surgeons. Quantitative (marking error, task completion time, and number of task repetitions) and qualitative (perceived usability, perceived workload, user preference and user satisfaction) data are collected to assess BREAST+ performance during perforator marking. The average time taken to mark each perforator is 7.7 ± 6.5 s, with an average absolute error of 6.8 ± 2.6 mm and an estimated average deviation of 3.6 ± 1.4 mm. The results revealed non-negligeable biases in user estimates likely attributed to depth perception inaccuracies. Still, the study concluded that BREAST+ is both accurate and considerably more efficient (∼6 times faster) when compared to the conventional perforator marking approach.</p>","PeriodicalId":37474,"journal":{"name":"Healthcare Technology Letters","volume":"11 6","pages":"301-306"},"PeriodicalIF":2.8000,"publicationDate":"2024-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11665790/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Healthcare Technology Letters","FirstCategoryId":"1085","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1049/htl2.12095","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENGINEERING, BIOMEDICAL","Score":null,"Total":0}
引用次数: 0

Abstract

Deep inferior epigastric artery perforator flap reconstruction is a common technique for breast reconstruction surgery in cancer patients. Preoperative planning typically depends on radiological reports and 2D images to help surgeons locate abdominal perforator vessels before surgery. Here, BREAST+, an augmented reality interface for the HoloLens 2, designed to facilitate accurate marking of perforator locations on the patients' skin and to seamlessly access relevant clinical data in the operating room is proposed. The system is evaluated in a controlled setting by conducting a user study with 27 medical students and 2 breast surgeons. Quantitative (marking error, task completion time, and number of task repetitions) and qualitative (perceived usability, perceived workload, user preference and user satisfaction) data are collected to assess BREAST+ performance during perforator marking. The average time taken to mark each perforator is 7.7 ± 6.5 s, with an average absolute error of 6.8 ± 2.6 mm and an estimated average deviation of 3.6 ± 1.4 mm. The results revealed non-negligeable biases in user estimates likely attributed to depth perception inaccuracies. Still, the study concluded that BREAST+ is both accurate and considerably more efficient (∼6 times faster) when compared to the conventional perforator marking approach.

Abstract Image

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
BREAST+:增强现实界面,加速DIEAP皮瓣重建手术的穿支标记。
腹壁深下动脉穿支瓣重建术是肿瘤患者乳房重建术的常用技术。术前计划通常依赖于放射学报告和2D图像,以帮助外科医生在手术前定位腹部穿支血管。本文提出了HoloLens 2的增强现实界面BREAST+,旨在帮助准确标记患者皮肤上的穿孔位置,并在手术室中无缝访问相关临床数据。通过对27名医科学生和2名乳房外科医生进行用户研究,在受控环境下对该系统进行评估。收集定量(标记错误、任务完成时间和任务重复次数)和定性(感知可用性、感知工作量、用户偏好和用户满意度)数据来评估BREAST+在穿孔器标记过程中的表现。标记每个穿孔的平均时间为7.7±6.5 s,平均绝对误差为6.8±2.6 mm,估计平均偏差为3.6±1.4 mm。结果显示,用户估计中不可忽略的偏差可能归因于深度感知不准确。尽管如此,该研究得出结论,与传统的射孔器标记方法相比,BREAST+既准确又高效(快6倍)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
Healthcare Technology Letters
Healthcare Technology Letters Health Professions-Health Information Management
CiteScore
6.10
自引率
4.80%
发文量
12
审稿时长
22 weeks
期刊介绍: Healthcare Technology Letters aims to bring together an audience of biomedical and electrical engineers, physical and computer scientists, and mathematicians to enable the exchange of the latest ideas and advances through rapid online publication of original healthcare technology research. Major themes of the journal include (but are not limited to): Major technological/methodological areas: Biomedical signal processing Biomedical imaging and image processing Bioinstrumentation (sensors, wearable technologies, etc) Biomedical informatics Major application areas: Cardiovascular and respiratory systems engineering Neural engineering, neuromuscular systems Rehabilitation engineering Bio-robotics, surgical planning and biomechanics Therapeutic and diagnostic systems, devices and technologies Clinical engineering Healthcare information systems, telemedicine, mHealth.
期刊最新文献
Differential analysis of brain functional network parameters in MHE patients The Feasibility of Ambulatory Heart Rate Variability Monitoring in Non-Suicidal Self-Injury Signal-quality-aware multisensor fusion for atrial fibrillation detection Deep regression 2D-3D ultrasound registration for liver motion correction in focal tumour thermal ablation Writing the Signs: An Explainable Machine Learning Approach for Alzheimer's Disease Classification from Handwriting
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1