2023 Design of Medical Devices Conference最新文献

NON-TRADITIONAL TRADEMARK AND DESIGN PATENT STRATEGIES FOR MEDICAL DEVICES 医疗器械非传统商标与外观设计专利策略

2023 Design of Medical Devices Conference Pub Date : 2023-04-17 DOI: 10.1115/dmd2023-8964

Steve Baird, G. Smock, Draeke Weseman, Jake Abdo

引用次数: 0

CREATING A FRACTURE REDUCTION AND WIRE NAVIGATION SIMULATOR FOR ORTHOPAEDIC SKILLS TRAINING AND ASSESSMENT 创建骨折复位和导线导航模拟器，用于骨科技能培训和评估

2023 Design of Medical Devices Conference Pub Date : 2023-04-17 DOI: 10.1115/dmd2023-4204

Marcus Tatum, Steven A. Long, Don Anderson, G. Thomas

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TOWARDS THE DEVELOPMENT OF A WEARABLE DEVICE TO MANAGE UPPER EXTREMITY LYMPHEDEMA 开发一种可穿戴设备来治疗上肢淋巴水肿

2023 Design of Medical Devices Conference Pub Date : 2023-04-17 DOI: 10.1115/dmd2023-6551

L. Thomas, Seth Jarvis, Laura Wenger, Tara Newberry, Andre Muelenaer Jr., C. Arena

{"title":"TOWARDS THE DEVELOPMENT OF A WEARABLE DEVICE TO MANAGE UPPER EXTREMITY LYMPHEDEMA","authors":"L. Thomas, Seth Jarvis, Laura Wenger, Tara Newberry, Andre Muelenaer Jr., C. Arena","doi":"10.1115/dmd2023-6551","DOIUrl":"https://doi.org/10.1115/dmd2023-6551","url":null,"abstract":"\u0000 3 to 5 million Americans are affected by lymphedema and 140 to 200 million patients worldwide [1,2]. The chronic, progressive condition is only treatable, but not curable, leaving patients with debilitating symptoms that can significantly impair their quality of life [3]. Treatments include manual lymphatic drainage (MLD) massage and compression garments, often applied sequentially. MLD is limited by consistent access to a qualified lymphedema therapist. Our wearable device utilizes vibration to mimic the patterns of massage in MLD. The long-term goal is to enable at-home maintenance therapy between MLD sessions and improve the overall management of lymphedema. Acceleration, frequency, and sound levels of our motors were tested and compared to current clinical solutions. Acceleration testing was conducted by placing an accelerometer in a tissue model at varying distances to collect acceleration data at different duty cycles. Frequency data was calculated in MATLAB using FFT. Acceleration, sound frequency, and sound level of our motors were all within a close range of the values collected from clinical tools. The acceleration frequency of the motors rose slightly with an increased duty cycle, but did not exceed the smaller clinical tool frequency values. Based on our testing, the motors used in our device are comparable to clinical tools currently used by certified lymphedema therapists.","PeriodicalId":325836,"journal":{"name":"2023 Design of Medical Devices Conference","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130926325","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

SURROGATE MODELS OF BLOOD FLOW DYNAMICS IN BRAIN ANEURYSMS USING DYNAMIC MODE DECOMPOSITION 采用动态模态分解的脑动脉瘤血流动力学替代模型

2023 Design of Medical Devices Conference Pub Date : 2023-04-17 DOI: 10.1115/dmd2023-8522

Trung Le, Tam Nguyen, Phat K. Huynh, T. Le

引用次数: 0

ON THE DESIGN OF A NOVEL PHONOENTEROGRAM SENSING DEVICE USING AI ASSISTED COMPUTER-AIDED AUSCULTATION 一种新型人工智能辅助计算机听诊声图传感装置的设计

2023 Design of Medical Devices Conference Pub Date : 2023-04-17 DOI: 10.1115/dmd2023-7977

S. Damani, D. Damani, Renisha Redij, Arshia K. Sethi, Pratyusha Muddaloor, Anoushka Kapoor, Anjali Rajagopal, K. Gopalakrishnan, X. J. Wang, V. Chedid, Alexander J. Ryu, Christopher A. Aakre, S. P. Arunachalam

{"title":"ON THE DESIGN OF A NOVEL PHONOENTEROGRAM SENSING DEVICE USING AI ASSISTED COMPUTER-AIDED AUSCULTATION","authors":"S. Damani, D. Damani, Renisha Redij, Arshia K. Sethi, Pratyusha Muddaloor, Anoushka Kapoor, Anjali Rajagopal, K. Gopalakrishnan, X. J. Wang, V. Chedid, Alexander J. Ryu, Christopher A. Aakre, S. P. Arunachalam","doi":"10.1115/dmd2023-7977","DOIUrl":"https://doi.org/10.1115/dmd2023-7977","url":null,"abstract":"\u0000 Bowel sounds have been previously used to study intestinal motility and overall digestive health in various clinical settings. However, the blurred definition of bowel sounds and their subtypes, limited resources for interpretation, poor sensitivity, and low positive predictive value led to their restricted utility. Recent advances in artificial intelligence and machine learning have steered interest in developing unique tools using the phonoenterogram to analyze diverse bowel sounds. In our study, bowel sounds were recorded from eight healthy volunteers using the Eko Duo stethoscope. A novel deep-learning algorithm was designed to classify the recordings into baseline or prominent bowel sounds. A total of 11,210 data points (5,605 balanced sounds) were used to train and test the model to yield an accuracy of 0.895, a precision of 0.890, and a recall of 0.854 reflecting successful segregation of these sounds into respective groups. More extensive studies enrolling healthy and diseased subjects with a device specifically tailored to record bowel sounds are needed to generalize these results and determine their application in the patient population.","PeriodicalId":325836,"journal":{"name":"2023 Design of Medical Devices Conference","volume":"238 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122168413","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

BLOOD PRESSURE PREDICTION FROM PHOTOPLETHYSMOGRAM SIGNAL USING ARTIFICIAL INTELLIGENCE 利用人工智能从光容积图信号预测血压

2023 Design of Medical Devices Conference Pub Date : 2023-04-17 DOI: 10.1115/dmd2023-2769

Rutuja M. Shinde, Manga Manga, Neha Muthavarapu, K. Gopalakrishnan, Christopher A. Aakre, Alexander J. Ryu, S. P. Arunachalam

{"title":"BLOOD PRESSURE PREDICTION FROM PHOTOPLETHYSMOGRAM SIGNAL USING ARTIFICIAL INTELLIGENCE","authors":"Rutuja M. Shinde, Manga Manga, Neha Muthavarapu, K. Gopalakrishnan, Christopher A. Aakre, Alexander J. Ryu, S. P. Arunachalam","doi":"10.1115/dmd2023-2769","DOIUrl":"https://doi.org/10.1115/dmd2023-2769","url":null,"abstract":"\u0000 Blood pressure measurement in current medical practice relies on manual methods with the most widely used modality being sphygmomanometers. Utilizing the principle of Photoplethysmography, it is possible to provide an accurate reading of one’s blood pressure through light signals and photodetector devices. This research paper introduces a new Artificial Intelligence driven approach to predict Blood pressure levels and classify them according to the updated ACC (American College of Cardiology) criteria as Normal, Elevated, Stage I, and II Hypertension from the given PPG signal values using Machine Learning Models. This research paper aims to accurately read the Systolic and Diastolic Blood Pressure using Artificial Intelligence, place them into the correct value bins and further prove that the blood pressure values differ based on different skin tones in different light wavelengths such as red, infrared, and green. Machine Learning models such as the Support Vector Machine have shown an accuracy of 70.58% for Systolic Blood Pressure and Decision Tree with an accuracy of 74.4% for Diastolic Blood Pressure classification. The models used in this research are Support Vector Machine, Decision Tree and K-Nearest Neighbor. This research study has future applications and extensions to predict blood pressure levels for patients with different skin tones under different light radiations and PPG signal readings. Neural Network models will be developed to compare the blood predictions from this work.","PeriodicalId":325836,"journal":{"name":"2023 Design of Medical Devices Conference","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124927897","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

UMBRELAA: DESIGN OF A VARIABLE-SIZED LEFT ATRIAL APPENDAGE OCCLUSION DEVICE FOR STROKE PREVENTION 伞:用于预防脑卒中的可变尺寸左心耳闭塞装置的设计

2023 Design of Medical Devices Conference Pub Date : 2023-04-17 DOI: 10.1115/dmd2023-1984

Madison Reddie, Gari Eberly, Aviva Jesse Levi, Diego Quevedo-Moreno, Keegan L. Mendez, E. Roche

引用次数: 0

DEPLOYING COMPUTER VISION DETECTION METHOD IN MEDICAL SIMULATION TRAINING USING MACHINE LEARNING 利用机器学习将计算机视觉检测方法应用于医学模拟训练

2023 Design of Medical Devices Conference Pub Date : 2023-04-17 DOI: 10.1115/dmd2023-3868

Hang-Ling Wu, Dailen C. Brown, Scarlett R. Miller, Jason E. Moore

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LAPAROSCOPIC TROCAR INSERTION FORCE SIMULATION 腹腔镜套管针插入力模拟

2023 Design of Medical Devices Conference Pub Date : 2023-04-17 DOI: 10.1115/dmd2023-4027

Samson Galvin, Samantha Scarpinella, Dr. Shawn Safford, Dr. Scarlett Miller, Dr. Jason Moore

引用次数: 0

ZAMENIX™ R, ROBOTIC-ASSISTED RETROGRADE INTRARENAL SURGERY SYSTEM FOR RENAL STONE REMOVAL AND ITS EFFICACY AND SAFETY EVALUATION Zamenix™r，机器人辅助逆行肾内手术系统去除肾结石及其疗效和安全性评价

2023 Design of Medical Devices Conference Pub Date : 2023-04-17 DOI: 10.1115/dmd2023-7694

Dong-Ho Lee, Joonhwan Kim, Jung-Gil Han, Hyeon Seo, J. Lee, Sung Yong Cho, H. K. Park, D. Kwon

{"title":"ZAMENIX™ R, ROBOTIC-ASSISTED RETROGRADE INTRARENAL SURGERY SYSTEM FOR RENAL STONE REMOVAL AND ITS EFFICACY AND SAFETY EVALUATION","authors":"Dong-Ho Lee, Joonhwan Kim, Jung-Gil Han, Hyeon Seo, J. Lee, Sung Yong Cho, H. K. Park, D. Kwon","doi":"10.1115/dmd2023-7694","DOIUrl":"https://doi.org/10.1115/dmd2023-7694","url":null,"abstract":"\u0000 We introduce Zamenix™ R, a novel robotic retrograde intrarenal surgery (RIRS) system that can remotely control a ureteroscope, laser, and stone basket. The efficacy and safety of the system were explored in an in vivo comparative pilot study using a porcine model and a multicenter, prospective, single-arm, pivotal human clinical trial. In the in-vivo test, 10 pigs were randomly divided into two groups: conventional RIRS and robotic RIRS. Three surgeons with different RIRS proficiency participated and they performed two tasks: 1) Stone retrieval test, then followed by 2) Stone fragmentation and retrieval test. In the clinical study, forty-seven adult patients who have one or more stones with a maximum size of 5-30 mm were recruited. The stone-free rate was 93.5%. The postoperative complication rate was 6.5% with three cases of Grade II urinary tract infection (Clavien-Dindo Classification). The experimental results demonstrated the feasibility and comparable safety to conventional RIRS with improved operator’s fatigue and radiation exposure.","PeriodicalId":325836,"journal":{"name":"2023 Design of Medical Devices Conference","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121585052","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0