This paper discusses non-traditional trademarks and design patents as essential Intellectual Property assets for medical device companies. The paper presents a strategy for layering the time-limited protection offered by design patents with the perpetual protection afforded to trademarks as an effective strategy for permanent exclusivity of non-functional aspects of medical device products.
{"title":"NON-TRADITIONAL TRADEMARK AND DESIGN PATENT STRATEGIES FOR MEDICAL DEVICES","authors":"Steve Baird, G. Smock, Draeke Weseman, Jake Abdo","doi":"10.1115/dmd2023-8964","DOIUrl":"https://doi.org/10.1115/dmd2023-8964","url":null,"abstract":"\u0000 This paper discusses non-traditional trademarks and design patents as essential Intellectual Property assets for medical device companies. The paper presents a strategy for layering the time-limited protection offered by design patents with the perpetual protection afforded to trademarks as an effective strategy for permanent exclusivity of non-functional aspects of medical device products.","PeriodicalId":325836,"journal":{"name":"2023 Design of Medical Devices Conference","volume":"97 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":"124678929","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}
Marcus Tatum, Steven A. Long, Don Anderson, G. Thomas
We describe a novel surgical simulator designed to train and assess an orthopaedic residents surgical skill on the tasks of fracture reduction and surgical wire navigation. We use a unique approach of combining accurate surgical models created using patient CT data along with a 3D tracking system that allows for realistic, real-time imaging along with automated performance tracking and benchmarking. Additionally, we have devised a method for tracking the surgical wire associated with the procedure to allow for a complete simulation of reducing and pinning a pediatric elbow fracture.
{"title":"CREATING A FRACTURE REDUCTION AND WIRE NAVIGATION SIMULATOR FOR ORTHOPAEDIC SKILLS TRAINING AND ASSESSMENT","authors":"Marcus Tatum, Steven A. Long, Don Anderson, G. Thomas","doi":"10.1115/dmd2023-4204","DOIUrl":"https://doi.org/10.1115/dmd2023-4204","url":null,"abstract":"\u0000 We describe a novel surgical simulator designed to train and assess an orthopaedic residents surgical skill on the tasks of fracture reduction and surgical wire navigation. We use a unique approach of combining accurate surgical models created using patient CT data along with a 3D tracking system that allows for realistic, real-time imaging along with automated performance tracking and benchmarking. Additionally, we have devised a method for tracking the surgical wire associated with the procedure to allow for a complete simulation of reducing and pinning a pediatric elbow fracture.","PeriodicalId":325836,"journal":{"name":"2023 Design of Medical Devices Conference","volume":"2 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":"130735272","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}
L. Thomas, Seth Jarvis, Laura Wenger, Tara Newberry, Andre Muelenaer Jr., C. Arena
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.
{"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}
The evolution of blood flow is vital in understanding the pathogenesis of brain aneurysms. Several past studies have shown evidence for a turbulent inflow jet at the aneurysm neck. Even though there is a great need for analyzing inflow jet dynamics in clinical practice, data summarized in non-invasive modalities such as Magnetic Resonance Imaging or Computed Tomography are usually limited in spatial and temporal resolutions, and thus cannot account for the hemodynamics. In this work, Dynamic Mode Decomposition (DMD) is used to pinpoint the dominant modes of the inflow jet in patient-specific models of internal sidewall aneurysms utilizing high-resolution data from Computational Fluid Dynamics. The purpose of this thesis is to prove that the dynamic modes are not only governed by the hemodynamics of the parent artery but by the inflow jet interaction with the distal wall. Our work indicates that DMD is an essential tool for analyzing blood flow patterns of brain aneurysms and is a promising tool to be used in in-vivo context.
{"title":"SURROGATE MODELS OF BLOOD FLOW DYNAMICS IN BRAIN ANEURYSMS USING DYNAMIC MODE DECOMPOSITION","authors":"Trung Le, Tam Nguyen, Phat K. Huynh, T. Le","doi":"10.1115/dmd2023-8522","DOIUrl":"https://doi.org/10.1115/dmd2023-8522","url":null,"abstract":"\u0000 The evolution of blood flow is vital in understanding the pathogenesis of brain aneurysms. Several past studies have shown evidence for a turbulent inflow jet at the aneurysm neck. Even though there is a great need for analyzing inflow jet dynamics in clinical practice, data summarized in non-invasive modalities such as Magnetic Resonance Imaging or Computed Tomography are usually limited in spatial and temporal resolutions, and thus cannot account for the hemodynamics. In this work, Dynamic Mode Decomposition (DMD) is used to pinpoint the dominant modes of the inflow jet in patient-specific models of internal sidewall aneurysms utilizing high-resolution data from Computational Fluid Dynamics. The purpose of this thesis is to prove that the dynamic modes are not only governed by the hemodynamics of the parent artery but by the inflow jet interaction with the distal wall. Our work indicates that DMD is an essential tool for analyzing blood flow patterns of brain aneurysms and is a promising tool to be used in in-vivo context.","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":"129177085","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}
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
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.
{"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}
Rutuja M. Shinde, Manga Manga, Neha Muthavarapu, K. Gopalakrishnan, Christopher A. Aakre, Alexander J. Ryu, S. P. Arunachalam
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.
{"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}
Madison Reddie, Gari Eberly, Aviva Jesse Levi, Diego Quevedo-Moreno, Keegan L. Mendez, E. Roche
Blood clots originating in the left atrial appendage (LAA) are the leading cause of ischemic stroke in patients with nonvalvular atrial fibrillation (AF). Complications from and contraindications to oral anticoagulants (OACs), in addition to the recent successes of endocardial LAA closure devices, have driven increased interest in mechanical LAA occlusion. However, current devices are limited in their abilities to accommodate diverse LAA anatomies, motivating the development of a novel endocardial LAA occluder that supports more anatomical variability. We present the design of an in-situ expandable plug as well as an accompanying pacifier module for LAA occlusion. The final design accommodates LAA diameter ranges of 14 millimeters for each device size (10-24mm and 24-38mm), double that of any approved device. This adaptability can help to overcome imperfect pre-procedural imaging and suboptimal device fit. Benchtop tug and leak tests demonstrate the stability and sealing capacities of the design.
{"title":"UMBRELAA: DESIGN OF A VARIABLE-SIZED LEFT ATRIAL APPENDAGE OCCLUSION DEVICE FOR STROKE PREVENTION","authors":"Madison Reddie, Gari Eberly, Aviva Jesse Levi, Diego Quevedo-Moreno, Keegan L. Mendez, E. Roche","doi":"10.1115/dmd2023-1984","DOIUrl":"https://doi.org/10.1115/dmd2023-1984","url":null,"abstract":"\u0000 Blood clots originating in the left atrial appendage (LAA) are the leading cause of ischemic stroke in patients with nonvalvular atrial fibrillation (AF). Complications from and contraindications to oral anticoagulants (OACs), in addition to the recent successes of endocardial LAA closure devices, have driven increased interest in mechanical LAA occlusion. However, current devices are limited in their abilities to accommodate diverse LAA anatomies, motivating the development of a novel endocardial LAA occluder that supports more anatomical variability. We present the design of an in-situ expandable plug as well as an accompanying pacifier module for LAA occlusion. The final design accommodates LAA diameter ranges of 14 millimeters for each device size (10-24mm and 24-38mm), double that of any approved device. This adaptability can help to overcome imperfect pre-procedural imaging and suboptimal device fit. Benchtop tug and leak tests demonstrate the stability and sealing capacities of the design.","PeriodicalId":325836,"journal":{"name":"2023 Design of Medical Devices Conference","volume":"504 ","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114001400","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}
Hang-Ling Wu, Dailen C. Brown, Scarlett R. Miller, Jason E. Moore
A machine learning (ML) object detection algorithm was developed to replace the original color-based image detection algorithm for the Dynamic Haptic Robotic Trainer Plus (DHRT+). This image recognition system was used for medical training in Central Venous Catheterization (CVC). This image tracking allows for the training system to provide accurate performance feedback to the user during the training process. The ML object detection algorithm was developed and evaluated using training data. The results indicate that increasing the training data set improves the detection system’s accuracy. The system was found to have an overall precision rate of 90.9% and a recall rate of 81.69%. This new ML model will be implemented into the DHRT+ system and used to train medical residents.
开发了一种机器学习(ML)目标检测算法，以取代原来的基于颜色的图像检测算法，用于Dynamic Haptic Robotic Trainer Plus (DHRT+)。该图像识别系统用于中心静脉置管(CVC)的医学培训。这种图像跟踪允许训练系统在训练过程中向用户提供准确的性能反馈。使用训练数据开发并评估了ML对象检测算法。结果表明，增加训练数据集可以提高检测系统的准确率。该系统的总体准确率为90.9%，召回率为81.69%。这个新的ML模型将被应用到DHRT+系统中，并用于培训住院医师。
{"title":"DEPLOYING COMPUTER VISION DETECTION METHOD IN MEDICAL SIMULATION TRAINING USING MACHINE LEARNING","authors":"Hang-Ling Wu, Dailen C. Brown, Scarlett R. Miller, Jason E. Moore","doi":"10.1115/dmd2023-3868","DOIUrl":"https://doi.org/10.1115/dmd2023-3868","url":null,"abstract":"\u0000 A machine learning (ML) object detection algorithm was developed to replace the original color-based image detection algorithm for the Dynamic Haptic Robotic Trainer Plus (DHRT+). This image recognition system was used for medical training in Central Venous Catheterization (CVC). This image tracking allows for the training system to provide accurate performance feedback to the user during the training process. The ML object detection algorithm was developed and evaluated using training data. The results indicate that increasing the training data set improves the detection system’s accuracy. The system was found to have an overall precision rate of 90.9% and a recall rate of 81.69%. This new ML model will be implemented into the DHRT+ system and used to train medical residents.","PeriodicalId":325836,"journal":{"name":"2023 Design of Medical Devices Conference","volume":"1098 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":"122913923","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}
Samson Galvin, Samantha Scarpinella, Dr. Shawn Safford, Dr. Scarlett Miller, Dr. Jason Moore
Laparoscopic surgery is a common minimally invasive procedure that is performed through small ports in the abdominal wall. The ports are created with tools called trocars that are used to inflate the abdominal cavity and are used as gateways for surgical tools. There is limited training on trocar insertion for novice surgeons and it is a crucial step in the laparoscopic procedure. There are several challenges associated with placing and inserting trocars in the abdominal area. In this paper, experimental data is used to create a mathematical model for the expected trocar force, which had an average error of 3.67%. A novel trocar training system is proposed to effectively simulate the haptic feeling of inserting a trocar into the abdomen.
{"title":"LAPAROSCOPIC TROCAR INSERTION FORCE SIMULATION","authors":"Samson Galvin, Samantha Scarpinella, Dr. Shawn Safford, Dr. Scarlett Miller, Dr. Jason Moore","doi":"10.1115/dmd2023-4027","DOIUrl":"https://doi.org/10.1115/dmd2023-4027","url":null,"abstract":"\u0000 Laparoscopic surgery is a common minimally invasive procedure that is performed through small ports in the abdominal wall. The ports are created with tools called trocars that are used to inflate the abdominal cavity and are used as gateways for surgical tools. There is limited training on trocar insertion for novice surgeons and it is a crucial step in the laparoscopic procedure. There are several challenges associated with placing and inserting trocars in the abdominal area. In this paper, experimental data is used to create a mathematical model for the expected trocar force, which had an average error of 3.67%. A novel trocar training system is proposed to effectively simulate the haptic feeling of inserting a trocar into the abdomen.","PeriodicalId":325836,"journal":{"name":"2023 Design of Medical Devices Conference","volume":"27 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":"125955527","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}
Dong-Ho Lee, Joonhwan Kim, Jung-Gil Han, Hyeon Seo, J. Lee, Sung Yong Cho, H. K. Park, D. Kwon
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.
{"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}
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