Browsing by Subject "Robotics"

Now showing 1 - 7 of 7
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    A Comparison of Robotic, Laparoscopic and Open Partial Nephrectomy
    (Society of Laparoendoscopic Surgeons, 2012) Lucas, Steven M.; Mellon, Matthew J.; Erntsberger, Luke; Sundaram, Chandru P.; Urology, School of Medicine
    Introduction: Comparison of treatments for partial nephrectomy is limited by case selection. We compared robotic (RPN), laparoscopic (LPN), and open partial nephrectomy (OPN), controlling for tumor size, patient age, sex, and nephrometry score. Methods: RPN, LPN, and OPN procedures between March 2003 and March 2010 were reviewed. All RPN and LPN were included, and 2 OPN were matched for each RPN in tumor size (±0.5cm), patient age (±10 y), sex, and nephrometry score. Perioperative outcomes were compared. Results: Ninety-six partial nephrectomy procedures were reviewed: 27 RPN, 15 LPN, and 54 OPN. RPN, LPN, and OPN had similar median tumor size (2.4, 2.2, and 2.3cm, respectively), nephrometry score (6.0 each), and preoperative glomerular filtration rate (71.5, 84.6, and 77.0 mL/min/1.73m(2), respectively). Blood loss was higher for OPN (250 mL) than for RPN or LPN (100 mL), P < 0.001. Operative time was shorter in OPN (147 min) than in RPN (190 min) or LPN (195 min), P < .001. Median warm ischemia time was shorter for OPN (12.0 min) than for RPN (25.0 min) or LPN (29.5 min), P Kt; .05. Cold ischemia time for OPN was 25.0 min. A 10% glomerular filtration rate decline occurred in 10 RPN, 5 LPN, and 29 OPN cases (P < .252). Median hospital stay for LPN and RPN was 2.0 d versus 3.0 d for OPN (P < .001). Urine leak occurred in 1 RPN and 3 OPN cases. Postoperative complications occurred in 4 RPN (3 were Clavien grade 2 or less), 1 LPN (grade 1), and 7 OPN (6 were grade 2 or less) cases. Conclusion: Renal function preservation and complications are similar for each treatment modality. OPN offers faster operative and ischemia times at the expense of greater blood loss and hospital stay.
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    Comparison of Robot-Assisted Nephrectomy with Laparoscopic and Hand-Assisted Laparoscopic Nephrectomy
    (Society of Laparoscopic & Robotic Surgeons, 2010-07) Boger, Michelle; Lucas, Steven M.; Popp, Sara C.; Gardner, Thomas A.; Sundaram, Chandru P.; Urology, School of Medicine
    Objective: To compare the initial perioperative outcomes of our robot-assisted laparoscopic nephrectomies with laparoscopic and hand-assisted nephrectomies performed by 2 experienced laparoscopic surgeons. Patients and Methods: We retrospectively evaluated all patients who underwent laparoscopic (LN), hand-assisted (HALN), and robot-assisted laparoscopic nephrectomy (RALN) for benign and malignant diseases between August 2006 and December 2008. Data collected included patient age, body mass index, operative times, estimated blood loss, complications, and hospital stay. Radical nephrectomy was performed for renal neoplasms, and simple nephrectomy was performed for suspected benign diseases. In addition, average direct costs and total costs were calculated for each laparoscopic approach. Results: Forty-six patients underwent LN, 20 underwent HALN, and 13 underwent RALN. The median operative time was 171, 210, and 168 minutes, respectively. LN, HALN, and RALN groups had similar median EBL [(100mL (IQR=113mL), 100mL (IQR=150mL), and 100mL (IQR=125mL); P=0.695], length of hospital stay [2.0d (IQR=1.0d), 3.0d (IQR=2.0d), and 2.0d (IQR=3.0d); P=0.233], and postoperative morphine equivalent analgesic requirements [33mg (IQR=43mg), 45mg (IQR=50mg), and 30mg (IQR=16mg); P=0.766]. Three patients (6%) in the LN group had complications, 2 (10%) in the HALN group had complications, and 4 (30%) in the RALN group had complications. The average total direct operating room costs were $5,500, $6,979, and $6,869 for the LN, HALN, and RALN groups, respectively. Conclusions: Early experience with robotic assistance for radical and simple nephrectomy offers no significant advantage over traditional laparoscopic or hand-assisted approaches. It was also more costly.
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    Compassionate Care with Autonomous AI Humanoid Robots in Future Healthcare Delivery: A Multisensory Simulation of Next-Generation Models
    (MDPI, 2024-11-11) Hernandez, Joannes Paulus Tolentino; School of Nursing
    The integration of AI and robotics in healthcare raises concerns, and additional issues regarding autonomous systems are anticipated. Effective communication is crucial for robots to be seen as "caring", necessitating advanced mechatronic design and natural language processing (NLP). This paper examines the potential of humanoid robots to autonomously replicate compassionate care. The study employs computational simulations using mathematical and agent-based modeling to analyze human-robot interactions (HRIs) surpassing Tetsuya Tanioka's TRETON. It incorporates stochastic elements (through neuromorphic computing) and quantum-inspired concepts (through the lens of Martha Rogers' theory), running simulations over 100 iterations to analyze complex behaviors. Multisensory simulations (visual and audio) demonstrate the significance of "dynamic communication", (relational) "entanglement", and (healthcare system and robot's function) "superpositioning" in HRIs. Quantum and neuromorphic computing may enable humanoid robots to empathetically respond to human emotions, based on Jean Watson's ten caritas processes for creating transpersonal states. Autonomous AI humanoid robots will redefine the norms of "caring". Establishing "pluralistic agreements" through open discussions among stakeholders worldwide is necessary to align innovations with the values of compassionate care within a "posthumanist" framework, where the compassionate care provided by Level 4 robots meets human expectations. Achieving compassionate care with autonomous AI humanoid robots involves translating nursing, communication, computer science, and engineering concepts into robotic care representations while considering ethical discourses through collaborative efforts. Nurses should lead the design and implementation of AI and robots guided by "technological knowing" in Rozzano Locsin's TCCN theory.
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    Distributed Monocular SLAM for Indoor Map Building
    (Hindawi, 2017) Egodagamage, Ruwan; Tuceryan, Mihran; Computer and Information Science, School of Science
    Utilization and generation of indoor maps are critical elements in accurate indoor tracking. Simultaneous Localization and Mapping (SLAM) is one of the main techniques for such map generation. In SLAM an agent generates a map of an unknown environment while estimating its location in it. Ubiquitous cameras lead to monocular visual SLAM, where a camera is the only sensing device for the SLAM process. In modern applications, multiple mobile agents may be involved in the generation of such maps, thus requiring a distributed computational framework. Each agent can generate its own local map, which can then be combined into a map covering a larger area. By doing so, they can cover a given environment faster than a single agent. Furthermore, they can interact with each other in the same environment, making this framework more practical, especially for collaborative applications such as augmented reality. One of the main challenges of distributed SLAM is identifying overlapping maps, especially when relative starting positions of agents are unknown. In this paper, we are proposing a system having multiple monocular agents, with unknown relative starting positions, which generates a semidense global map of the environment.
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    From the Dexterous Surgical Skill to the Battlefield-A Robotics Exploratory Study
    (Oxford University Press, 2021) Gonzalez, Glebys T.; Kaur, Upinder; Rahma, Masudur; Venkatesh, Vishnunandan; Sanchez, Natalia; Hager, Gregory; Xue, Yexiang; Voyles, Richard; Wachs, Juan; Surgery, School of Medicine
    Introduction: Short response time is critical for future military medical operations in austere settings or remote areas. Such effective patient care at the point of injury can greatly benefit from the integration of semi-autonomous robotic systems. To achieve autonomy, robots would require massive libraries of maneuvers collected with the goal of training machine learning algorithms. Although this is attainable in controlled settings, obtaining surgical data in austere settings can be difficult. Hence, in this article, we present the Dexterous Surgical Skill (DESK) database for knowledge transfer between robots. The peg transfer task was selected as it is one of the six main tasks of laparoscopic training. In addition, we provide a machine learning framework to evaluate novel transfer learning methodologies on this database. Methods: A set of surgical gestures was collected for a peg transfer task, composed of seven atomic maneuvers referred to as surgemes. The collected Dexterous Surgical Skill dataset comprises a set of surgical robotic skills using the four robotic platforms: Taurus II, simulated Taurus II, YuMi, and the da Vinci Research Kit. Then, we explored two different learning scenarios: no-transfer and domain-transfer. In the no-transfer scenario, the training and testing data were obtained from the same domain; whereas in the domain-transfer scenario, the training data are a blend of simulated and real robot data, which are tested on a real robot. Results: Using simulation data to train the learning algorithms enhances the performance on the real robot where limited or no real data are available. The transfer model showed an accuracy of 81% for the YuMi robot when the ratio of real-tosimulated data were 22% to 78%. For the Taurus II and the da Vinci, the model showed an accuracy of 97.5% and 93%, respectively, training only with simulation data. Conclusions: The results indicate that simulation can be used to augment training data to enhance the performance of learned models in real scenarios. This shows potential for the future use of surgical data from the operating room in deployable surgical robots in remote areas.
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    Kinematic changes following robotic-assisted upper extremity rehabilitation in children with hemiplegia : dosage effects on movement time
    (2018-04-30) Cardinal, Ryan Edward; Altenburger, Peter; Fuchs, Robyn; Massie, Crystal; Warden, Stuart
    Background: Rehabilitation Robotics (RR) has become a more widely used and better understood treatment intervention and research tool in the last 15 years. Traditional research involves pre and post-test outcomes, making it difficult to analyze changes in behavior during the treatment process. Harnessing kinematics captured throughout each treatment allows motor learning to be quantified and questions of application and dosing to be answered. Objective: The aims of this secondary analysis were: (i) to investigate the impact of treatment presentation during RR on upper extremity movement time (mt) in children with hemiplegic cerebral palsy (CP) and (ii) to investigate the impact of training structure (dose and intensity) on mt in children with CP participating in RR. Methods: Subjects completed 16 intervention sessions of RR (2 x week; 8 weeks) with a total of 1,024 repetitions of movement per session and three assessments: pre, post and 6 month f/u. During each assessment and intervention, subjects completed “one-way record” assessments tracking performance on a planar task without robotic assistance. Kinematics from these records were extracted to assess subject performance over the course of and within sessions. Results: For all participants, a significant decrease in mt was found at post-test and follow-up. No significant differences were found in mt for age, severity or group placement. A significant interaction was found between treatment day, block and group (p = .033). Significant mt differences were found between the three blocks of intervention within individual days (p = .001). Specifically, significant differences were found over the last block of treatment (p = .032) and between successive treatment days (p = .001). Conclusion: The results indicate that for children with CP participating in RR, the number of repetitions per session is important. We hypothesized that children’s performance would plateau during a treatment day as attention waned, the opposite proved to be true. Despite the high-number of repetitions and associated cognitive demand, subjects’ performance actually trended upwards throughout the 1,024 repetitions suggesting that children were able to tolerate and learn from a high volume of repetitions.
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    The learning curve and factors affecting warm ischemia time during robot-assisted partial nephrectomy
    (Wolters Kluwer, 2015-07) Dube, Hitesh; Bahler, Clinton D.; Sundaram, Chandru P.; Department of Urology, IU School of Medicine
    INTRODUCTION: The learning curve for robotic partial nephrectomy was investigated for an experienced laparoscopic surgeon and factors associated with warm ischemia time (WIT) were assessed. MATERIALS AND METHODS: Between 2007 and 2014, one surgeon completed 171 procedures. Operative time, blood loss, complications and ischemia time were examined to determine the learning curve. The learning curve was defined as the number of procedures needed to reach the targeted goal for WIT, which most recently was 20 min. Statistical analyses including multivariable regression analysis and matching were performed. RESULTS: Comparing the first 30 to the last 30 patients, mean ischemia time (23.0-15.2 min, P < 0.01) decreased while tumor size (2.4-3.4 cm, P = 0.02) and nephrometry score (5.9-7.0, P = 0.02) increased. Body mass index (P = 0.87), age (P = 0.38), complication rate (P = 0.16), operating time (P = 0.78) and estimated blood loss (P = 0.98) did not change. Decreases in ischemia time corresponded with revised goals in 2011 and early vascular unclamping with the omission of cortical renorrhaphy in selected patients. A multivariable analysis found nephrometry score, tumor diameter, cortical renorrhaphy and year of surgery to be significant predictors of WIT. CONCLUSIONS: Adoption of robotic assistance for a surgeon experienced with laparoscopic surgery was associated with low complication rates even during the initial cases of robot-assisted partial nephrectomy. Ischemia time decreased while no significant changes in blood loss, operating time or complications were seen. The largest decrease in ischemia time was associated with adopting evidence-based goals and new techniques, and was not felt to be related to a learning curve.