A5069354612- Surgical Simulation and Training
- Cardiac, Anesthesia and Surgical Outcomes
- Anatomy and Medical Technology
- Artificial Intelligence in Healthcare and Education
- Hemodynamic Monitoring and Therapy
- Human Pose and Action Recognition
- Colorectal Cancer Screening and Detection
- Medical Imaging and Analysis
- Soft Robotics and Applications
- Augmented Reality Applications
- Robotic Mechanisms and Dynamics
- Biomedical and Engineering Education
- Advanced X-ray and CT Imaging
- Control and Dynamics of Mobile Robots
- Explainable Artificial Intelligence (XAI)
- Multimodal Machine Learning Applications
- Robotic Path Planning Algorithms
- Generative Adversarial Networks and Image Synthesis
- Digital Imaging in Medicine
- Advanced Vision and Imaging
- Gastrointestinal Bleeding Diagnosis and Treatment
- Human Motion and Animation
- Enhanced Recovery After Surgery
- Radiomics and Machine Learning in Medical Imaging
Intuitive Surgical (United States)
2020
Georgia Institute of Technology
2015-2019
Atlanta Technical College
2017-2018
Lahore University of Management Sciences
2011
Owing to recent advances in machine learning and the ability harvest large amounts of data during robotic-assisted surgeries, surgical science is ripe for foundational work. We present a dataset videos their accompanying labels this purpose. describe how was collected some its unique attributes. Multiple example problems are outlined. Although curated particular set scientific challenges (in an paper), it general enough be used broad range questions. Our hope that exposes larger community...
Most evaluations of surgical workflow or surgeon skill use simple, descriptive statistics (e.g., time) across whole procedures, thereby deemphasizing critical steps and potentially obscuring inefficiencies deficiencies. In this work, we examine off-line, temporal clustering methods that chunk training procedures into clinically relevant tasks during robot-assisted surgery.We collected system kinematics events data from nine surgeons performing five different on a porcine model using the da...
A surgeon's technical skills are an important factor in delivering optimal patient care. Most existing methods to estimate remain subjective and resource intensive. Robotic-assisted surgery (RAS) provides a unique opportunity develop objective metrics using key elements of intraoperative surgeon behavior which can be captured unobtrusively, such as instrument positions button presses. Recent studies have shown that based on these data (referred performance indicators [OPIs]) correlate select...
The number of international benchmarking competitions is steadily increasing in various fields machine learning (ML) research and practice. So far, however, little known about the common practice as well bottlenecks faced by community tackling questions posed. To shed light on status quo algorithm development specific field biomedical imaging analysis, we designed an survey that was issued to all participants challenges conducted conjunction with IEEE ISBI 2021 MICCAI conferences (80 total)....
Surgical data science is revolutionizing minimally invasive surgery by enabling context-aware applications. However, many challenges exist around surgical (and health data, more generally) needed to develop models. This work - presented as part of the Endoscopic Vision (EndoVis) challenge at Medical Image Computing and Computer Assisted Intervention (MICCAI) 2020 conference seeks explore potential for visual domain adaptation in overcome privacy concerns. In particular, we propose use video...
The ability to automatically detect and track surgical instruments in endoscopic videos can enable transformational interventions. Assessing performance efficiency, identifying skilled tool use choreography, planning operational logistical aspects of OR resources are just a few the applications that could benefit. Unfortunately, obtaining annotations needed train machine learning models identify localize tools is difficult task. Annotating bounding boxes frame-by-frame tedious...
Purpose: Basic surgical skills of suturing and knot tying are an essential part medical training. Having automated system for assessment could help save experts time improve training efficiency. There have been some recent attempts at using either video analysis or acceleration data. In this paper, we present a novel approach OSATS based provide different features on multi-modal data (video accelerometer data). Methods: We conduct the largest study, to best our knowledge, basic dataset that...
This paper builds on the traditional ball and plate balancing system; a novel method for making follow path of any polar graph, with equilibrium position as origin, is discussed extended to make 'n' sided polygon. A 5-wire Higgstec touch panel coupled Arduino Duemilanove Atmega 328 was used. description hardware software used algorithm followed also presented.
International benchmarking competitions have become fundamental for the comparative performance assessment of image analysis methods. However, little attention has been given to investigating what can be learnt from these competitions. Do they really generate scientific progress? What are common and successful participation strategies? makes a solution superior competing method? To address this gap in literature, we performed multi-center study with all 80 that were conducted scope IEEE ISBI...
Purpose: Manual feedback in basic RMIS training can consume a significant amount of time from expert surgeons' schedule and is prone to subjectivity. While VR-based tasks generate automated score reports, there no mechanism generating for surgeons performing surgical training. In this paper, we explore the usage different holistic features skill assessment using only robot kinematic data propose weighted feature fusion technique improving prediction performance. Methods: We perform our...
Adverse surgical outcomes are costly to patients and hospitals. Approaches benchmark care often limited gross measures across the entire procedure despite performance of particular tasks being largely responsible for undesirable outcomes. In order produce metrics from as opposed whole procedure, methods recognize automatically individual needed. this paper, we propose several approaches activities in robot-assisted minimally invasive surgery using deep learning. We collected a clinical...
Purpose: Surgical task-based metrics (rather than entire procedure metrics) can be used to improve surgeon training and, ultimately, patient care through focused interventions. Machine learning models automatically recognize individual tasks or activities are needed overcome the otherwise manual effort of video review. Traditionally, these have been evaluated using frame-level accuracy. Here, we propose evaluating surgical activity recognition by their effect on efficiency metrics. In this...
Timely and effective feedback within surgical training plays a critical role in developing the skills required to perform safe efficient surgery. Feedback from expert surgeons, while especially valuable this regard, is challenging acquire due their typically busy schedules, may be subject biases. Formal assessment procedures like OSATS GEARS attempt provide objective measures of skill, but remain time-consuming. With advances machine learning there an opportunity for fast automated on...