This paper proposes a novel tool detection and tracking approach using uncalibrated monocular surgical videos for computer-aided surgical interventions. We hypothesize surgical tool end-effector to be the most distinguishable part of a tool and employ state-of-the-art object detection methods to learn the shape and localize the tool in images. For tracking, we propose a Product of Tracking Experts (PoTE) based generalized object tracking framework by probabilistically-merging tracking outputs (probabilistic/non-probabilistic) from timevarying numbers of trackers. In the current implementation of PoTE, we use three tracking experts -point-feature-based, region-based and object detection-based. A novel point featurebased tracker is also proposed in the form of a voting based bounding box geometry estimation technique building upon point-feature correspondences. Our tracker is causal which makes it suitable for real-time applications. This framework has been tested on real surgical videos and is shown to significantly improve upon the baseline results.
Exoskeletons are a new class of articulated mechanical systems whose performance is realized while in intimate contact with the human user. The overall performance depends on many factors including selection of architecture, device, parameters and the nature of the coupling to the human, offering numerous challenges to designevaluation and refinement. In this paper, we discuss merger of techniques from the musculoskeletal analysis and simulation-based design to study and analyze the performance of such exoskeletons. A representative example of a simplified exoskeleton interacting with and assisting the human arm is used to illustrate principal ideas. Overall, four different case-scenarios are developed and examined with quantitative performance measures to evaluate the effectiveness of the design and allow for design refinement. The results show that augmentation by way of the exoskeleton can lead to a significant reduction in muscle loading.
Objective: The aim of this research was to estimate the impact of body mass index (BMI) on surgical outcomes in patients undergoing robotic-assisted gynecologic surgery. Materials and Methods: This study was a retrospective review of prospectively collected cohort data for a consecutive series of patients undergoing gynecologic robotic surgery in a single institution. BMI, expressed as kg/m 2 , was abstracted from the medical charts of all patients undergoing robotic hysterectomy. Data on estimated blood loss (EBL), hemoglobin (Hb) drop, procedure time, length of hospital stay, uterine weight, pain-medication use, and complications were also extracted. Results: Two hundred and eighty-one patients underwent robotic operations. Types of procedures were total hysterectomy with or without adnexal excision, and total hysterectomies with lymphadenectomies. Eighty-four patients who were classified as morbidly obese (BMI > 35) were compared with 197 patients who had a BMI of < 35 (nonmorbidly obese). For patients with BMI < 35, and BMI > 35, the mean BMI was 27.1 and 42.5 kg/m 2 ( p < 0.05), mean age was 49 and 50 ( p = 0.45), mean total operative time was 222 and 266 minutes ( p < 0.05), console time 115 and 142 minutes ( p < 0.05), closing time (from undocking until port-site fascia closure) was 30 and 41 minutes ( p < 0.05), EBL was 67 and 79 mL ( p = 0.27), Hb drop was 1.6 and 1.4 ( p = 0.28), uterine weight was 196.2 and 227 g ( p = 0.52), pain-medication use 93.7 and 111 mg of morphine ( p = 0.46), and mean length of stay was 1.42 and 1.43 days (0.9), all respectively. No statistically significant difference was noted between the 2 groups for EBL, Hb drop, LOS, uterine weight, pain-medication use, or complications. The only statistically significant difference was seen in operating times and included docking, console, closing, and procedure times. There were no perioperative mortalities. Morbidity occurred in 24 patients (8%). In the morbidly obese group, there were 6 complications (7%) and, in the nonmorbidly obese group, there were 18 complications (9%). Conclusions: Morbid obesity does not appear to be associated with an increased risk of morbidity in patients undergoing robotically assisted gynecologic surgery. Morbid obesity is associated with increased procedure time, but otherwise appears to have no difference in outcomes. Robotic surgery offered an ideal approach, allowing minimally invasive surgery in these technically challenging patients, with no significant increase in morbidity. J GYNECOL SURG 30:81)
Abstract²Assessment of surgical skill, arising from the synthesis of the cognitive and sensorimotor capabilities of the surgeon, has predominantly been a subjective task. Development of quantitative metrics-of-performance with clinical relevance and other desirable characteristics (repeatability and stability) has always lagged behind. New opportunities for objective and automated assessment frameworks have arisen by virtue of technological advances in computation, video-processing, and data-acquisition, especially in the robotic Minimally Invasive Surgical (rMIS) realm. Most efforts focus on semi-quantitative (Likert scale) or inadequately validated, spatially-or temporally-aggregated quantitative metrics derived from direct physical measurements. In this work we propose an automated surgical expertise evaluation method, by adapting well-established motion studies methodologies, especially for MIS evaluation. This method relies on segmenting a primary task into sub-tasks, which can be evaluated by statistical analyses of micromotions. Motion studies were developed by 2 methods: (A) manual annotation process by experts (to serve as a benchmark); and (B) automatedkinematic-analysis-of-videos; for economy, repeatability as well as dexterity. The da Vinci SKILLS simulator was used to serve as a uniform testbed. Surgeons with varied levels of expertise were recruited to perform two representative simplified tasks (Peg Board and Pick & Place). The automated kinematic analysis of video was compared with the ground truth data (obtained by manual labeling) using misclassification rate and true classification confusion matrix. Future studies aimed towards analyzing real surgical procedures are already underway.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.