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Tracking and Calibration
Published in Terry M. Peters, Cristian A. Linte, Ziv Yaniv, Jacqueline Williams, Mixed and Augmented Reality in Medicine, 2018
In the context of computer-assisted orthopedic surgery, a tracked A-mode ultrasound is used as a variable-length stylus to digitize the surface of bony structures [32,33]. The calibration of A-mode ultrasound is typically formulated as a variation of pivot calibration (Section 2.5.1). Alternatively, it can also be formulated as line-to-lines registration [34], where the distance to the object is not needed.
Advances in Primary and Revision Hip Arthroplasty
Published in K. Mohan Iyer, Hip Joint in Adults: Advances and Developments, 2018
Shibu P. Krishnan, G. Gopinath
Computer-assisted orthopaedic surgery (CAOS) in THA is aimed at improving the accuracy of component positioning along with restoration of offset and limb lengths, thereby improving outcomes and minimising complications. The different techniques of CAOS THA include passive, active and semiactive systems [5]. Passive systems include the use of navigation aids that assist the surgeon in preoperative planning and informs on implant positioning during surgery. Active systems include the use of surgical robots that autonomously perform the surgery as planned by the surgeon. A semiactive system provides guidance to the operator with a haptic robot arm for positioning of the implant that was planned preoperatively. A detailed description of CAOS THA has been dealt with in a separate chapter.
Intervention: Nanotechnology in Reconstructive Intervention and Surgery
Published in Harry F. Tibbals, Medical Nanotechnology and Nanomedicine, 2017
Robotics in orthopedic surgery is used for accurate cutting, drilling, and milling of cavities to receive implants [261]. Orthopedic surgery requires accurate registration using iducial markers (metal pins) or points on the surface of the bone. Since the bone can be treated as a fixed object, computer control of robotic surgery is simpliied. The surgeon has to connect the robot to the bone and expose the bone for access and secure ixation of the robot to the bone, requiring careful soft-tissue management [262,263]. Computerassisted and robotic surgery aids these tasks to produce greater spatial accuracy and more reliable and reproducible outcomes. The ability of robots to combine high precision with strong forces is especially appropriate for surgery involving large structural bone tissues. Computer-assisted orthopedic surgery can assist non-robotic surgery for guidance and planning; however, robot-assisted orthopedic surgery can achieve levels of accuracy, precision, and safety not capable with computer assistance alone [264].
Optimal definitions for computing HKA angle in caos: an in-vitro comparison study
Published in Computer Assisted Surgery, 2022
Guillaume Dardenne, Bhushan Borotikar, Hoel Letissier, Ahmed Zemirline, Eric Stindel
Studies have shown that Computer Assisted Orthopedic Surgery (CAOS) can reduce the complications for the interventions requiring an accurate measurement of the HKAA [5–8]. Although there is a consensus in the literature to obtain the frontal HKAA on anteroposterior (AP) radiographs [1,9], several approaches have been applied in CAOS. The HKAA measurement relies on both the localization of the three lower limb joint centers (hip, ankle, and knee) and the determination of the frontal plane (FP). Thus, defining three centers and one plane are of prime importance in determining the lower limb alignment and the HKAA. The hip center (HC) and the ankle center (AC) are computed using well-established kinematic techniques, respectively, by acquiring the center of rotation of the circumduction motion of the hip [10–12], and by localizing the middle of the medial and lateral malleolus of the ankle [13]. However, multiple methods exist to determine both the knee center (KC) and the FP and to date there is no single study to report the efficacy of the methods used.
Population pharmacokinetic analysis of acetaminophen overdose with immediate release, extended release and modified release formulations
Published in Clinical Toxicology, 2022
Daniel A. Spyker, Richard C. Dart, Luke Yip, Kate Reynolds, Scott Brittain, Mark Yarema
This study has limitations. Although the largest 2 studies (CAOS and US-NMS) included a large number of patient exposures, these studies involved only immediate release (IR) formulation. We used a small fraction of these data by selecting the cases with 7 or more APAP levels. We compared 5 major PK parameters by number of APAP levels reported for 298 subjects with 7 or more levels to 1,918 subjects with 4, 5 or 6 levels. All 5 parameters were similar (no statistical difference). We have included this comparison as Table S-3 in Supplemental 7. We had access to a limited amount of overdose data for ER and MR exposures. The amount and timing of APAP concentration data for the overdose studies were generally less than we would have liked, especially for the early (absorption) phase. While all available coingestant data were classified and examined in the model building, only 4% of the subjects reported any anticholinergic exposure and we did not detect a statistically significant associated signal for any coingestants except for opioids. PK data following multidose exposures is quite limited and thus limits the accuracy of multidose simulations.
Changing nomogram risk zone classification with serial testing after acute acetaminophen overdose: a retrospective database analysis
Published in Clinical Toxicology, 2019
Adam Mutsaers, Jason P. Green, Marco L. A. Sivilotti, Mark C. Yarema, Dylan Tucker, David W. Johnson, Daniel A. Spyker, Barry H. Rumack
The CAOS was a structured explicit medical record review of all patients admitted for APAP poisoning based on their primary or secondary discharge diagnosis classified using the International Classification of Diseases codes 965.4 (9th revision, poisoning by aromatic analgesic) and T39.1 (10th revision, poisoning by nonopioid analgesics, antipyretics, and antirheumatics (4-aminophenol derivatives)). A single investigator trained one to three medical record reviewers per city until a percentage agreement of 80% or greater and an inter-reviewer κ > 0.8 were established on a random subset of at least 50 records per reviewer. Medical record reviewers were blinded to the study hypothesis. The accuracy of data collection was assessed by an independent review of the first 100 charts for each data abstractor, followed by quarterly database assessment for the duration of data collection. Data were collected from paper medical records for the entire study period (July 1997–November 2005), which predated the widespread adoption of electronic medical records. Further details on the design, selection of participants, definitions, and data collection for CAOS have been described previously [14].