This study investigated the intraobserver errors in obtaining visually selected anatomic landmarks that were used in registration process in a nonimage-based computer-assisted total knee replacement (TKR) system. The landmarks studied were center of distal femur, medial and lateral femoral epicondyle, center of proximal tibia, medial malleolus, and lateral malleolus. Repeated registration in the above sequence was done for 100 times by a single surgeon. The maximum combined errors in the mechanical axis of the lower limb were only 1.32 degrees (varus/valgus) in the coronal plane and 4.17 degrees (flexion/extension) in the sagittal plane. The maximum error in transepicondylar axis was 8.2 degrees. The errors using the visual selection of anatomic landmarks for the registration technique of bony landmarks in nonimage-based navigated TKR did not introduce significant error in the mechanical axis of the lower limb in the coronal plane. However, the error in the transepicondylar axis was significant in the "worst-case scenario."
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http://dx.doi.org/10.1016/j.arth.2005.02.011 | DOI Listing |
Purpose: Accurate identification of radiographic landmarks is fundamental to characterizing glenohumeral relationships before and sequentially after shoulder arthroplasty, but manual annotation of these radiographs is laborious. We report on the use of artificial intelligence, specifically computer vision and deep learning models (DLMs), in determining the accuracy of DLM-identified and surgeon identified (SI) landmarks before and after anatomic shoulder arthroplasty.
Materials & Methods: 240 true anteroposterior radiographs were annotated using 11 standard osseous landmarks to train a deep learning model.
J Craniofac Surg
January 2025
Department of Otolaryngology-Head and Neck Surgery, Medical University of South Carolina.
Background: Children with cleft lip ± palate (CL/P) may undergo nasoalveolar molding (NAM) before surgery to achieve arch alignment and tension-free closure, yet the endpoint of arch dimensions has not been defined.
Objective: To characterize the size and shape of infant palates using anatomic landmarks on magnetic resonance imaging in infants without CL/P.
Methods: Magnetic resonance imaging of infants without cleft palate younger than 3 months were reviewed and 13 measurements were taken to define palatal shape: distance between incisive foramen (IF) and incisors (IN), IF and middle of canines (MOC), between MOCs, between first molars (FM), 2 depth and 4 angle measurements.
Surg Endosc
January 2025
Surgery Department, Meander Medical Centre, Maatweg, Amersfoort, 3818 TZ, Utrecht, The Netherlands.
Background: Specific pelvic bone dimensions have been identified as predictors of total mesorectal excision (TME) difficulty and outcomes. However, manual measurement of these dimensions (pelvimetry) is labor intensive and thus, anatomic criteria are not included in the pre-operative difficulty assessment. In this work, we propose an automated workflow for pelvimetry based on pre-operative magnetic resonance imaging (MRI) volumes.
View Article and Find Full Text PDFJ Med Syst
January 2025
Department of Neurosurgery, University Medical Centre Utrecht, Utrecht, The Netherlands.
This study aimed to develop and validate a cost-effective, customizable patient-specific phantom for simulating external ventricular drain placement, combining image segmentation, 3-D printing and molding techniques. Two variations of the phantom were created based on patient MRI data, integrating a realistic skin layer with anatomical landmarks, a 3-D printed skull, an agarose polysaccharide gel brain, and a ventricular cavity. To validate the phantom, 15 neurosurgeons, residents, and physician assistants performed 30 EVD placements.
View Article and Find Full Text PDFSci Rep
January 2025
School of Mechanical Engineering, College of Engineering, University of Tehran, Tehran, Iran.
Anatomical Landmark detection in CT-Scan images is widely used in the identification of skeletal disorders. However, the traditional process of manually detecting anatomical landmarks, especially in three dimensions, is both time-consuming and prone to human errors. We propose a novel, deep-learning-based approach to automatic detection of 3D landmarks in CT images of the lower limb.
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