scholarly journals Effect of marker position and size on the registration accuracy of HoloLens in a non-clinical setting with implications for high-precision surgical tasks

2021 ◽  
Author(s):  
Laura Pérez-Pachón ◽  
Parivrudh Sharma ◽  
Helena Brech ◽  
Jenny Gregory ◽  
Terry Lowe ◽  
...  
Keyword(s):  
Pattern Recognition ◽  
Real World ◽  
Sufficient Accuracy ◽  
Surgical Instruments ◽  
Patient Specific ◽  
Navigation Systems ◽  
Registration Accuracy ◽  
Marker Position ◽  
Virtual Models ◽  
Image Pattern

Abstract Purpose Emerging holographic headsets can be used to register patient-specific virtual models obtained from medical scans with the patient’s body. Maximising accuracy of the virtual models’ inclination angle and position (ideally, ≤ 2° and ≤ 2 mm, respectively, as in currently approved navigation systems) is vital for this application to be useful. This study investigated the accuracy with which a holographic headset registers virtual models with real-world features based on the position and size of image markers. Methods HoloLens® and the image-pattern-recognition tool Vuforia Engine™ were used to overlay a 5-cm-radius virtual hexagon on a monitor’s surface in a predefined position. The headset’s camera detection of an image marker (displayed on the monitor) triggered the rendering of the virtual hexagon on the headset’s lenses. 4 × 4, 8 × 8 and 12 × 12 cm image markers displayed at nine different positions were used. In total, the position and dimensions of 114 virtual hexagons were measured on photographs captured by the headset’s camera. Results Some image marker positions and the smallest image marker (4 × 4 cm) led to larger errors in the perceived dimensions of the virtual models than other image marker positions and larger markers (8 × 8 and 12 × 12 cm). ≤ 2° and ≤ 2 mm errors were found in 70.7% and 76% of cases, respectively. Conclusion Errors obtained in a non-negligible percentage of cases are not acceptable for certain surgical tasks (e.g. the identification of correct trajectories of surgical instruments). Achieving sufficient accuracy with image marker sizes that meet surgical needs and regardless of image marker position remains a challenge.

scholarly journals Multi-Drug Featurization and Deep Learning Improve Patient-Specific Predictions of Adverse Events

2021 ◽  
Vol 18 (5) ◽  
pp. 2600
Author(s):  
Ioannis N. Anastopoulos ◽  
Chloe K. Herczeg ◽  
Kasey N. Davis ◽  
Atray C. Dixit
Keyword(s):  
Machine Learning ◽  
Real World ◽  
Variable Number ◽  
Patient Specific ◽  
Real World Evidence ◽  
Genetic Features ◽  
The Uk ◽  
Therapeutic Compounds ◽  
Improve Patient ◽  
Reaction Profile

While the clinical approval process is able to filter out medications whose utility does not offset their adverse drug reaction profile in humans, it is not well suited to characterizing lower frequency issues and idiosyncratic multi-drug interactions that can happen in real world diverse patient populations. With a growing abundance of real-world evidence databases containing hundreds of thousands of patient records, it is now feasible to build machine learning models that incorporate individual patient information to provide personalized adverse event predictions. In this study, we build models that integrate patient specific demographic, clinical, and genetic features (when available) with drug structure to predict adverse drug reactions. We develop an extensible graph convolutional approach to be able to integrate molecular effects from the variable number of medications a typical patient may be taking. Our model outperforms standard machine learning methods at the tasks of predicting hospitalization and death in the UK Biobank dataset yielding an R2 of 0.37 and an AUC of 0.90, respectively. We believe our model has potential for evaluating new therapeutic compounds for individualized toxicities in real world diverse populations. It can also be used to prioritize medications when there are multiple options being considered for treatment.

scholarly journals Three-dimensional technologies used for patient specific applications in orthopedics

2021 ◽  
Vol 67 (2) ◽  
pp. 77-85
Author(s):  
Flaviu Moldovan ◽  
Tiberiu Bataga
Keyword(s):  
Orthopedic Surgery ◽  
Three Dimensional ◽  
Medical Application ◽  
Surgical Instruments ◽  
Patient Specific ◽  
Innovative Technology ◽  
Clinical Environment ◽  
Clinical Practices ◽  
Content Description ◽  
Almost All

Abstract Background: Three-dimensional (3D) technologies have numerous medical applications and have gained a lot of interest in medical world. After the advent of three-dimensional printing technology, and especially in last decade, orthopedic surgeons began to apply this innovative technology in almost all areas of orthopedic traumatic surgery. Objective: The aim of this paper is to give an overview of 3D technologies current usage in orthopedic surgery for patient specific applications. Methods: Two major databases PubMed and Web of Science were explored for content description and applications of 3D technologies in orthopedic surgery. It was considered papers presenting controlled studies and series of cases that include descriptions of 3D technologies compatible with applications to human medical purposes. Results: First it is presented the available three-dimensional technologies that can be used in orthopedic surgery as well as methods of integration in order to achieve the desired medical application for patient specific orthopedics. Technology starts with medical images acquisition, followed by design, numerical simulation, and printing. Then it is described the state of the art clinical applications of 3D technologies in orthopedics, by selecting the latest reported articles in medical literature. It is focused on preoperative visualization and planning, trauma, injuries, elective orthopedic surgery, guides and customized surgical instrumentation, implants, orthopedic fixators, orthoses and prostheses. Conclusion: The new 3D digital technologies are revolutionizing orthopedic clinical practices. The vast potential of 3D technologies is increasingly used in clinical practice. These technologies provide useful tools for clinical environment: accurate preoperative planning for cases of complex trauma and elective cases, personalized surgical instruments and personalized implants. There is a need to further explore the vast potential of 3D technologies in many other areas of orthopedics and to accommodate healthcare professionals with these technologies, as well as to study their effectiveness compared to conventional methods.

scholarly journals A New Hybrid Distance-Based Similarity Measure for Refined Neutrosophic sets and its Application in Medical Diagnosis

MATEMATIKA ◽  
2019 ◽  
Vol 35 (1) ◽  
pp. 83-94 ◽  
Author(s):  
Vakkas Ulucay ◽  
Adil Kılıç ◽  
Memet Şahin ◽  
Harun Deniz
Keyword(s):  
Decision Making ◽  
Pattern Recognition ◽  
Image Segmentation ◽  
Similarity Measure ◽  
Real World ◽  
Medical Diagnosis ◽  
Neutrosophic Sets ◽  
Hybrid Distance

 In recent times, refined neutrosophic sets introduced by Deli [6] has been one of the most powerful and flexible approaches for dealing with complex and uncertain situations of real world. In particular, the decision making methods between refined neutrosophic sets are important since it has applications in various areas such as image segmentation, decision making, medical diagnosis, pattern recognition and many more. The aim of this paper is to introduce a new distance-based similarity measure for refined neutrosophic sets. The properties of the proposed new distance-based similarity measure have been studied and the findings are applied in medical diagnosis of some diseases with a common set of symptoms.

scholarly journals Time-Frequency Feature Analysis

2021 ◽  
Author(s):  
Behnaz Ghoraani
Keyword(s):  
Pattern Recognition ◽  
Real World ◽  
Feature Detection ◽  
Classification Systems ◽  
Time Frequency ◽  
Analysis Methodology ◽  
Stationary Signals ◽  
Time Varying Frequency ◽  
Non Stationary Signal ◽  
Discriminative Pattern

Most of the real-world signals in nature are non-stationary, i.e., their statistics are time variant. Extracting the time-varying frequency characteristics of a signal is very important in understanding the signal better, which could be of immense use in various applications such as pattern recognition and automated-decision making systems. In order to extract meaningful time-frequency (TF) features, a joint TF analysis is required. The proposed work is an attempt to develop a generalized TF analysis methodology that exploits the benefits of TF distribution (TFD) in pattern classification systems as related to discriminant feature detection and classification. Our objective is to introduce a unique and efficient way of performing non-stationary signal analysis using adaptive and discriminant TF techniques. To fulfill this objective, in the first point, we build a novel TF matrix (TFM) decomposition that increases the effectiveness of segmentation in real-world signals. Instantaneous and unique features are extracted from each segment such that they successfully represent joint TF structure of the signal. In the second point, based on the above technique, two unique and novel discriminant TF analysis methods are proposed to perform an improved and discriminant feature selection of any non-stationary signals. The first approach is a new machine learning method that identifies the clusters of the discriminant features to compute the presence of the discriminative pattern in any given signal, and classify them accordingly. The second approach is a discriminant TFM (DTFM) framework, which is a combination of TFM decomposition and the discriminant clustering techniques. The developed DTFM analysis automatically identifies the differences between different classes as the distinguishing structure, and uses the identified structure to accurately classify and locate the discriminant structure in the signal. The theoretical properties of the proposed approaches pertaining to pattern recognition and detection are examined in this dissertation. The extracted TF features provide strong and successful characterization and classification of real and synthetic non-stationary signals. The proposed TF techniques facilitate the adaptation of TF quantification to any feature detection technique in automating the identification process of discriminatory TF features, and can find applications in many different fields including biomedical and multimedia signal processing.

Comparison of Patient-Specific Instruments with Standard Surgical Instruments in Determining Glenoid Component Position

2012 ◽  
Vol 94 (23) ◽  
pp. 2167-2175 ◽  
Author(s):  
Michael D. Hendel ◽  
Jason A. Bryan ◽  
Wael K. Barsoum ◽  
Eric J. Rodriguez ◽  
John J. Brems ◽  
...  
Keyword(s):  
Surgical Instruments ◽  
Patient Specific ◽  
Glenoid Component ◽  
Component Position ◽  
Patient Specific Instruments

Chemometric Classification Techniques as a Tool for Solving Problems in Analytical Chemistry

2014 ◽  
Vol 97 (1) ◽  
pp. 19-28 ◽  
Author(s):  
Marta Bevilacqua ◽  
Riccardo Nescatelli ◽  
Remo Bucci ◽  
Andrea D Magrì ◽  
Antonio L Magrì ◽  
...  
Keyword(s):  
Analytical Chemistry ◽  
Pattern Recognition ◽  
Real World ◽  
Chemometric Methods ◽  
Classification Techniques ◽  
The Family ◽  
Real World Problems

Abstract Supervised pattern recognition (classification) techniques, i.e., the family of chemometric methods whose aim is the prediction of a qualitative responseon a set of samples, represent a very important assortment of tools for solving problems in several areas of applied analytical chemistry. This paper describes the theory behind the chemometric classificationtechniques most frequently used inanalytical chemistry together with some examples of their application to real-world problems.

Implementation of Backpropagation for Ulap-ulap Pattern Recognition

2021 ◽  
Vol 9 (3) ◽  
pp. 405
Author(s):  
Ni Luh Yulia Alami Dewi ◽  
I Wayan Santiyasa
Keyword(s):  
Image Processing ◽  
Pattern Recognition ◽  
Data Collection ◽  
Test Data ◽  
Training Data ◽  
Image Pattern

Ulap-ulap is one of the symbols used to indicate that a building has been carried out Mlaspas ceremony. Mlaspas is one of the ceremonies performed to purify and clean a building. Ulap-ulap itself consists of various types depending on the building where it is placed, for example the ulap-ulap placed on the Pelinggih building will be different from the ulap-ulap placed on the Bale building. So that the pattern contained in each type of Ulap-ulap is different. The purpose of this research is to be able to do pattern recognition on Ulap-ulap images. The method used in this study is Backpropagation, and for its implementation, the MATLAB 7.5.0 (R2007b) application is used. This study used 18 images of Ulap-ulap, including 15 training data and 6 test data. The stages of the process carried out are for Ulap-ulap pattern recognition, the first is data collection, then image processing, and finally the pattern recognition. Recognition of the Ulap-ulap image pattern with Backpropagation, resulted in an accuracy of 83.333%.

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