90 research outputs found
Artificial Intelligence in Musculoskeletal Conditions
Artificial intelligence (AI) refers to computer capabilities that resemble human intelligence. AI implies the ability to learn and perform tasks that have not been specifically programmed. Moreover, it is an iterative process involving the ability of computerized systems to capture information, transform it into knowledge, and process it to produce adaptive changes in the environment. A large labeled database is needed to train the AI system and generate a robust algorithm. Otherwise, the algorithm cannot be applied in a generalized way. AI can facilitate the interpretation and acquisition of radiological images. In addition, it can facilitate the detection of trauma injuries and assist in orthopedic and rehabilitative processes. The applications of AI in musculoskeletal conditions are promising and are likely to have a significant impact on the future management of these patients
Faculty Publications and Creative Works 1999
One of the ways in which we recognize our faculty at the University of New Mexico is through Faculty Publications & Creative Works. An annual publication, it highlights our faculty\u27s scholarly and creative activities and achievements and serves as a compendium of UNM faculty efforts during the 1999 calendar year. Faculty Publications & Creative Works strives to illustrate the depth and breadth of research activities performed throughout our University\u27s laboratories, studios and classrooms. We believe that the communication of individual research is a significant method of sharing concepts and thoughts and ultimately inspiring the birth of new ideas. In support of this, UNM faculty during 1999 produced over 2,292 works, including 1,837 scholarly papers and articles, 78 books, 82 book chapters, 175 reviews, 113 creative works and 7 patented works. We are proud of the accomplishments of our faculty which are in part reflected in this book, which illustrates the diversity of intellectual pursuits in support of research and education at the University of New Mexico
Vision Sensors and Edge Detection
Vision Sensors and Edge Detection book reflects a selection of recent developments within the area of vision sensors and edge detection. There are two sections in this book. The first section presents vision sensors with applications to panoramic vision sensors, wireless vision sensors, and automated vision sensor inspection, and the second one shows image processing techniques, such as, image measurements, image transformations, filtering, and parallel computing
Advanced Applications of Rapid Prototyping Technology in Modern Engineering
Rapid prototyping (RP) technology has been widely known and appreciated due to its flexible and customized manufacturing capabilities. The widely studied RP techniques include stereolithography apparatus (SLA), selective laser sintering (SLS), three-dimensional printing (3DP), fused deposition modeling (FDM), 3D plotting, solid ground curing (SGC), multiphase jet solidification (MJS), laminated object manufacturing (LOM). Different techniques are associated with different materials and/or processing principles and thus are devoted to specific applications. RP technology has no longer been only for prototype building rather has been extended for real industrial manufacturing solutions. Today, the RP technology has contributed to almost all engineering areas that include mechanical, materials, industrial, aerospace, electrical and most recently biomedical engineering. This book aims to present the advanced development of RP technologies in various engineering areas as the solutions to the real world engineering problems
Predictive modelling of football injuries
The goal of this thesis is to investigate the potential of predictive modelling for football injuries.
This work was conducted in close collaboration with Tottenham Hotspurs FC (THFC), the PGA
European tour and the participation of Wolverhampton Wanderers (WW).
Three investigations were conducted:
1. Predicting the recovery time of football injuries using the UEFA injury
recordings: The UEFA recordings is a common standard for recording injuries in
professional football. For this investigation, three datasets of UEFA injury recordings
were available: one from THFC, one from WW and one that was constructed by
merging both. Poisson, negative binomial and ordinal regression were used to model
the recovery time after an injury and assess the significance of various injury-related
covariates. Then, different machine learning algorithms (support vector machines,
Gaussian processes, neural networks, random forests, naïve Bayes and k-nearest
neighbours) were used in order to build a predictive model. The performance of the
machine learning models is then improved by using feature selection conducted
through correlation-based subset feature selection and random forests.
2. Predicting injuries in professional football using exposure records: The
relationship between exposure (in training hours and match hours) in professional
football athletes and injury incidence was studied. A common problem in football is
understanding how the training schedule of an athlete can affect the chance of him
getting injured. The task was to predict the number of days a player can train before he
gets injured. The dataset consisted of the exposure records of professional footballers
in Tottenham Hotspur Football Club from the season 2012-2013. The problem was
approached by a Gaussian process model equipped with a dynamic time warping kernel
that allowed the calculation of the similarity of exposure records of different lengths.
3. Predicting intrinsic injury incidence using in-training GPS measurements: A
significant percentage of football injuries can be attributed to overtraining and fatigue.
GPS data collected during training sessions might provide indicators of fatigue, or
might be used to detect very intense training sessions which can lead to overtraining.
This research used GPS data gathered during training sessions of the first team of
THFC, in order to predict whether an injury would take place during a week. The data
consisted of 69 variables in total. Two different binary classification approaches were
followed and a variety of algorithms were applied (supervised principal component
analysis, random forests, naïve Bayes, support vector machines, Gaussian process,
neural networks, ridge logistic regression and k-nearest neighbours). Supervised
principal component analysis shows the best results, while it also allows the extraction
of components that reduce the total number of variables to 3 or 4 components which
correlate with injury incidence.
The first investigation contributes the following to the field:
• It provides models based on the UEFA injury recordings, a standard used by many
clubs, which makes it easier to replicate and apply the results.
• It investigates which variables seem to be more highly related to the prediction of
recovery after an injury.
• It provides a comparison of models for predicting the time to return to play after injury.
The second investigation contributes the following to the field:
• It provides a model that can be used to predict the time when the first injury of the
season will take place.
• It provides a kernel that can be utilized by a Gaussian process in order to measure the
similarity of training and match schedules, even if the time series involved are of
different lengths.
The third investigation contributes the following to the field:
• It provides a model to predict injury on a given week based on GPS data gathered from
training sessions.
• It provides components, extracted through supervised principal component analysis,
that correlate with injury incidence and can be used to summarize the large number of
GPS variables in a parsimonious way
Aerospace medicine and biology: A continuing bibliography with indexes (supplement 402)
This bibliography lists 244 reports, articles and other documents introduced into the NASA Scientific and Technical Information System during Nov. 1992. Subject coverage includes: aerospace medicine and physiology, life support systems and man/system technology, protective clothing, exobiology and extraterrestrial life, planetary biology, and flight crew behavior and performance
Pathophysiological study of the hippocampus in a presenilin 1 M146V rat model of Alzheimer's disease
Advancing clinical evaluation and diagnostics with artificial intelligence technologies
Machine Learning (ML) is extensively used in diverse healthcare applications to aid
physicians in diagnosing and identifying associations, sometimes hidden, between dif-
ferent biomedical parameters. This PhD thesis investigates the interplay of medical
images and biosignals to study the mechanisms of aging, knee cartilage degeneration,
and Motion Sickness (MS).
The first study shows the predictive power of soft tissue radiodensitometric parameters
from mid-thigh CT scans. We used data from the AGES-Reykjavik study, correlating
soft tissue numerical profiles from 3,000 subjects with cardiac pathophysiologies, hy-
pertension, and diabetes. The results show the role of fat, muscle, and connective tissue
in the evaluation of healthy aging. Moreover, we classify patients experiencing gait
symptoms, neurological deficits, and a history of stroke in a Korean population, reveal-
ing the significant impact of cognitive dual-gait analysis when coupled with single-gait.
The second study establishes new paradigms for knee cartilage assessment, correlating
2D and 3D medical image features obtained from CT and MRI scans. In the frame
of the EU-project RESTORE we were able to classify degenerative, traumatic, and
healthy cartilages based on their bone and cartilage features, as well as we determine
the basis for the development of a patient-specific cartilage profile.
Finally, in the MS study, based on a virtual reality simulation synchronized with a
moving platform and EEG, heart rate, and EMG, we extracted over 3,000 features
and analyzed their importance in predicting MS symptoms, concussion in female ath-
letes, and lifestyle influence. The MS features are extracted from the brain, muscle,
heart, and from the movement of the center of pressure during the experiment and
demonstrate their potential value to advance quantitative evaluation of postural con-
trol response.
This work demonstrates, through various studies, the importance of ML technologies in
improving clinical evaluation and diagnosis contributing to advance our understanding
of the mechanisms associated with pathological conditions.Tölvulærdómur (Machine Learning eða ML) er algjörlega viðurkennt og nýtt í ýmsum
heilbrigðisþjónustuviðskiptum til að hjálpa læknunum við að greina og finna tengsl
milli mismunandi líffærafræðilegra gilda, stundum dulinna. Þessi doktorsritgerð fjallar
um samspil læknisfræðilegra mynda og lífsmerkja til að skoða eðli aldrunar, niðurbrot
hnéhringjar og hreyfikerfissjúkdóms (Motion Sickness eða MS).
Fyrsta rannsóknin sýnir spárkraft midjubeins-CT-skanna í því að fullyrða staðfest-
ar meðalþyngdarlíkön, þar sem gögn úr AGES-Reykjavik-rannsókninni eru tengd við
hjarta- og æðafræðilega sjúkdóma, blóðþrýstingsveikindi og sykursýki hjá 3.000 þátt-
takendum. Niðurstöðurnar sýna hlutverk fitu, vöðva og tengikjarna í mati á heilbrigð-
um öldrun. Þar að auki flokkum við sjúklinga sem upplifa gangvandamál, taugaein-
kenni og sögu af heilablóðfalli í kóreanskri þjóð, þar sem einstök gangtaksskoðun er
tengd saman við tvískoðun.
Önnur rannsóknin setur upp ný tölfræðisfræðileg umhverfisviðmið til matar á hnéhringju
með samhengi 2D og 3D mynda sem aflað er úr CT og MRI-skömmtum. Í rauninni
höfum við getuð flokkað niðurbrots-, slys- og heilbrigðar hnéhringjur á grundvelli bein-
og brjóskmerkja með raun að sækja niðurstöður í umfjöllun um sjúklingar eftir réttu
einkasniði.
Að lokum, í MS-rannsókninni, notum við myndræn tilraun samþættaða með hreyfan-
legan grundvöll og EEG, hjartslátt, EMG þar sem yfir 3.000 aðgerðir eru útfránn og
greindir til að átta sig á áhrifum MS, höfuðárás hjá konum sem eru íþróttamenn, lífs-
stíl og fleira. Einkenni MS eru aflöguð úr heilanum, vöðvum, hjarta og frá hreyfingum
þyngdupunktsins á meðan tilraunin stendur og sýna mög
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