Abstract: A retrospective study conducted at Christian Medical
College (CMC) Teaching Hospital, Vellore, India on 14th August
2014 to assess the accuracy of clinically estimated foetal weight upon
labour admission. Estimating foetal weight is a crucial factor in
assessing maternal and foetal complications during and after labour.
Medical notes of ninety-eight postnatal women who fulfilled the
inclusion criteria were studied to evaluate the correlation between
their recorded Estimated Foetal Weight (EFW) on admission and
actual birth weight (ABW) of the newborn after delivery. Data
concerning maternal and foetal demographics was also noted.
Accuracy was determined by absolute percentage error and
proportion of estimates within 10% of ABW. Actual birth weights
ranged from 950-4080g. A strong positive correlation between EFW
and ABW (r=0.904) was noted. Term deliveries (≥40 weeks) in the
normal weight range (2500-4000g) had a 59.5% estimation accuracy
(n=74) compared to pre-term (4000g) were underestimated by 25% (n=3) and low birthweight
(LBW) babies were overestimated by 12.7% (n=9). Registrars who
estimated foetal weight were accurate in babies within normal weight
ranges. However, there needs to be an improvement in predicting
weight of macrosomic and LBW foetuses. We have suggested the use
of an amended version of the Johnson’s formula for the Indian
population for improvement and a need to re-audit once
implemented.
Abstract: Measuring the Electrocardiogram (ECG) signal is an
essential process for the diagnosis of the heart diseases. The ECG
signal has the information of the degree of how much the heart
performs its functions. In medical diagnosis and treatment systems,
Decision Support Systems processing the ECG signal are being
developed for the use of clinicians while medical examination. In this
study, a modular wireless ECG (WECG) measuring and recording
system using a single board computer and e-Health sensor platform
is developed. In this designed modular system, after the ECG signal
is taken from the body surface by the electrodes first, it is filtered and
converted to digital form. Then, it is recorded to the health database
using Wi-Fi communication technology. The real time access of the
ECG data is provided through the internet utilizing the developed
web interface.
Abstract: The goal of this paper is to present the diagnostic
contribution that the screening instrument, Mini-Mental State
Examination-2: Expanded Version (MMSE-2:EV), brings in
detecting the cognitive impairment or in monitoring the progress of
degenerative disorders. The diagnostic signification is underlined by
the interpretation of the MMSE-2:EV scores, resulted from the test
application to patients with mild and major neurocognitive disorders.
The cases were selected from current practice, in order to cover vast
and significant neurocognitive pathology: mild cognitive impairment,
Alzheimer’s disease, vascular dementia, mixed dementia, Parkinson’s
disease, conversion of the mild cognitive impairment into
Alzheimer’s disease. The MMSE-2:EV version was used: it was
applied one month after the initial assessment, three months after the
first reevaluation and then every six months, alternating the blue and
red forms. Correlated with age and educational level, the raw scores
were converted in T scores and then, with the mean and the standard
deviation, the z scores were calculated. The differences of raw scores
between the evaluations were analyzed from the point of view of
statistic signification, in order to establish the progression in time of
the disease. The results indicated that the psycho-diagnostic approach
for the evaluation of the cognitive impairment with MMSE-2:EV is
safe and the application interval is optimal. In clinical settings with a
large flux of patients, the application of the MMSE-2:EV is a safe
and fast psychodiagnostic solution. The clinicians can draw objective
decisions and for the patients: it does not take too much time and
energy, it does not bother them and it doesn’t force them to travel
frequently.
Abstract: Moving into a new era of healthcare, new tools and
devices are developed to extend and improve health services, such as
remote patient monitoring and risk prevention. In this concept,
Internet of Things (IoT) and Cloud Computing present great
advantages by providing remote and efficient services, as well as
cooperation between patients, clinicians, researchers and other health
professionals. This paper focuses on patients suffering from bipolar
disorder, a brain disorder that belongs to a group of conditions
called affective disorders, which is characterized by great mood
swings. We exploit the advantages of Semantic Web and Cloud
Technologies to develop a patient monitoring system to support
clinicians. Based on intelligently filtering of evidence-knowledge and
individual-specific information we aim to provide treatment
notifications and recommended function tests at appropriate times or
concluding into alerts for serious mood changes and patient’s nonresponse
to treatment. We propose an architecture as the back-end
part of a cloud platform for IoT, intertwining intelligence devices
with patients’ daily routine and clinicians’ support.
Abstract: Pulmonary Function Tests are important non-invasive
diagnostic tests to assess respiratory impairments and provides
quantifiable measures of lung function. Spirometry is the most
frequently used measure of lung function and plays an essential role
in the diagnosis and management of pulmonary diseases. However,
the test requires considerable patient effort and cooperation,
markedly related to the age of patients resulting in incomplete data
sets. This paper presents, a nonlinear model built using Multivariate
adaptive regression splines and Random forest regression model to
predict the missing spirometric features. Random forest based feature
selection is used to enhance both the generalization capability and the
model interpretability. In the present study, flow-volume data are
recorded for N= 198 subjects. The ranked order of feature importance
index calculated by the random forests model shows that the
spirometric features FVC, FEF25, PEF, FEF25-75, FEF50 and the
demographic parameter height are the important descriptors. A
comparison of performance assessment of both models prove that, the
prediction ability of MARS with the `top two ranked features namely
the FVC and FEF25 is higher, yielding a model fit of R2= 0.96 and
R2= 0.99 for normal and abnormal subjects. The Root Mean Square
Error analysis of the RF model and the MARS model also shows that
the latter is capable of predicting the missing values of FEV1 with a
notably lower error value of 0.0191 (normal subjects) and 0.0106
(abnormal subjects) with the aforementioned input features. It is
concluded that combining feature selection with a prediction model
provides a minimum subset of predominant features to train the
model, as well as yielding better prediction performance. This
analysis can assist clinicians with a intelligence support system in the
medical diagnosis and improvement of clinical care.
Abstract: Background: Dimensional and transdiagnostic approaches as a result of high comorbidity among mental disorders have captured researchers and clinicians interests for exploring the latent factors to development and maintenance of some psychological disorders. The goal of present study is comparing some of these common factors between generalized anxiety disorder and unipolar mood disorder. Methods: 27 patients with generalized anxiety disorder, 29 patients with depression disorder were recruited by using SCID-I and 69 non-clinical populations were selected by using GHQ cut off point. MANCOVA was used for analyzing data. Results: The results show that worry, rumination, intolerance of uncertainty, maladaptive metacognitive beliefs, and experiential avoidance were all significantly different between GAD and unipolar mood disorder groups. However, there weren’t any significant differences in difficulties in emotion regulation and neuroticism between GAD and unipolar mood disorder groups. Discussion: Results indicate that although there are some transdiagnostic and common factors in GAD and unipolar mood disorder, there may be some specific vulnerability factors for each disorder. Further study is needed for answering these questions.
Abstract: The objective of this study was to test how advanced digital technology enables a more effective training on the handwriting of children with handwriting deficit. This study implemented the graphomotor apparatuses to a computer-assisted instruction system. In a randomized controlled trial, the experiments for verifying the intervention effect were conducted. Forty two children with handwriting deficit were assigned to computer-assisted instruction, sensorimotor training or control (no intervention) group. Handwriting performance was measured using the Elementary reading/writing test and computerized handwriting evaluation before and after 6 weeks of intervention. Analysis of variance of change scores were conducted to show whether statistically significant difference across the three groups. Significant difference was found among three groups. Computer group shows significant difference from the other two groups. Significance was denoted in near-point, far-point copy, dictation test, and writing from phonetic symbols. Writing speed and mean stroke velocity in near-, far-point and short paragraph copy were found significantly difference among three groups. Computer group shows significant improvement from the other groups. For clinicians and school teachers, the results of this study provide a motor control based insight for the improvement of handwriting difficulties.
Abstract: Analysis and visualization of microarraydata is veryassistantfor biologists and clinicians in the field of diagnosis and treatment of patients. It allows Clinicians to better understand the structure of microarray and facilitates understanding gene expression in cells. However, microarray dataset is a complex data set and has thousands of features and a very small number of observations. This very high dimensional data set often contains some noise, non-useful information and a small number of relevant features for disease or genotype. This paper proposes a non-linear dimensionality reduction algorithm Local Principal Component (LPC) which aims to maps high dimensional data to a lower dimensional space. The reduced data represents the most important variables underlying the original data. Experimental results and comparisons are presented to show the quality of the proposed algorithm. Moreover, experiments also show how this algorithm reduces high dimensional data whilst preserving the neighbourhoods of the points in the low dimensional space as in the high dimensional space.
Abstract: Microcirculation is essential for the proper supply of
oxygen and nutritive substances to the biological tissue and the
removal of waste products of metabolism. The determination of
blood flow in the capillaries is therefore of great interest to clinicians.
A comparison has been carried out using the developed non-invasive,
non-contact and whole field laser speckle contrast imaging (LSCI)
based technique and as well as a commercially available laser
Doppler blood flowmeter (LDF) to evaluate blood flow at the finger
tip and elbow and is presented here. The LSCI technique gives more
quantitative information on the velocity of blood when compared to
the perfusion values obtained using the LDF. Measurement of blood
flow in capillaries can be of great interest to clinicians in the
diagnosis of vascular diseases of the upper extremities.
Abstract: Wireless capsule Endoscopy (WCE) has rapidly
shown its wide applications in medical domain last ten years
thanks to its noninvasiveness for patients and support for thorough
inspection through a patient-s entire digestive system including
small intestine. However, one of the main barriers to efficient
clinical inspection procedure is that it requires large amount of
effort for clinicians to inspect huge data collected during the
examination, i.e., over 55,000 frames in video. In this paper, we
propose a method to compute meaningful motion changes of
WCE by analyzing the obtained video frames based on regional
optical flow estimations. The computed motion vectors are used to
remove duplicate video frames caused by WCE-s imaging nature,
such as repetitive forward-backward motions from peristaltic
movements. The motion vectors are derived by calculating
directional component vectors in four local regions. Our
experiments are performed on small intestine area, which is of
main interest to clinical experts when using WCEs, and our
experimental results show significant frame reductions comparing
with a simple frame-to-frame similarity-based image reduction
method.
Abstract: Our Medicine-oriented research is based on a medical
data set of real patients. It is a security problem to share
patient private data with peoples other than clinician or hospital
staff. We have to remove person identification information
from medical data. The medical data without private data
are available after a de-identification process for any research
purposes. In this paper, we introduce an universal automatic
rule-based de-identification application to do all this stuff on an
heterogeneous medical data. A patient private identification is
replaced by an unique identification number, even in burnedin
annotation in pixel data. The identical identification is used
for all patient medical data, so it keeps relationships in a data.
Hospital can take an advantage of a research feedback based
on results.
Abstract: While OCD is one of the most commonly occurring
psychiatric conditions experienced by older adults, there is a paucity
of research conducted into the treatment of older adults with OCD.
This case study represents the first published investigation of a
cognitive treatment for geriatric OCD. It describes the successful
treatment of an 86-year old man with a 63-year history of OCD using
Danger Ideation Reduction Therapy (DIRT). The client received 14
individual, 50-minute treatment sessions of DIRT over 13 weeks.
Clinician-based Y-BOCS scores reduced 84% from 25 (severe) at
pre-treatment, to 4 (subclinical) at 6-month post-treatment follow-up
interview, demonstrating the efficacy of DIRT for this client. DIRT
may have particular advantages over ERP and pharmacological
approaches, however further research is required in older adults with
OCD.
Abstract: The identification and classification of the spine deformity play an important role when considering surgical planning for adolescent patients with idiopathic scoliosis. The subject of this article is the Lenke classification of scoliotic spines using Cobb angle measurements. The purpose is two-fold: (1) design a rulebased diagram to assist clinicians in the classification process and (2) investigate a computer classifier which improves the classification time and accuracy. The rule-based diagram efficiency was evaluated in a series of scoliotic classifications by 10 clinicians. The computer classifier was tested on a radiographic measurement database of 603 patients. Classification accuracy was 93% using the rule-based diagram and 99% for the computer classifier. Both the computer classifier and the rule based diagram can efficiently assist clinicians in their Lenke classification of spine scoliosis.
Abstract: Hemodialysis patients might suffer from unhealthy
care behaviors or long-term dialysis treatments. Ultimately they need
to be hospitalized. If the hospitalization rate of a hemodialysis center
is high, its quality of service would be low. Therefore, how to decrease
hospitalization rate is a crucial problem for health care. In this study
we combined temporal abstraction with data mining techniques for
analyzing the dialysis patients' biochemical data to develop a decision
support system. The mined temporal patterns are helpful for clinicians
to predict hospitalization of hemodialysis patients and to suggest them
some treatments immediately to avoid hospitalization.
Abstract: Atrial Fibrillation is the most common sustained
arrhythmia encountered by clinicians. Because of the invisible
waveform of atrial fibrillation in atrial activation for human, it is
necessary to develop an automatic diagnosis system. 12-Lead ECG
now is available in hospital and is appropriate for using Independent
Component Analysis to estimate the AA period. In this research, we
also adopt a second-order blind identification approach to transform
the sources extracted by ICA to more precise signal and then we use
frequency domain algorithm to do the classification. In experiment,
we gather a significant result of clinical data.
Abstract: Bone growth factors, such as Bone Morphogenic
Protein-2 (BMP-2) have been approved by the FDA to replace grafting for some surgical interventions, but the high dose requirement limits its use in patients. Noggin, an extracellular protein, blocks the effect of BMP-2 by binding to BMP. Preventing
the BMP-2/noggin interaction will help increase the free
concentration of BMP-2 and therefore should enhance its efficacy to
induce bone formation. The work presented here involves
computational design of novel small molecule inhibitory agents of BMP-2/noggin interaction, based on our current understanding of
BMP-2, and its known putative ligands (receptors and antagonists). A
successful acquisition of such an inhibitory agent of BMP-2/noggin interaction would allow clinicians to reduce the dose required of
BMP-2 protein in clinical applications to promote osteogenesis. The
available crystal structures of the BMPs, its receptors, and the binding partner noggin were analyzed to identify the critical residues
involved in their interaction. In presenting this study, LUDI de novo design method was utilized to perform virtual screening of a large
number of compounds from a commercially available library against the binding sites of noggin to identify the lead chemical compounds
that could potentially block BMP-noggin interaction with a high specificity.
Abstract: With the rapid growth in business size, today-s businesses orient Throughout thirty years local, national and international experience in medicine as a medical student, junior doctor and eventually Consultant and Professor in Anaesthesia, Intensive Care and Pain Management, I note significant generalised dissatisfaction among medical students and doctors regarding their medical education and practice. We repeatedly hear complaints from patients about the dysfunctional health care system they are dealing with and subsequently the poor medical service that they are receiving. Medical students are bombarded with lectures, tutorials, clinical rounds and various exams. Clinicians are weighed down with a never-ending array of competing duties. Patients are extremely unhappy about the long waiting lists, loss of their records and the continuous deterioration of the health care service. This problem has been reported in different countries by several authors [1,2,3]. In a trial to solve this dilemma, a genuine idea has been suggested implementing computer technology in medicine [2,3]. Computers in medicine are a medium of international communication of the revolutionary advances being made in the application of the computer to the fields of bioscience and medicine [4,5]. The awareness about using computers in medicine has recently increased all over the world. In Misr University for Science & Technology (MUST), Egypt, medical students are now given hand-held computers (Laptop) with Internet facility making their medical education accessible, convenient and up to date. However, this trial still needs to be validated. Helping the readers to catch up with the on going fast development in this interesting field, the author has decided to continue reviewing the literature, exploring the state-of-art in computer based medicine and up dating the medical professionals especially the local trainee Doctors in Egypt. In part I of this review article we will give a general background discussing the potential use of computer technology in the various aspects of the medical field including education, research, clinical practice and the health care service given to patients. Hope this will help starting changing the culture, promoting the awareness about the importance of implementing information technology (IT) in medicine, which is a field in which such help is needed. An international collaboration is recommended supporting the emerging countries achieving this target.
Abstract: In this work, we improve a previously developed
segmentation scheme aimed at extracting edge information from
speckled images using a maximum likelihood edge detector. The
scheme was based on finding a threshold for the probability density
function of a new kernel defined as the arithmetic mean-to-geometric
mean ratio field over a circular neighborhood set and, in a general
context, is founded on a likelihood random field model (LRFM). The
segmentation algorithm was applied to discriminated speckle areas
obtained using simple elliptic discriminant functions based on
measures of the signal-to-noise ratio with fractional order moments.
A rigorous stochastic analysis was used to derive an exact expression
for the cumulative density function of the probability density
function of the random field. Based on this, an accurate probability
of error was derived and the performance of the scheme was
analysed. The improved segmentation scheme performed well for
both simulated and real images and showed superior results to those
previously obtained using the original LRFM scheme and standard
edge detection methods. In particular, the false alarm probability was
markedly lower than that of the original LRFM method with
oversegmentation artifacts virtually eliminated. The importance of
this work lies in the development of a stochastic-based segmentation,
allowing an accurate quantification of the probability of false
detection. Non visual quantification and misclassification in medical
ultrasound speckled images is relatively new and is of interest to
clinicians.