7 research outputs found
Classification of Caesarean Section and Normal Vaginal Deliveries Using Foetal Heart Rate Signals and Advanced Machine Learning Algorithms
ABSTRACT – Background: Visual inspection of Cardiotocography traces by obstetricians and midwives is the gold standard for monitoring the wellbeing of the foetus during antenatal care. However, inter- and intra-observer variability is high with only a 30% positive predictive value for the classification of pathological outcomes. This has a significant negative impact on the perinatal foetus and often results in cardio-pulmonary arrest, brain and vital organ damage, cerebral palsy, hearing, visual and cognitive defects and in severe cases, death. This paper shows that using machine learning and foetal heart rate signals provides direct information about the foetal state and helps to filter the subjective opinions of medical practitioners when used as a decision support tool. The primary aim is to provide a proof-of-concept that demonstrates how machine learning can be used to objectively determine when medical intervention, such as caesarean section, is required and help avoid preventable perinatal deaths. Methodology: This is evidenced using an open dataset that comprises 506 controls (normal virginal deliveries) and 46 cases (caesarean due to pH ≤7.05 and pathological risk). Several machine-learning algorithms are trained, and validated, using binary classifier performance measures. Results: The findings show that deep learning classification achieves Sensitivity = 94%, Specificity = 91%, Area under the Curve = 99%, F-Score = 100%, and Mean Square Error = 1%. Conclusions: The results demonstrate that machine learning significantly improves the efficiency for the detection of caesarean section and normal vaginal deliveries using foetal heart rate signals compared with obstetrician and midwife predictions and systems reported in previous studies
Classification of Foetal Distress and Hypoxia Using Machine Learning
Foetal distress and hypoxia (oxygen deprivation) is considered a serious condition and one of the main factors for caesarean section in the obstetrics and gynaecology department. It is considered to be the third most common cause of death in new-born babies. Foetal distress occurs in about 1 in 20 pregnancies. Many foetuses that experience some sort of hypoxic effects can have series risks such as damage to the cells of the central nervous system that may lead to life-long disability (cerebral palsy) or even death. Continuous labour monitoring is essential to observe foetal wellbeing during labour. Many studies have used data from foetal surveillance by monitoring the foetal heart rate with a cardiotocography, which has succeeded traditional methods for foetal monitoring since 1960. Despite the indication of normal results, these results are not reassuring, and a small proportion of these foetuses are actually hypoxic. This study investigates the use of machine learning classifiers for classification of foetal hypoxic cases using a novel method, in which we are not only considering the classification performance only, but also investigating the worth of each participating parameter to the classification as seen by medical literature. The main parameters that are included in this study as indicators of metabolic acidosis are: pH level (which is a measure of the hydrogen ion concentration of blood to specify the acidity or alkalinity), as an indicator of respiratory acidosis; Base Deficit of extra-cellular fluid level and Base Excess (BE) (which is the measure of the total concentration of blood buffer base that indicates metabolic acidosis or compensated respiratory alkalosis). In addition to other parameters such as the PCO2 (partial pressure of carbon dioxide can reflect the hypoxic state of the foetus) and the Apgar scores (which shows the foetal physical activity within a specific time interval after birth). The provided data was an open-source partum clinical data obtained by Physionet, including both hypoxic cases and normal cases. Six well known machine learning classifier are used for the classification; each model was presented with a set of selected features derived from the clinical data. Classifier evaluation is performed using the receiver operating characteristic curve analysis, area under the curve plots, as well as confusion matrix. The simulation results indicate that machine learning classifiers provide good results in diagnosis of foetal hypoxia, in addition to acceptable results of different combinations of parameters to differentiate the cases
Minimally Interactive Segmentation with Application to Human Placenta in Fetal MR Images
Placenta segmentation from fetal Magnetic Resonance (MR) images is important for fetal surgical planning. However, accurate segmentation results are difficult to achieve for automatic methods, due to sparse acquisition, inter-slice motion, and the widely varying position and shape of the placenta among pregnant women. Interactive methods have been widely used to get more accurate and robust results. A good interactive segmentation method should achieve high accuracy, minimize user interactions with low variability among users, and be computationally fast. Exploiting recent advances in machine learning, I explore a family of new interactive methods for placenta segmentation from fetal MR images. I investigate the combination of user interactions with learning from a single image or a large set of images. For learning from a single image, I propose novel Online Random Forests to efficiently leverage user interactions for the segmentation of 2D and 3D fetal MR images. I also investigate co-segmentation of multiple volumes of the same patient with 4D Graph Cuts. For learning from a large set of images, I first propose a deep learning-based framework that combines user interactions with Convolutional Neural Networks (CNN) based on geodesic distance transforms to achieve accurate segmentation and good interactivity. I then propose image-specific fine-tuning to make CNNs adaptive to different individual images and able to segment previously unseen objects. Experimental results show that the proposed algorithms outperform traditional interactive segmentation methods in terms of accuracy and interactivity. Therefore, they might be suitable for segmentation of the placenta in planning systems for fetal and maternal surgery, and for rapid characterization of the placenta by MR images. I also demonstrate that they can be applied to the segmentation of other organs from 2D and 3D images
Comprehensive data analysis to study parturition
Our limited understanding of the molecular mechanisms driving the onset of normal
human parturition makes it difficult to identify ‘what goes wrong’ in conditions such as
preterm labour (PTL), preterm prelabour rupture of membranes (PPROM) and
postpartum haemorrhage (PPH). This incomplete understanding seriously hampers the
development of effective ways to predict, prevent and treat parturition complications,
which are a cause of significant neonatal and maternal morbidity. Two principal barriers
to improving our understanding are 1) the great complexity of both the molecular
interactions initiating parturition and the aetiology of parturition complications, and 2)
the difficulty in generating relevant high quality molecular and epidemiological data. To
help make sense of this complexity, data should be analysed comprehensively to maximise
the amount of useful information gleaned from it.
This thesis aimed to explore the use of specialist methods to analyse novel and previously
published data to study the molecular mechanisms initiating human parturition and the
epidemiology of parturition complications.
The molecular mechanisms initiating parturition were explored through a gene expression
microarray of labouring and non-labouring myometrial tissue. This is the largest
microarray of its kind to date. Functional analysis and a network graph approach were
used to reveal genes and molecular pathways associated with labour. The first ever meta-analysis
of similar myometrial microarray datasets was also conducted to assess the
reliability and generalisability of the results. This work supported the hypothesis that
labour is associated with inflammatory events in the myometrium. A computer model of
an inflammatory signalling pathway associated with infection-induced PTL was then built
to provide proof of concept that such models can be used to study parturition. The model
was based on published data and described lipopolysaccharide-induced activation of the
transcription factor Nuclear Factor kappa B (NF-κB). This is the first attempt to generate a
dynamic kinetic model that has relevance to the molecular mechanisms of PTL, and the
first model of this pathway to explicitly include molecular interactions upstream of NF-κB
activation.
The epidemiology of complications at parturition was explored using three methods.
Firstly, a novel approach was developed to use network graphs to visualise and analyse a
dataset of nearly 50,000 birth records. The approach provided a quick and effective way to
preliminarily explore relationships between exposures and pregnancy outcomes in an
unbiased data-driven manner. Secondly, a record-linkage study of two datasets of birth
records was conducted to determine risk factors for PPH, including intergenerational
transmission of risk. This confirmed several known risk factors of PPH and showed that
women whose mothers or grandmothers had PPH do not appear to be at increased risk
themselves. Finally, a systematic review and meta-analysis of three randomised controlled
trials investigated the effectiveness of fetal assessment methods in improving maternal
and neonatal outcomes following PPROM. The review concluded that there is currently
insufficient evidence on the benefits and harms of any method of fetal assessment, and
further randomised controlled trials are required
Performance Evaluation of Smart Decision Support Systems on Healthcare
Medical activity requires responsibility not only from clinical knowledge and skill but
also on the management of an enormous amount of information related to patient care. It is
through proper treatment of information that experts can consistently build a healthy wellness
policy. The primary objective for the development of decision support systems (DSSs) is
to provide information to specialists when and where they are needed. These systems provide
information, models, and data manipulation tools to help experts make better decisions in a
variety of situations.
Most of the challenges that smart DSSs face come from the great difficulty of dealing
with large volumes of information, which is continuously generated by the most diverse types
of devices and equipment, requiring high computational resources. This situation makes this
type of system susceptible to not recovering information quickly for the decision making. As a
result of this adversity, the information quality and the provision of an infrastructure capable
of promoting the integration and articulation among different health information systems (HIS)
become promising research topics in the field of electronic health (e-health) and that, for this
same reason, are addressed in this research. The work described in this thesis is motivated
by the need to propose novel approaches to deal with problems inherent to the acquisition,
cleaning, integration, and aggregation of data obtained from different sources in e-health environments,
as well as their analysis.
To ensure the success of data integration and analysis in e-health environments, it
is essential that machine-learning (ML) algorithms ensure system reliability. However, in this
type of environment, it is not possible to guarantee a reliable scenario. This scenario makes
intelligent SAD susceptible to predictive failures, which severely compromise overall system
performance. On the other hand, systems can have their performance compromised due to the
overload of information they can support.
To solve some of these problems, this thesis presents several proposals and studies
on the impact of ML algorithms in the monitoring and management of hypertensive disorders
related to pregnancy of risk. The primary goals of the proposals presented in this thesis are
to improve the overall performance of health information systems. In particular, ML-based
methods are exploited to improve the prediction accuracy and optimize the use of monitoring
device resources. It was demonstrated that the use of this type of strategy and methodology
contributes to a significant increase in the performance of smart DSSs, not only concerning precision
but also in the computational cost reduction used in the classification process.
The observed results seek to contribute to the advance of state of the art in methods
and strategies based on AI that aim to surpass some challenges that emerge from the integration
and performance of the smart DSSs. With the use of algorithms based on AI, it is possible to
quickly and automatically analyze a larger volume of complex data and focus on more accurate
results, providing high-value predictions for a better decision making in real time and without
human intervention.A atividade médica requer responsabilidade não apenas com base no conhecimento
e na habilidade clínica, mas também na gestão de uma enorme quantidade de informações
relacionadas ao atendimento ao paciente. É através do tratamento adequado das informações
que os especialistas podem consistentemente construir uma política saudável de bem-estar. O
principal objetivo para o desenvolvimento de sistemas de apoio à decisão (SAD) é fornecer informações
aos especialistas onde e quando são necessárias. Esses sistemas fornecem informações,
modelos e ferramentas de manipulação de dados para ajudar os especialistas a tomar melhores
decisões em diversas situações.
A maioria dos desafios que os SAD inteligentes enfrentam advêm da grande dificuldade
de lidar com grandes volumes de dados, que é gerada constantemente pelos mais diversos
tipos de dispositivos e equipamentos, exigindo elevados recursos computacionais. Essa situação
torna este tipo de sistemas suscetível a não recuperar a informação rapidamente para a
tomada de decisão. Como resultado dessa adversidade, a qualidade da informação e a provisão
de uma infraestrutura capaz de promover a integração e a articulação entre diferentes sistemas
de informação em saúde (SIS) tornam-se promissores tópicos de pesquisa no campo da saúde
eletrônica (e-saúde) e que, por essa mesma razão, são abordadas nesta investigação. O trabalho
descrito nesta tese é motivado pela necessidade de propor novas abordagens para lidar
com os problemas inerentes à aquisição, limpeza, integração e agregação de dados obtidos de
diferentes fontes em ambientes de e-saúde, bem como sua análise.
Para garantir o sucesso da integração e análise de dados em ambientes e-saúde é
importante que os algoritmos baseados em aprendizagem de máquina (AM) garantam a confiabilidade
do sistema. No entanto, neste tipo de ambiente, não é possível garantir um cenário
totalmente confiável. Esse cenário torna os SAD inteligentes suscetíveis à presença de falhas
de predição que comprometem seriamente o desempenho geral do sistema. Por outro lado, os
sistemas podem ter seu desempenho comprometido devido à sobrecarga de informações que
podem suportar.
Para tentar resolver alguns destes problemas, esta tese apresenta várias propostas e
estudos sobre o impacto de algoritmos de AM na monitoria e gestão de transtornos hipertensivos
relacionados com a gravidez (gestação) de risco. O objetivo das propostas apresentadas nesta
tese é melhorar o desempenho global de sistemas de informação em saúde. Em particular, os
métodos baseados em AM são explorados para melhorar a precisão da predição e otimizar o
uso dos recursos dos dispositivos de monitorização. Ficou demonstrado que o uso deste tipo
de estratégia e metodologia contribui para um aumento significativo do desempenho dos SAD
inteligentes, não só em termos de precisão, mas também na diminuição do custo computacional
utilizado no processo de classificação.
Os resultados observados buscam contribuir para o avanço do estado da arte em métodos
e estratégias baseadas em inteligência artificial que visam ultrapassar alguns desafios que
advêm da integração e desempenho dos SAD inteligentes. Como o uso de algoritmos baseados
em inteligência artificial é possível analisar de forma rápida e automática um volume maior de
dados complexos e focar em resultados mais precisos, fornecendo previsões de alto valor para uma melhor tomada de decisão em tempo real e sem intervenção humana