Preface
Page: ii-v (4)
Author: Geeta Rani, Vijaypal Singh Dhaka and Pradeep Kumar Tiwari
DOI: 10.2174/9789815179125124010002
Introduction
Page: vi-vii (2)
Author: Geeta Rani, Vijaypal Singh Dhaka and Pradeep Kumar Tiwari
DOI: 10.2174/9789815179125124010003
Dedication
Page: viii-viii (1)
Author: Geeta Rani, Vijaypal Singh Dhaka and Pradeep Kumar Tiwari
DOI: 10.2174/9789815179125124010004
Role of Federated Learning in Healthcare: A Review
Page: 1-16 (16)
Author: Geeta Rani, Meet Oza, Heta Patel, Vijaypal Singh Dhaka* and Sushma Hans
DOI: 10.2174/9789815179125124010006
PDF Price: $15
Abstract
In the modern era, there is a boom in automating medical diagnosis by
adopting emerging technologies and advanced applications of artificial intelligence.
These technologies require a huge amount of data for training the models and precisely
predicting the disease or disorder. Multiple organizations can contribute data for such
systems but maintaining data privacy while sharing the data is a major challenge. Also,
provisioning a large data corpus for the performance improvement of machine learning
and deep learning models in the healthcare domain while keeping the patient’s medical
confidentiality intact is a point of concern. Thus, there is a strong need to preserve the
privacy of medical data. This calls for the use of up-to-the-minute technologies where
the necessity of sharing raw data is completely eradicated, while each organization
receives a catered infrastructure for processing data. A cross-silo federated learning
model is based on the concept of decentralized data weights collection from multiple
clients which are then processed on the central server for modeling and aggregation,
thus maintaining data privacy in its true sense. The authors in this manuscript provide a
detailed comparative study of the different deep learning-based models in federated
learning and how efficiently they can classify lung X-Ray images into three classes:
Covid-19, Pneumonia, and Normal. This study can provide a benchmark for the
researchers looking forward to deep learning-based model applications of cross-silo
federated learning in healthcare.
Role of Artificial Intelligence in 3-D Bone Image Reconstruction: A Review
Page: 17-30 (14)
Author: Nitesh Pradhan, Vijaypal Singh Dhaka, Geeta Rani and Monika Agarwal
DOI: 10.2174/9789815179125124010007
PDF Price: $15
Abstract
Three-dimensional geometry of a bone is important in the correct diagnosis
of a disease, arthritis, or other bone deformities. The modalities such as Computer
Tomography Scans and Magnetic Resonance Imaging are used for a three-dimensional
view of a bone. Both the above- stated modalities have high costs and expose the
patient to strong carcinogenic radiations. Computer Tomography captures an extensive
number of images to collect the required information from a bone. Another modality
Magnetic Resonance Imaging is more suitable for retrieving information from soft
tissues rather than bones. Therefore, it becomes less effective to read the pathology
from bones. This has motivated the authors to identify imaging techniques useful in
detecting the pathology or deformity in bones. Also, this is the need of the hour to
provide a low cost and safer technique of bone imaging. To address this need, we
present a review of the bone imaging techniques and techniques applied for the
conversion of two dimensional images into three-dimensional form. We also give the
directions for developing the patient-specific and organ-specific optimized techniques
for 3-D reconstruction.
Role of Machine Learning and Deep Learning Techniques in Detection of Disease Severity: A Survey
Page: 31-51 (21)
Author: Geeta Rani, Vijaypal Singh Dhaka* and Sushma Hans
DOI: 10.2174/9789815179125124010008
PDF Price: $15
Abstract
The increasing number of health issues is a cause of concern for public as
well as health services across the globe. However, a boom in the use of imaging
techniques such as CT scans and chest radiographs has been observed for correct
diagnosis. But, manual scanning of these modalities requires expertise in modality
reading. It is also a time-consuming task. Artificial intelligence-based techniques have
proven their potential in pattern recognition, object identification, and data analysis.
Therefore, these techniques can be used to provide assisting tools for the primary
screening of diseases from these modalities. It has been observed from the literature
that a lot of research works are available on disease diagnosis and classification using
machine learning, and deep learning. But, the disease severity detection is
underexplored. Moreover, the techniques employed for the detection of the severity of
diseases have lacunae that need immediate attention. These challenges motivated us to
review the machine learning and deep learning-based technological solutions proposed
in the literature for the detection of disease severity. The objective of this research is to
present a comprehensive survey of research works available about disease severity
detection. This research also presents a comparative analysis of the machine learning
techniques and deep learning techniques employed, datasets used, and performance
achieved. It also highlights the drawbacks of the technological solution proposed.
Further, it provides the directions for future scope in the domain of disease severity
detection.
Computer-aided Bio-medical Tools for Disease Identification
Page: 52-79 (28)
Author: E. Francy Irudaya Rani, T. Lurthu Pushparaj and E. Fantin Irudaya Raj*
DOI: 10.2174/9789815179125124010009
PDF Price: $15
Abstract
The health expert’s crucial task is to interpret the output and treat the disease
accordingly. They may delay the decision-making during emergencies. To address this
issue, research on smart tools for biomedical applications is much needed which may
help in making accurate decisions at the earliest stage. Discovery in medicinal research
requires state-of-the-art computer-based tools for diagnosing and treating complex
diseases such as cancer, COVID-19, SARS-Cov, MERS-Cov, tuberculosis, brain
disorders, heart, and lung-related chronic infections. Among various diagnostic
methods, image-based disease identification stands out as the most prominent approach
for detecting new and complex diseases. A well-trained computerized biomedical
system can provide physicians with enhanced support for early disease detection.
Biomedical images are typically acquired from various sources, including CT,
ultrasound, MRI, dermoscopy, X-ray, biopsy, and endoscopy. Presently, a wide range
of image-analysis procedures are available for biomedical images. These procedures
involve image acquisition, pre-processing, segmentation, feature extraction, and
classification, all contributing to improved disease decision accuracy. Although many
biomedical images are available online free of cost, the proper procedure must be
followed to select appropriate images from databases and enhance their quality. This is
important for effectively training image-processing algorithms and increasing their
efficiency. This leads to improved instrument performance and more valuable insights
into the diseases under study. It also handles complex and vast image data to detect
early signs of unusual signals, growth, inflammation, cell damage, protein sequence
changes, and blockages. Additionally, it should be user-friendly and convincing to
health experts to identify hidden biological issues. This chapter emphasizes the power
of computerized tools in image analysis and disease detection. It also focuses on recent
developments in the field of medicinal research.
Prognosis of Dementia using Machine Learning
Page: 80-91 (12)
Author: Anu Saini, Sunita Kumari*, Ritik, Rajni and Sushma Hans
DOI: 10.2174/9789815179125124010010
PDF Price: $15
Abstract
The brain is one of the most sensitive parts of the human body which
transmits millions of signals every moment. Dementia is the most emerging brain
health issue which involves memory loss, difficulty in problem-solving, handling
complex tasks, etc. Dementia is a syndrome that causes a loss of mental ability. It
affects memory, thinking, shape, comprehension, counting, reading ability, language,
and judgment. Dementia affects millions of people and can be the leading cause of
death. It is now the seventh leading cause of death worldwide, as well as one of the
major causes of impairment and reliance on elderly people. There is no treatment for
dementia at present. The importance of early detection and diagnosis in improving
early and effective management is crucial. Predicting dementia in advance can lead us
to a better life. To predict dementia, various Machine Learning models have been used.
In this paper, Dementia is predicted on the basis of MRI Images, for this, three
different datasets of MRI Images have been collected. Furthermore, for better
prediction, various Machine learning models are used to predict dementia and validate
the performance with statistical analysis like K-Nearest Neighbours, XG Boost,
Support Vector Machine, Random Forest Algorithm (RFA), and Convolutional Neural
Network (CNN). Out of all algorithms, Random Forest Algorithm and Convolutional
Neural Network gave the best result with the accuracy of 93.2 and 99.9 respectively.
A Clinical Decision Support System for Effective Identification of the Onset of Asthma Disease
Page: 92-102 (11)
Author: M.R. Pooja*
DOI: 10.2174/9789815179125124010011
PDF Price: $15
Abstract
We present a clinical decision support system for the identification of
asthmatics in two different cohorts representing rural and urban populations in India.
The input data representing the two populations are cross-sectional in nature and are
necessarily categorical in nature, with information on clinical history emphasizing
clinical symptoms and patterns characterizing the disease. The system is described as
hybrid as it combines the unsupervised and supervised learning techniques in a unique
way as discussed in the work presented in the paper. The clustering information
emphasizing the phenotypic characterization of asthma is an input to the classifier and
a significant improvement is observed in the performance of the classifier. The results
of the developed hybrid decision support system are quite promising for suitable
deployment in a real-time scenario, as it explores the benefits of both supervised and
unsupervised learning techniques. Further, the use of clustering information in the form
of cluster evaluation scores as an input parameter to the classifiers can efficiently
predict disease outcomes, especially with diseases such as asthma, as the disease is
heterogeneous and exhibits several disease subtypes and heterogeneous phenotypes.
Applying Deep Learning and Computer Vision for Early Diagnosis of Eye Diseases
Page: 103-130 (28)
Author: Shradha Dubey* and Manish Dixit
DOI: 10.2174/9789815179125124010012
PDF Price: $15
Abstract
Medical image processing has a significant role in clinical investigation and
recent medical research. An appropriate image-based medical assessment helps to
analyze or detect critical diseases early, as it has a high value of medical information.
In this study, medical imaging is reviewed for the diagnosis of eye diseases using
computational intelligence. However, the identification of these diseases using
traditional image processing is quite complicated. Nowadays, various machine learning
and deep learning approaches are developed for the detection of different eye diseases
which are helpful for the detection of the diseases at an early stage. Research showed
that eye disorders are more serious in emerging or underdeveloped nations due to
inadequate healthcare facilities and skilled health workers. An estimate of 45 million
people around the world are blind and the tragic fact is that only 75% of these cases are
curable. Moreover, the doctor-patient ratio around the globe is about 1: 10,000.
Therefore, it takes an hour to create a screening system for the identification of these
illnesses. Ophthalmology is close to making breakthroughs in evaluating, diagnosing,
and treating eye diseases. Additionally, many eye and vision problems show no
obvious signs. As a consequence, people are often unaware that problems exist. Early
detection of diseases is a primary concern as they could be easily cured before leading
to severity. This research paper focuses on detecting eye illnesses, such as Diabetic
retinopathy, Diabetic Macular Edema, Glaucoma, Age macular Degeneration, Retinal
Vascular Occlusions, and Retinal Detachment. The authors explore various algorithms,
imaging modalities, and challenges in this context. The study aims to raise awareness
about eye disorders leading to blindness using computer vision, image processing, and
deep learning techniques. It also investigates how these machine learning and deep
learning approaches can aid in early disease diagnoses for effective treatment before
vision loss occurs.
The Fusion of Human-Computer Interaction and Artificial Intelligence Leads to the Emergence of Brain Computer Interaction
Page: 131-145 (15)
Author: M. Kiruthiga Devi*
DOI: 10.2174/9789815179125124010014
PDF Price: $15
Abstract
A personal computer may be used, with input devices such as a keyboard,
mouse, and joystick serving as an interface between the computers and the human. The
euphemistic, physically challenged are unable to use these computer systems, therefore,
BCI technology has advanced external applications to be managed without physical
movements in order to assist these physically disabled people and address the
limitations of HCI. The technological advancement in the field of cognitive
neuroscience and brain imaging has enabled it to communicate directly with the human
brain instead of using an interface. Rather than generating signals from muscle
movements, these systems use brain activity to monitor computers or communication
devices. Researchers in the field of Human-Computer Interaction (HCI) look at ways
for machines to utilize as many sensory sources as possible. Furthermore, researchers
have begun to consider implicit types of data, input that is not specifically performed to
instruct a machine to perform a task. Systems can evolve dynamically based on this
data in order to assist the user with the task at hand. Here we discussed components of
Brain-Computer Interface, its characteristics and challenges. The researchers are
attempting to replace conventional classifiers with Convolutional neural networks
(CNNs) that would provide a promising advantage in classification. The EEG signals
from the brain can be linked seamlessly to mechanical systems via BCI applications,
making it a rapidly growing technology that has applications in fields such as Artificial
Intelligence and Computational Intelligence.
Mining Standardized EHR Data: Exploration, Issues, and Solution
Page: 146-158 (13)
Author: Shivani Batra*, Vinay Kumar, Neha Kohli and Vaishali Arya
DOI: 10.2174/9789815179125124010015
PDF Price: $15
Abstract
Medical database is among the most crucial databases in terms of their
applicability to human life. Many researchers are in search of knowledge that is
abstracted within the data. Data mining is popular in today's world as it gives access to
knowledge that is otherwise unavailable. The concealed knowledge which is offered as
a result of it can help the individual to make better decisions. Data mining tools in
health have great potential. These solutions may be divided into four categories:
therapeutic efficacy assessment, patient care, customer service, and embezzlement
monitoring. The authors discovered that giving decision assistance in the medical
sector with an emphasis on electronic health records (EHRs) can save lives. Though
offering decision assistance in EHRs using data mining is valuable, it needs
consistency. As a result, the authors intend to use data mining methods on standardised
EHRs to create a decision support system. This paper presents the state-of-the-art data
mining approaches and their application in the healthcare sector. It provides an
integrated summary and a comparison detail of the existing literature. This chapter
surveys several issues that need to be handled before employing data mining on EHRs
and further proposes a solution for dealing with these problems. The problems such as
multiple origins, multiple formats, missing data, distinguished users, data granularity,
flexibility, and sparseness need immediate attention from researchers. Resolving these
problems is important to build an efficient standardized EHRs database.
Role of Database in Epidemiological Situation
Page: 159-171 (13)
Author: Kanika Soni, Shelly Sachdeva and Shivani Batra*
DOI: 10.2174/9789815179125124010016
PDF Price: $15
Abstract
In this technological era, the technology of databases is very essential to
many aspects of modern life. To give the prospective medical practitioner, the finest in
class and most recent medical knowledge, it seems mandatory that education in the
health domain be well-integrated with the most recent databases. This is because there
is a growing demand for it and there are benefits from the collaboration of healthrelated issues of the public and database technology. Database technology can help
improve health in several ways, including connecting geographically separated health
providers and patients, collecting data for research studies like drug and vaccine trials,
keeping track of chronic diseases, and guaranteeing that patients follow their prescribed
treatments. In this pandemic situation of COVID-19, which the whole world is
currently suffering, the current paper attempts to emphasize the databases’ role. It
illustrates how the COVID-19 Dataset can be stored, queried, and analyzed, and helps
in providing decision support to various end-users. We have performed descriptive
analysis by executing specific queries on the COVID-19 Dataset. Then, we performed
predictive analysis using two data analysis techniques on the COVID-19 Dataset to
approximate the situation in some major cities of India. Further, we have visualized our
results to get valuable information from our analysis.
Subject Index
Page: 172-177 (6)
Author: Geeta Rani, Vijaypal Singh Dhaka and Pradeep Kumar Tiwari
DOI: 10.2174/9789815179125124010017
Introduction
This book is a comprehensive review of technologies and data in healthcare services. It features a compilation of 10 chapters that inform readers about the recent research and developments in this field. Each chapter focuses on a specific aspect of healthcare services, highlighting the potential impact of technology on enhancing practices and outcomes. The main features of the book include 1) referenced contributions from healthcare and data analytics experts, 2) a broad range of topics that cover healthcare services, and 3) demonstration of deep learning techniques for specific diseases. Key topics: - Federated learning in analysis of sensitive healthcare data while preserving privacy and security. - Artificial intelligence for 3-D bone image reconstruction. - Detection of disease severity and creating personalized treatment plans using machine learning and software tools - Case studies for disease detection methods for different disease and conditions, including dementia, asthma, eye diseases - Brain-computer interfaces - Data mining for standardized electronic health records - Data collection, management, and analysis in epidemiological research The book is a resource for learners and professionals in healthcare service training programs and health administration departments.