The early birth of children can be associated with neurodevelopmental disease onset. In such cases, the lack of early diagnosis and early medical treatment negatively afects the rest of the child's life. In this context, recent developments in artificial intelligence (AI) in the medical field suggest a possible key role also in the cases of preterm birth through the integration of various sources of neurodiagnostic data in order to extract clinical information. In this manuscript, we have addressed the importance of the development of intelligent systems merging with the Internet of Medical Things (IoMT) for the analysis of the baby's movement. More in detail, we here consider a general prototype of an incubator for neonatal intensive care unit (NICU) and related tools capable of detecting/measuring vital signs and patient characteristics for newborns with particular attention to preterm infants. In this context, we will also provide a brief explanation of available datasets, such as BabyPose Dataset, MINI-RGBD, and MIA dataset. Furthermore, we will explore data mining techniques and the role of IoMT in the context of preterm infants and children. Finally, emphasis will be placed on technology communication, combination, and multidisciplinary research pursuing more accurate and improved self-guided techniques and systems.
According to World Health Organization (WHO) observations, the first month of life is a very
dangerous period for child survival, with 2.4 million newborns dying in 2020 [
The main causes of death from preterm birth are lack of breathing at birth, low birth weight,
illness, and other infection factors. In general, preterm birth is divided into three categories
according to the gestational age of delivery: moderate preterm (MP: 32-37 weeks), very preterm
(VP - 28-32 weeks), and extremely preterm (EP - less than 28 weeks) [
The normal and stable duration of delivery is considered completed when the pregnancy cycle exceeds 37 weeks of gestation. The earlier the birth, the higher the risk of death, and the need to monitor the preterm infant in the neonatal intensive care unit (NICU) for a long-time increase. Because of this critical condition, artificial intelligence systems for example coupled with incubator sensor systems can play an important role in overcoming the preterm mortality rate and improving the quality of care.
In recent years, many researchers have been working on the development of intelligence systems to improve the performance of neonatal behavior monitoring and analysis. In this area, contact and noncontact clinical data sources are being used to design automated intelligent systems. In addition, computer systems that were previously inadequate at the home of traditional and handcrafted features are now performing very well mainly due to the integration of machine learning and deep learning algorithms. In this regard, a general software architecture for neonatal sensing and monitoring is shown in Figure 1, which includes various IoMT-related concepts and technologies such as artificial intelligence, devices, sensors, big data, mobile devices, and what is considered for the design of state-of-the-art NICU incubators.
As said, monitoring neonatal behaviors is a key clinical activity for early diagnosis of possible
abnormalities or diseases. In this context, the AI and computer vision fields have given a lot of
attention to the automated identification and classification of newborn behaviors [
Based on the above-mentioned premises in the present review work we will discuss issues related to the role of AI systems and IoT that may help in designing automated systems to obtain accurate results and reduce the complexity rate in the field of NICU incubator implementation. More specifically, the article focuses on issues concerning: i) the knowledge base on the Neonatal Intensive Care Unit and related conditions; ii) data collection issues and publicly available reference datasets; iii) the role of data mining techniques for monitoring neurological disorders; iv) the role of IoT in the medical environment and the potential of the mentioned technologies in the field of incubator design and development.
An incubator is a device used to monitor clinical parameters and maintain environmental
conditions suitable for the life of a newborn baby. It is generally used in preterm birth or in
cases of specific pathologies at birth. The device is equipped with sensors that are capable
of monitoring/supporting the patient’s condition through the detection of behavioural and
physiological parameters (e.g. blood pressure, oxygenation, temperature, cardiac function, etc.)
of newborns that help doctors to prevent any morbidity leading to the critical phase [
In the following list, the main conditions requiring the use of incubators are detailed: • Intraventricular hemorrhage (IVH) Intraventricular hemorrhage (IVHs) causes the illness or disease and death of newborn infants. Infants whose birth weight is about 1500g usually develop an IVH. Mostly it occurs during the third day of birth and in some cases, it occurred before delivery. Important risk factors for IVHs are: increase atrial blood pressure, pneumothorax, and birth asphyxia [14, 15]. The potential of ML techniques to improve early detection of IVH has been highlighted in recent literature. • Periventricular leukomalacia (PVL) In this disease, the white matter near the cerebral ventricles dies. PVL is usually developed by premature infants, whose birthweight 1500g or 3lb 5oz. PVL afects infants (birth week < 25) when they are sufering from the deprivation of oxygen during delivery and at the time of birth [16]. The variation of oxygen and CO2 in the blood cause PVL while the surgeons predict this disease by applying standard psychological parameters to infants [17]. In the aspect of intelligent system development, many researchers have proposed several techniques to predict the PVL disease in neonates [18, 19]. • Nosocomial Infection Infections are the most common cause of mortality and illness for infants [20]. Around 45% of infants born before 25-28 weeks of gestation and kept alive in NICU incubators face critical infections. This infection, mainly caused by pathogens present in the hospital, is dificult to be identified at an early stage given that symptoms appear at the advanced pathological stage. Clinical checkups are mainly responsible for the infection spread [21]. Few examples of intelligent system for recognition of the above-mentioned neonatal infection are reported in literature [22, 23]. • Pneumothorax Pneumothorax occurs when air or gas accumulated in the process of inhaling and exhaling. In the body, the pleural cavity is a fluid-filled space that surrounds the lungs. Usually, 1-2% of infants face gas and air in their pleural cavities. There are two layers that surround the lungs. One is attached to the chest wall and the other is attached to the lungs. These layers move when we inhale or exhale, and in this process, fluid is emitted from the membrane for the lubrication of the lung’s smooth movement [14]. In pneumothorax, researchers have also used machine learning techniques to improve the detection as details are presented in [24, 25].
The implementation of a single intensive care unit is divided into two main types: real and simulated prototypes, where real prototype means the testing phase in a real environment while simulated prototypes are just computer-implemented and analyzed systems. In this regard, Figure 2 has shown the general prototype of the neonatal incubator.
There are several tools and systems which are usually connected with an incubator to monitor the condition of the baby at every moment. Due to these components, surgeons can easily analyze a number of vital signs and features like the warming system (body temperature, heart beat rate (HBR), and SpO2) and body behaviour systems (cameras). All these features of the neonatal are basically displayed on the incubator’s LCD. Moreover, the power supply systems are also connected to the incubator to manage the battery system. In any condition, the behaviour, oxygen level, or temperature is sensed as a bad outcome by the machine, it generates an alert buzzer which means the patient is in a critical stage. There are several parameters related to incubators that interact with IoT and further explanations are shown in table 1.
A. F. Symon et. al. [26] developed a system that detects the movement and crying sound of a newborn baby. The objective of this study was to analyze the contactless data modality to find the state of babies and also monitor their physical behaviour. It was suggested that previous systems were just controlling the temperature and humidity of the incubator without controlling the sound pollution which they found that it is a very mandatory parameter that can provide a comfortable environment to the baby. In another research [27], they designed a hardware system in combination with IoT’s based model to monitor the preterm incubator environment. The hardware components used microcontroller along with the other body temperature sensors. The performance of the proposed system was better as compared to the other related measuring systems. In this way, N. A. Zakaria et. al. [28] addressed another device to detect the infant body temperature in an incubator system. The device was a wearable sensor that measures the vital signs of baby and also sends the information to their parents through a wireless network. Furthermore, the portable device is utilized to visualize the information and any alert related to the baby health.
All the above-mentioned studies have shown contactless systems in detail but are not physically installed in any hospital. In recent research implemented at the John Radclife Hospital in Oxford [22]. This work has been designed similarly to previously discussed methods. They have adapted the video-based technique to monitor neonatals’ respiratory rate, heart rate, and oxygen saturation. By using these features, they have developed an algorithm that eficiently detects bradycardia events in the early stages.
PARAMETERS
SYSTEM MONITORING WITH THE PARAMETERS
[
Neonatal body temperature Incubator heat
Incubator Humidity [29, 31]
Neonatal body weight
This is a monitoring and risk management system, through cloud services, for neonates and it manages the critical stage alarm to domain experts for personal assistance.
This parameter maintains the incubator heat which is suficient for the development of the preterm baby.
This parameter controls the humidity level in the incubator and it helps in maintaining the temperature of the incubator.
By the help of this measure, surgeons came to know about the weight of neonate. In this way, they can analyze the growth of neonate based on his/her weight.
IT has cardinal importance in every aspect of our lives [32, 33, 34]. It has also proved itself as an important part of the medical field as well. Health IT is processing the information of diferent kinds of diseases using computer knowledge and its advancements. The capability of decision-making in health IT is a lot more than that of an individual human. As we know, computers can work more eficiently than humans. Health IT can assist all over the world’s medical community in diagnosing diferent diseases. Due to advancements in IT, the medical ifeld is also considering IT as an important part of it. The most tremendous thing in this IT domain is the amount of required data, that is available on the internet and anyone can access that information at any time [35].
However, this is not always the case with clinical data. Although there is a huge amount of clinical data that is collected by each hospital, this collection is usually very irregular and rough, both from one field to another but also in the same field in diferent countries and even in the same country from hospital to hospital. In fact, even today in many hospitals, data collection is done by hand by doctors or with the help of computer tools but often in a disorganized manner. This creates a huge problem in the pre- and post-processing of clinical data whose sets are often unusable. Added to this are the various problems of ethics and data privacy, which often require lengthy approval processes for their use. The case of the study of neurodevelopmental disorders presents a further degree of dificulty, given a large number of patients, which is certainly much smaller than in studies of cardiac diseases.
In this context, it is therefore crucial to develop systems that are able to automatically collect data in a standardized manner, but also to pre- and post-process collected data in order to boost the ability to extract useful clinical information from them. This is the context for all the IT technologies, IoMT that have been introduced above and that have the potential to drastically increase the quantity and quality of clinical data that could be available to data mining and ML-driven knowledge extraction algorithms. In this vision, a single NICU incubator becomes also a data collector for infant disease investigation based on real-world data. Nevertheless, some data for the analysis of the preterm‘s behaviour have already been collected and made available to the community. Those datasets are listed in Table 2.
Internet of Things (IoT) is an emerging technology that is increasing the data in various sectors daily. Big data analysis is a technique used to handle and evaluate enormous amounts of data using various methods. The IoT is a general paradigm. It changes its shape according to the environment, when we deal with the medical environment it is known as the IoMT. The objective of IoTs is to provide remote access to diferent physical devices and machines on service providers that cover location-based services, smart cities, smart streets, and homes. The IoT applications use invariably cloud storage combined with fog computing. Ubiquitous systems are increasing day by day and it reaches 50 billion in 2020 [31]. Nowadays, researchers have included many components in IoMT which have started to make the medical staf’s life very easy like web portals, WSN (Wireless Sensor Nodes), RFID (Radio Frequency Identification),
videos InfantsData[39]
videos
SyRIP[40]
SSBD[
Short Description The dataset contains 16 videos of 640 x 480 per frame size with 8–16-bit depth including 12 newborn cases with landmarks The dataset contains 12 videos having 640 x 480 per frame size with RGB Channels. This dataset is labeled on 25 infant babies The dataset contains 100 videos with 512 x 424 frame size. In this dataset, the behaviour of toddlers is labeled The dataset contains 85 videos with variant frame size and RGB channels. The dataset is labeled on 18 infant cases’ landmarks The dataset contains the RGB channel images of 17 infant patients 75 Youtube video, (m x n) frame size of RGB channels with benchmark dataset of behaviours of the preterm babies.
LCDs, detection sensors, etc [
In the near future, we hope to have a massive amount of data coming from the next generation
of incubators in NICUs. Several technologies for managing big data have already been developed
and successfully employed in other medical fields. In this context, it is worth mentioning that
ifve main strategies are recognized as successful in big data management: (1) create structured
big data, (2) data sharing culture to develop information, (3) training to use big data analytics,
(4) big data analytics with the combination of cloud computing, and (5) using big data analytics
techniques to generate new business ideas. The need of analytics is linked with improvement in
patient-centric services, detection of disease before it spreads, and -monitoring of the quality of
services and methods of treatment. Some tools like Apache Hadoop is highly scalable storage
platform. It provides cost-efective storage for large data. Apache Spark [
Modern IT has radically amplified the capacity and power of data mining and information extraction from data. Classical or ML/DL driven data mining concern the analysis of observational datasets to extract knowledge from them unraveled unknown relationships and rationalize data in useful ways for the end-user. In the context of data coming from NICU incubators, Figure 3 can be helpful in the development of an eficient automated health care system.
Following the flow described in Figure 3, we may identify the main techniques/steps
characterizing data mining technology. Those are general steps that can be specified depending on
the chosen application. For example, preprocessing applied to data concerning studies in Table
3 will be principally used to upgrade the nature of an image with diminishing varieties. This is
done to eradicate any infringements that cause entanglements in the preparing stage which
cause broad utilization of reality assets [
Classification, data rationalization, knowledge extraction, and statistical formulation usually
follow the preprocessing and can be combined or not. There are a high number of application
examples of data mining techniques applied to the medical field. In Table 3 we report the most
important application related to neonatal behavioral investigation. Again, all these approaches
can be considered for further application in concert with the novel generation of NICU incubators
for data analysis and knowledge extraction to support clinical decisions and precision medicine.
Study
[
In the context of neonatal care, software architectures for storing data, to be then analyzed by data mining techniques, should consider innovative tools for heterogeneous (structured and unstructured) data collection as data that may be collected through hardware diagnostic devices, custom sensors, and software solutions installed in a NICU incubator (or a set of NICU incubators). Those data either the raw data or the processed data – should thus be handled as sensitive data, considering the appropriate ethical and privacy procedures, amongst which compliance to the General Data Protection Regulation (GDPR). Examples of data sources are (a) clinical data and Electronic health records (EHRs); (b) imaging data; (c) IoT device data streams. It is worth noticing that developing data collectors and management systems for neonatal care may take advantage of existent tools already applied to manage data in other fields of medicine and explicitly developed to manage patient health data with all the protection systems that need to be used for this type of sensitive data.
The present research work has been developed as part of the PARENT project, funded by the European Union’s Horizon 2020 research and innovation program under the Marie SklodowskaCurie-Innovative Training Network 2020, Grant Agreement N° 956394 (https://parenth2020. com/).
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