Tag: Artificial Intelligence

Summary

Machine learning and deep learning, integral components of artificial intelligence, involve the process of training computers to learn and make informed decisions based on diverse datasets. Notably, recent strides in artificial intelligence predominantly emanate from the realm of deep learning, proving transformative across various domains, ranging from computer vision to health sciences. The impact of deep learning on medical practices has been particularly groundbreaking, reshaping traditional approaches to clinical applications. While certain medical subfields, such as pediatrics, initially lagged in harnessing the substantial advantages of deep learning, there is a noteworthy accumulation of related research in pediatric applications.

This paper undertakes a comprehensive review of newly developed machine learning and deep learning solutions tailored for neonatology applications. The assessment systematically explores the roles played by both classical machine learning and deep learning in neonatology, elucidating methodologies, including algorithmic advancements. Furthermore, it delineates the persisting challenges in evaluating neonatal diseases, adhering to PRISMA 2020 guidelines. The predominant areas of focus within neonatology’s AI applications encompass survival analysis, neuroimaging, scrutiny of vital parameters and biosignals, and the diagnosis of retinopathy of prematurity.

Drawing on 106 research articles spanning from 1996 to 2022, this systematic review meticulously categorizes and discusses their respective merits and drawbacks. The overarching goal of this study is to augment comprehensiveness, delving into potential directions for novel AI models and envisioning the future landscape of neonatology as AI continues to assert its influence. The narrative concludes by proposing roadmaps for the seamless integration of AI into neonatal intensive care units, foreseeing transformative implications for the field.

Commencement

The ongoing surge of artificial intelligence (AI) is reshaping diverse sectors, healthcare included, in an ever-evolving landscape. The dynamic nature of AI applications makes it challenging to keep pace with the constant innovations. Despite the profound impact of AI on daily life, many healthcare practitioners, especially in less-explored domains like neonatology, may not fully grasp the extent of AI integration into contemporary healthcare systems. This review aims to bridge this awareness gap, specifically targeting physicians navigating the intricate intersection of AI and neonatology.

The roots of AI, particularly within machine learning (ML), can be traced back to the 1950s, when Alan Turing conceptualized the “learning machine” alongside early military applications of rudimentary AI. During this era, computers were colossal, and the costs associated with expanding storage were exorbitant, limiting their capabilities. Over the ensuing decades, incremental advancements in both theoretical frameworks and technological infrastructure progressively enhanced the potency and adaptability of ML.

Understanding the mechanics of ML and its subset, deep learning (DL), is fundamental. ML, as a subset of AI, garnered attention due to its adeptness in handling data. ML algorithms and models possess the ability to learn from data, scrutinize, assess, and formulate predictions or decisions based on acquired insights. DL, a specialized form of ML, draws inspiration from the human brain’s neural networks, replicating their functionality through artificial neurons in computer neural networks. The distinctive feature of DL lies in its hierarchical architecture, facilitating the autonomous extraction of features from data, a departure from the reliance on human-engineered features in conventional ML.

Distinguishing ML from DL hinges on the complexity of models and the scale of datasets they can manage. ML algorithms prove effective across a spectrum of tasks, exhibiting simplicity in training and deployment. Conversely, DL algorithms necessitate larger datasets and intricate models but excel in tasks involving high-dimensional, intricate data, automatically identifying significant aspects without predefined elements of interest. The non-linear activation functions within DL architectures, such as artificial neural networks (ANN), contribute to the learning of complex features representative of provided data samples.

ML and DL fall into categories such as supervised, unsupervised, or reinforcement learning based on the nature of the input-output relationship. Their applications span classification, regression, and clustering tasks. DL’s success is contingent on large-scale data availability, innovative optimization algorithms, and the accessibility of Graphics Processing Units (GPUs). These autonomous learning algorithms, mirroring human learning processes, position DL as a pioneering ML method, catalyzing substantial transformations across medical and technological domains, marking it as the driving force propelling contemporary AI advancements.

In the field of deep learning (DL) applied to medical imaging, three primary problem categories exist: image segmentation, object detection (identifying organs or other anatomical/pathological entities), and image classification (e.g., diagnosis, prognosis, therapy response assessment)3. Numerous DL algorithms are commonly utilized in medical research, falling into specific algorithmic families:

1. Convolutional Neural Networks (CNNs): Mainly applied in computer vision and signal processing tasks, CNNs excel in tasks involving fixed spatial relationships, such as imaging data. The architecture comprises phases (layers) facilitating the acquisition of hierarchical features. Initial phases extract local features (corners, edges, lines), while subsequent phases extract more global features. Feature propagation involves nonlinearities and regularizations, enriching feature representation. Pooling operations reduce feature size, and the resulting features are employed for predictions (segmentation, detection, or classification)3,16.
2. Recurrent Neural Networks (RNNs): Tailored for retaining sequential data like text, speech, and time-series data (e.g., clinical or electronic health records). RNNs capture temporal relationships, aiding in predicting disease progression or treatment outcomes11,17,18. Long Short-Term Memory (LSTM) models, a subtype of RNNs, address limitations by learning long-term dependencies more effectively. LSTMs use a gated memory cell to store information, allowing them to learn complex patterns, particularly useful in audio classification17,19.
3. Generative Adversarial Networks (GANs): A DL model class used for generating new data resembling existing datasets. In healthcare, GANs create synthetic medical images using two CNNs (generator and discriminator). The generator produces synthetic images mimicking real ones, while the discriminator identifies artificially generated images. Adversarial training ensures the generator generates realistic data. GANs are versatile, employed for tasks like image enhancement, signal reconstruction, classification, and segmentation20,21,22.
4. Transfer Learning (TL): Rooted in cognitive science, TL minimizes annotation needs by transferring knowledge from pretrained models. However, using ImageNet pre-trained models for medical image classification can be inefficient due to differences between natural images and medical images25,26. Fine-tuning more layers in CNNs may improve accuracy, as the initial layers of ImageNet-pretrained networks may not efficiently detect low-level characteristics in medical images25,26.
5. Advanced DL Algorithms: Evolving daily, new methods such as Capsule Networks, Attention Mechanisms, and Graph Neural Networks (GNNs)27,28,29,30 are employed for imaging and non-imaging data analysis. Capsule Networks address CNN shortcomings, Attention Mechanisms enhance context understanding, and GNNs exhibit potential in both imaging and non-imaging data analysis28,34.

In the imaging field, both data-driven and physics-driven systems are essential. While DL methods show effectiveness, challenges persist, particularly in MRI construction where data-driven and physics-driven algorithms are employed based on the impracticality of acquiring fully sampled datasets.

The concept of Hybrid Intelligence, combining AI with human intellect, offers a promising collaborative approach. AI processes extensive data rapidly, while human expertise contributes context and intuition, leading to more precise decision-making. Hybrid intelligence systems hold promise for time-consuming tasks in healthcare and neonatology.

In the current landscape, AI in medicine has been in use for over a decade, with advancements in algorithms and hardware technologies contributing to its potential. Challenges remain as healthcare utilization increases, particularly in integrating AI into daily clinical practice.

Clinicians seek enhanced diagnostic tools from AI, anticipating reduced invasive tests and increased diagnostic accuracy. This systematic review aims to explore AI’s potential role in neonatology, envisioning a future where hybrid intelligence optimizes neonatal care. The study outlines AI applications, evaluation metrics, and challenges in neonatology, providing a comprehensive overview. The paper’s objectives encompass thorough explanations of AI models, categorization of neonatology-related applications, and discussions of challenges and future research directions.

To elucidate a comprehensive understanding, the objectives are:

1. Provide a detailed explanation of various AI models and thoroughly articulate evaluation metrics, elucidating the key features inherent in these models.
2. Categorize AI applications pertinent to neonatology into overarching macro-domains, expounding on their respective sub-domains and highlighting crucial aspects of the applicable AI models.
3. Scrutinize the contemporary landscape of studies, especially those in recent years, with a specific focus on the widespread utilization of machine learning (ML) across the entire spectrum of neonatology.
4. Furnish an exhaustive and well-structured overview, including a classification of Deep Learning (DL) applications deployed within the realm of neonatology.
5. Assess and engage in a discourse concerning prevailing challenges linked to AI implementation in neonatology, along with contemplating future avenues for research. This aims to provide clinicians with a comprehensive perspective on the current scenario.

I encompass a comprehensive concept that involves the application of computational algorithms capable of categorizing, predicting, or deriving valuable conclusions from vast datasets. Over the past three decades, various algorithms, including Naive Bayes, Genetic Algorithms, Fuzzy Logic, Clustering, Neural Networks (NN), Support Vector Machines (SVM), Decision Trees, and Random Forests (RF), have been utilized for tasks such as detection, diagnosis, classification, and risk assessment in the field of medicine. Traditional machine learning (ML) methods often incorporate hand-engineered features, which consist of visual descriptions and annotations learned from radiologists and are encoded into algorithms for image classification.

Medical data encompasses diverse unstructured sources such as images, signals, genetic expressions, electronic health records (EHR), and vital signs (refer to Fig. 3). The intricate structures of these data types allow deep learning (DL) frameworks to leverage their heterogeneity, achieving high levels of abstraction in data analysis.

While machine learning (ML) necessitates manual selection and crafted transformation of information from incoming data, deep learning (DL) executes these tasks more efficiently and with heightened efficacy. DL achieves this by autonomously uncovering components through the analysis of a large number of samples, a process that is highly automated. The literature extensively covers ML approaches predating the advent of DL.

For clinicians, understanding how the recommended ML model enhances patient care is crucial. Given that a single metric cannot encapsulate all desirable attributes, it is customary to describe a model’s performance using various metrics. Unfortunately, end-users often struggle to comprehend these measurements, making it challenging to objectively compare models across different research endeavors. Currently, there is no available method or tool to compare models based on the same performance measures. This section elucidates common ML and DL evaluation metrics to empower neonatologists in adapting them to their research and understanding upcoming articles and research design.

The widespread application of artificial intelligence (AI) spans daily life to high-risk medical scenarios. In neonatology, the incorporation of AI has been a gradual process, with numerous studies emerging in the literature. These studies leverage various imaging modalities, electronic health records, and ML algorithms, some of which are still in the early stages of integration into clinical workflows. Despite the absence of specific systematic reviews and future discussions in this field, several studies have focused on introducing AI systems to neonatology. However, their success has been limited. Recent advancements in DL have, however, shifted research in this field in a more promising direction. Evaluation metrics in these studies commonly include standard measures such as sensitivity (true-positive rate), specificity (true-negative rate), false-positive rate, false-negative rate, receiver operating characteristics (ROC), area under the ROC curves (AUC), and accuracy (Table 1).

Results

This systematic review adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol56. The search process was concluded on July 11, 2022. Initially, a substantial number of articles (approximately 9000) were identified, and a systematic approach was employed to screen and select pertinent articles based on their alignment with the research focus, study design, and relevance to the topic. After reviewing article abstracts, 987 studies were identified. Ultimately, our search resulted in the inclusion of 106 research articles spanning the years 1996 to 2022 (Fig. 4). The risk of bias was assessed using the QUADAS-2 tool.

Our discoveries are condensed into two sets of tables: Tables 2–5 outline AI techniques from the pre-deep learning era (“Pre-DL Era”) in neonatal intensive care units, categorized by the type of data and applications. Conversely, Tables 6 and 7 encompass studies from the DL Era, focusing on applications such as classification (prediction and diagnosis), detection (localization), and segmentation (pixel-level classification in medical images).

[^194^]: Hoshino et al., 2017
[^195^]: Dong et al., 2021
[^90^]: Ball et al., 2015
[^88^]: Smyser et al., 2016
[^93^]: Zimmer et al., 2017
[^100^]: Krishnan et al., 2017
[^91^]: Chiarelli et al., 2019
[^94^]: Song et al., 2017
[^137^]: Taylor et al., 2017
[^134^]: Ataer-Cansizoglu et al., 2015
[^133^]: Rani et al

Certainly! Here is the rewritten table:

Machine Learning Utilizations in Neonatal Mortality: A Comprehensive Overview

Neonatal mortality stands as a significant contributor to overall child mortality, representing 47 percent of deaths in children under the age of five, according to the World Health Organization60. The imperative to reduce global infant mortality by 203061 underscores the urgency of addressing this issue.

Machine Learning (ML) has been applied to investigate infant mortality, its determinants, and predictive modeling62,63,64,65,66,67,68. A recent study enrolled 1.26 million infants, predicting mortality as early as 5 minutes and as late as 7 days using an array of models, predominantly neural networks, random forests, and logistic regression (58.3%)67. While several studies reported favorable results, including AUC ranging from 58.3% to 97.0%, challenges such as small sample sizes and lack of dynamic parameter representation hindered broader clinical applicability67. Notably, gestational age, birth weight, and APGAR scores emerged as pivotal variables64,72. Future research recommendations emphasize external validation, calibration, and integration into healthcare practices67.

Neonatal sepsis, encompassing early and late onset, remains a formidable challenge in neonatal care, prompting ML applications for early detection. Studies predicted early sepsis using heart rate variability and clinical biomarkers with accuracies ranging from 64% to 94%74,75.

Advancements in neonatal healthcare have reduced severe prenatal brain injury incidence but underscored the need to predict neurodevelopmental outcomes. ML methods, including brain segmentation, connectivity analysis, and neurocognitive evaluations, have been employed to address this. Additionally, ML aids in neuromonitorization, such as automatic seizure detection from EEG and analyzing EEG biosignals in infants with hypoxic-ischemic encephalopathy (HIE)104,105,106,107,108.

ML applications extend to predicting complications in preterm infants, including Patent Ductus Arteriosus (PDA), Bronchopulmonary Dysplasia (BPD), and Retinopathy of Prematurity (ROP). PDA detection from electronic health records (EHR) and auscultation records demonstrated accuracies of 76% and 74%, respectively123,124. ML studies predicted BPD with accuracies up to 86%, and other research aimed to predict complications related to long-term invasive ventilation128,129,130.

ROP, a leading cause of childhood blindness, has seen ML applications in diagnosing and classifying from retinal fundus images, with systems achieving up to 95% accuracy132,133,134.

ML also finds utility in the diagnosis of various neonatal diseases, utilizing EHR and medical records for conditions like congenital heart defects, HIE, IVH, neonatal jaundice, NEC, and predicting rehospitalization135,136,84,85,137,138,139,142,143.

Furthermore, ML has been applied to analyze electronically captured physiologic data for artifact detection, late-onset sepsis prediction, and overall morbidity evaluation144,145,146.

In addressing metabolic disorders of newborns, ML methods, especially Support Vector Machines (SVM), have been employed for conditions like methylmalonic acidemia (MMA), phenylketonuria (PKU), and medium-chain acyl CoA dehydrogenase deficiency (MCADD)151,152,153. Notably, ML contributes to improving the positive predictive value in newborn screening programs for these disorders152.

In summary, ML applications span a wide spectrum in neonatal care, from predicting mortality and sepsis to assessing neurodevelopmental outcomes and complications in preterm infants, showcasing its diverse and impactful role in improving neonatal healthcare.

Deep Learning Advancements in Neonatology

Deep Learning in clinical image analysis serves three primary purposes: classification, detection, and segmentation. Classification focuses on identifying specific features in an image, detection involves locating multiple features within an image, and segmentation entails dividing an image into multiple parts7,9,154,155,156,157,158,159,160.

AI-Enhanced Neuroradiological Assessment in Neonatology

Neonatal neuroimaging plays a crucial role in identifying early signs of neurodevelopmental abnormalities, enabling timely intervention during a period of heightened neuroplasticity and rapid cognitive and motor development. The application of Deep Learning (DL) methods enhances the diagnostic process, providing earlier insights than traditional clinical signs would indicate.

However, imaging an infant’s brain using Magnetic Resonance Imaging (MRI) poses challenges due to lower tissue contrast, regional heterogeneity, age-related intensity variations, and the impact of partial volume effects. To address these issues, specialized computational neuroanatomy tools tailored for infant-specific MRI data are under development. The typical pipeline for predicting neurodevelopmental disorders from infant structural MRI involves image preprocessing, tissue segmentation, surface reconstruction, and feature extraction, followed by AI model training and prediction.

Segmenting a newborn’s brain is particularly challenging due to decreased signal-to-noise ratio, motion restrictions, and the smaller brain size. Various non-DL-based approaches, including parametric, classification, multi-atlas fusion, and deformable models, have been proposed for newborn brain segmentation. The evaluation metric, Dice Similarity Coefficient, measures segmentation accuracy.

The future of neonatal brain segmentation research involves developing more sophisticated neural segmentation networks. Despite advancements, the slow progress in the field of artificial intelligence in neonatology is attributed to a lack of open-source algorithms and limited datasets.

Further research should focus on enhancing DL accuracy in diagnosing conditions such as germinal matrix hemorrhage. Comparisons between DL and sonographers in identifying suspicious studies, grading hemorrhages accurately, and improving diagnostic capabilities of head ultrasound in diverse clinical scenarios warrant attention.

The evaluation of prematurity complications using DL in neonatology encompasses various applications, including disease prediction, MR image analysis, combined EHR data analysis, and predicting neurocognitive outcomes and mortality. DL proves effective in detecting conditions like PDA, IVH, BPD, ROP, and retinal hemorrhage. Additionally, DL contributes to treatment planning, NICU discharge, personalized medicine, and follow-up care.

DL’s potential in ROP screening programs is notable, offering cost-effective solutions for detecting severe cases that require therapy. Studies show DL outperforming experts in diagnosing plus disease and quantifying the clinical progression of ROP. DL applications extend to sleep protection in the NICU, real-time evaluation of cardiac MRI for congenital heart disease, classification of brain dysmaturation from neonatal brain MRI, and disease classification from thermal images.

Two groundbreaking studies emphasize the impact of DL on nutrition practices in NICU and the use of wireless sensors. ML techniques, unbiased and data-driven, showcase the potential to bring about clinical practice changes and improve monitoring, preventing iatrogenic injuries in neonatal care.

Discussion

The studies in neonatology involving AI were systematically categorized based on three primary criteria:

(i) Whether the studies utilized Machine Learning (ML) or Deep Learning (DL) methods,
(ii) Whether imaging data or non-imaging data were employed in the studies, and
(iii) According to the primary aim of the study, whether it focused on diagnosis or other predictive aspects.

In the pre-Deep Learning era, the majority of neonatology studies were conducted using ML methods. Specifically, we identified 12 studies that utilized ML along with imaging data for diagnostic purposes. Furthermore, 33 studies employed non-imaging data for diagnostic applications. The spectrum of imaging data studies covered diverse areas such as biliary atresia (BA) diagnosis based on stool color, postoperative enteral nutrition for neonatal high intestinal obstruction, functional brain connectivity in preterm infants, retinopathy of prematurity (ROP) diagnosis, neonatal seizure detection from video records, and newborn jaundice screening. Non-imaging studies for diagnosis included congenital heart defects diagnosis, baby cry analysis, inborn metabolic disorder diagnosis and screening, hypoxic-ischemic encephalopathy (HIE) grading, EEG analysis, patent ductus arteriosus (PDA) diagnosis, vital sign analysis, artifact detection, extubation and weaning analysis, and bronchopulmonary dysplasia (BPD) diagnosis.

In contrast, studies involving Deep Learning applications were less prevalent compared to Machine Learning. DL studies focused on brain segmentation, intraventricular hemorrhage (IVH) diagnosis, EEG analysis, neurocognitive outcome prediction, and PDA and ROP diagnosis. While the DL field is expected to witness increased research in upcoming articles, it is noteworthy that there have been several articles and studies on the application of AI in neonatology. However, many of these lack sufficient details, making it challenging to evaluate and compare them comprehensively, thus limiting their utility for clinicians.

Several limitations exist in the application of AI in neonatology, including the absence of prospective design, challenges in clinical integration, small sample sizes, and evaluations limited to single centers. DL has demonstrated potential in extracting information from clinical images, bioscience, and biosignals, as well as integrating unstructured and structured data in Electronic Health Records (EHR). However, key concerns related to DL in medicine include difficulties in clinical integration, the need for expertise in decision mechanisms, lack of data and annotations, insufficient explanations and reasoning capabilities, limited collaboration efforts across institutions, and ethical considerations. These challenges collectively impact the success of DL in the medical domain and are categorized into six components for further examination.

Challenges in Integrating Clinical Practices

“Challenges in Integrating AI into Neonatal Healthcare: A Perspective on Clinical Trials

Despite the significant advancements in AI accuracy within the healthcare domain, translating these achievements into practical treatment pathways faces multiple hurdles. One major concern among physicians is the lack of well-established randomized clinical trials, particularly in pediatrics, demonstrating the reliability and enhanced effectiveness of AI systems compared to traditional methods for diagnosing neonatal diseases and recommending suitable therapies. Comprehensive discussions on the pros and cons of such studies are presented in tables and relevant sections. Current research predominantly focuses on imaging-based or signal-based investigations, often centered around a specific variable or disease. Neonatologists and pediatricians express the need for evidence-based algorithms with proven efficacy. Remarkably, there are only six prospective clinical trials in neonatology involving AI. One notable trial, supported by the European Union Cost Program, explores the detection of neonatal seizures using conventional EEG in the NICU197. Another study investigates the physiological effects of music in premature infants208, though it does not employ AI analysis. A recent trial, “Rebooting Infant Pain Assessment: Using Machine Learning to Exponentially Improve Neonatal Intensive Care Unit Practice (BabyAI),” is currently recruiting209. Another ongoing study aims to collect real-time data on pain signals in non-verbal infants using sensor-fusion and machine learning algorithms210. However, no results have been submitted yet. Similarly, the “Prediction of Extubation Readiness in Extreme Preterm Infants by the Automated Analysis of Cardiorespiratory Behavior: APEX study” completed recruitment with 266 infants, but results are pending211. In summary, there is a notable scarcity of prospective multicenter randomized AI studies with published results in the neonatology field. Addressing this gap requires planning clinically integrated prospective studies that incorporate real-time data collection, considering the rapidly changing clinical circumstances of infants and the inclusion of multimodal data with both imaging and non-imaging components.”

Requisite Expertise in Decision-Making Mechanisms

“In the realm of neonatology, considering whether to heed a system’s recommendation may necessitate the presentation of corroborative evidence95,96,125,202. Many proposed AI solutions in the medical domain are not meant to supplant the decision-making or expertise of physicians but rather serve as valuable aids. In the challenging landscape of neonatal survival without sequelae, AI could revolutionize neonatology. The diverse spectrum of neonatal diseases, coupled with varying clinical presentations based on gestational age and postnatal age, complicates accurate diagnoses for neonatologists. AI holds the potential for early disease detection, offering crucial assistance to clinicians for prompt responses and favorable therapeutic outcomes.

Neonatology involves collaborative efforts across multiple disciplines in patient management, presenting an opportunity for AI to achieve unprecedented levels of efficacy. With increased resources and support from physicians, AI could make significant contributions to neonatology. Collaboration extends to various pediatric specialties, such as perinatology, pediatric surgery, radiology, pediatric cardiology, pediatric neurology, pediatric infectious disease, neurosurgery, cardiovascular surgery, and other subspecialties. These multidisciplinary workflows, involving patient follow-up and family engagement, could benefit from AI-based predictive analysis tools to address potential risks and neurological issues. AI-supported monitoring systems, capable of analyzing real-time data from monitors and detecting changes simultaneously, would be valuable not only for routine Neonatal Intensive Care Unit (NICU) care but also for fostering “family-centered care”212,213 initiatives. While neonatologists remain central to decision-making and communication with parents, AI could actively contribute to NICU practices. Hybrid intelligence offers a platform for monitoring both abrupt and subtle clinical changes in infants’ conditions.

The limited understanding of Deep Learning (DL) among many medical professionals poses a challenge in establishing effective communication between data scientists and medical specialists. A considerable number of medical professionals, including pediatricians and neonatologists, lack familiarity with AI and its applications due to limited exposure to the field. However, efforts are underway to bridge this gap, with clinicians taking the lead in AI initiatives through conferences, workshops, courses, and even coding schools214,215,216,217,218.

Looking ahead, neonatal critical conditions are likely to be monitored by human-in-the-loop systems, and AI-empowered risk classification systems may assist clinicians in prioritizing critical care and allocating resources precisely. While AI cannot replace neonatologists, it can serve as a clinical decision support system in the dynamic and urgent environment of the NICU, calling for prompt responses.”

Challenges Stemming from Insufficient Imaging Data, Annotations, and Reproducibility Issues

“There is a growing interest in leveraging deep learning methodologies for predicting neurological abnormalities using connectome data; however, their application in preterm populations has been restricted. Similar to most deep learning (DL) applications, these models often necessitate extensive datasets. Yet, obtaining large neuroimaging datasets, especially in pediatric settings, remains challenging and costly. DL’s success depends on well-labeled, high-capacity models trained with numerous examples, posing a significant challenge in the realm of neonatal AI applications.

Accurate labeling demands considerable physician effort and time, exacerbating the existing challenges. Unfortunately, there is a lack of large-scale collaboration between physicians and data scientists that could streamline data gathering, sharing, and labeling processes. Overcoming these challenges holds the promise of utilizing DL in prevention and diagnosis programs, fundamentally transforming clinical practice. Here, we explore the potential of DL in revolutionizing various imaging modalities within neonatology and child health.

The need for a massive volume of data is a significant impediment, especially with the increasing sophistication of DL architectures. However, collecting a substantial amount of clean, verified, and diverse data for various neonatal applications is challenging. Data augmentation techniques and building models with shallow networks are proposed solutions, though they may not be universally applicable. Additionally, issues arise in generalizing models to new data, especially considering variations in MRI contrasts, scanners, and sequences between institutions. Continuous learning strategies are suggested to address this challenge.

Most studies lack open-source algorithms and fail to clarify validation methods, introducing methodological bias. Reproducibility becomes a crucial concern for comparing algorithm success. Furthermore, the lack of explanations and reasoning in widely used DL models poses a risk, especially in high-stakes medical settings. Trustworthiness is imperative for the widespread adoption of AI in neonatology.

Collaboration efforts across multiple institutions face privacy concerns related to cross-site sharing of imaging data. Federated learning has been proposed to address privacy issues, but data heterogeneity may impact model efficacy. Ethical concerns, including informed consent, bias, safety, transparency, patient privacy, and allocation, add complexity to health AI. The implementation of an ethics framework in neonatology AI is yet to be reported.

Despite these challenges, the potential benefits of AI in healthcare are substantial, including increased speed, cost reduction, improved diagnostic accuracy, enhanced efficiency, and increased access to clinical information. AI’s impact on neonatal intensive care units and healthcare, while promising, requires clinicians’ support, emphasizing the need for collaboration between AI researchers and clinicians for successful implementation in neonatal care.”

Approaches Employed

“Review of Literature and Search Methodology
We conducted a comprehensive literature search using databases such as PubMed™, IEEEXplore™, Google Scholar™, and ScienceDirect™ to identify publications related to the applications of AI, ML, and DL in neonatology. Our search strategy involved various combinations of keywords, including technical terms (AI, DL, ML, CNN) and clinical terms (infant, neonate, prematurity, preterm infant, hypoxic ischemic encephalopathy, neonatology, intraventricular hemorrhage, infant brain segmentation, NICU mortality, infant morbidity, bronchopulmonary dysplasia, retinopathy of prematurity). The inclusion criteria were publications dated between 1996 and 2022, focusing on AI in neonatology, written in English, published in peer-reviewed journals, and objectively assessing AI applications in neonatology. Exclusions comprised review papers, commentaries, letters to the editor, purely technical studies without clinical context, animal studies, statistical models like linear regression, non-English language studies, dissertation theses, posters, biomarker prediction studies, simulation-based studies, studies involving infants older than 28 days, perinatal death studies, and obstetric care studies. The initial search yielded around 9000 articles, from which 987 relevant research papers were identified through careful abstract examination (Fig. 4). Ultimately, 106 studies meeting our criteria were selected for inclusion in this systematic review (see Supplementary file). The evaluation covered diverse aspects, including sample size, methodology, data types, evaluation metrics, as well as the strengths and limitations of the studies (Tables 2–7).

Availability of Data
Dr. E. Keles and Dr. U. Bagci have complete access to all study data, ensuring data integrity and accuracy. All study materials are accessible upon reasonable request from the corresponding author.”