Future of machine learning in paediatrics

Clarke, Sarah L N; Parmesar, Kevon; Saleem, Moin A.; Ramanan, Athimalaipet V

doi:10.1136/archdischild-2020-321023

Cited by 14 publications

(4 citation statements)

References 44 publications

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“…Machine learning (ML) is now the key to AI and the fundamental way to make computers intelligent, allowing machines to recognize patterns and make decisions with minimal human intervention [ 34 ]. In fact, ML is a combination of computer science and statistics [ 35 ]. In organoid analysis, ML teaches computers to identify organoid phenotypes, allowing programs to process large amounts of biomedical data without relying on manual modification of parameters [ 36 ].…”

Section: The Development Of Artificial Intelligence (Ai) and Its Adva...mentioning

confidence: 99%

Organoids revealed: morphological analysis of the profound next generation in-vitro model with artificial intelligence

Chen

et al. 2023

Bio-des. Manuf.

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In modern terminology, “organoids” refer to cells that grow in a specific three-dimensional (3D) environment in vitro, sharing similar structures with their source organs or tissues. Observing the morphology or growth characteristics of organoids through a microscope is a commonly used method of organoid analysis. However, it is difficult, time-consuming, and inaccurate to screen and analyze organoids only manually, a problem which cannot be easily solved with traditional technology. Artificial intelligence (AI) technology has proven to be effective in many biological and medical research fields, especially in the analysis of single-cell or hematoxylin/eosin stained tissue slices. When used to analyze organoids, AI should also provide more efficient, quantitative, accurate, and fast solutions. In this review, we will first briefly outline the application areas of organoids and then discuss the shortcomings of traditional organoid measurement and analysis methods. Secondly, we will summarize the development from machine learning to deep learning and the advantages of the latter, and then describe how to utilize a convolutional neural network to solve the challenges in organoid observation and analysis. Finally, we will discuss the limitations of current AI used in organoid research, as well as opportunities and future research directions. Graphic abstract

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Section: The Development Of Artificial Intelligence (Ai) and Its Adva...mentioning

confidence: 99%

Organoids revealed: morphological analysis of the profound next generation in-vitro model with artificial intelligence

Chen

et al. 2023

Bio-des. Manuf.

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“…For example, Instagram posts have been used as a predictor of physical activity in adults aged 18–30 years (Liu et al, 2021 ). A comprehensive review of machine learning in paediatrics is covered elsewhere (Clarke et al, 2022 ), supporting the value of AI in the field of precision diagnostics in paediatrics. As electronic patient records become the global norm, it is likely that the application AI will become fundamental to improve diagnosis and clinical outcomes.…”

Section: A Digital Approach To Precision Diagnostics and Medicine In ...mentioning

confidence: 99%

Precision diagnostics in children

Dimitri¹

2023

Camb. prisms Precis. med.

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Medical practice is transforming from a reactive to a pro-active and preventive discipline that is underpinned by precision medicine. The advances in technologies in such fields as genomics, proteomics, metabolomics, transcriptomics and artificial intelligence have resulted in a paradigm shift in our understanding of specific diseases in childhood, greatly enhanced by our ability to combine data from changes within cells to the impact of environmental and population changes. Diseases in children have been reclassified as we understand more about their genomic origin and their evolution. Genomic discoveries, additional 'Omics' data and advances such as optical genome mapping have driven rapid improvements in the precision and speed of diagnoses of diseases in children, and are now being incorporated into newborn screening, have improved targeted therapies in childhood, and have supported the development of predictive biomarkers to assess therapeutic impact and determine prognosis in congenital and acquired diseases of childhood.New medical device technologies are facilitating data capture at a population level to support higher diagnostic accuracy and tailored therapies in children according to predicted population outcome, and digital ecosystems now tailor therapies and provide support for their specific needs. By capturing biological and environmental data as early as possible in childhood, we can understand factors that predict disease or maintain health, and track changes across a more extensive longitudinal path. Data from multiple health and external sources over long time periods starting from birth or even in the in-utero environment will provide further clarity about how to sustain health and prevent or predict disease. In this respect, we will not only use data to diagnose disease, but precision diagnostics will aid the 'diagnosis of good health'. The principle of 'start early and gain more' will thus underpin the value of applying a personalised medicine approach early in life.

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“…Amongst the plethora of clinical applications of AI models obtained through FL, there is still lack of research focusing on pediatric care, despite the current workforce challenges and rising demands on pediatric services due to increased patient complexity and comorbidity [4]. In this context, the Secur-e-Health project aims at developing a solution in terms of Clinical Decision Support (CDS) tools encompassing FL predictive models and recommendation systems for smart pediatric care.…”

Section: Introductionmentioning

confidence: 99%

Secur-e-Health Project: Towards Federated Learning for Smart Pediatric Care

Rb-Silva

Ribeiro

Almeida

et al. 2023

Caring Is Sharing – Exploiting the Value in Data for Health and Innovation

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The application of machine learning (ML) algorithms to electronic health records (EHR) data allows the achievement of data-driven insights on various clinical problems and the development of clinical decision support (CDS) systems to improve patient care. However, data governance and privacy barriers hinder the use of data from multiple sources, especially in the medical field due to the sensitivity of data. Federated learning (FL) is an attractive data privacy-preserving solution in this context by enabling the training of ML models with data from multiple sources without any data sharing, using distributed remotely hosted datasets. The Secur-e-Health project aims at developing a solution in terms of CDS tools encompassing FL predictive models and recommendation systems. This tool may be especially useful in Pediatrics due to the increasing demands on Pediatric services, and the current scarcity of ML applications in this field compared to adult care. Herein we provide a description of the technical solution proposed in this project for three specific pediatric clinical problems: childhood obesity management, pilonidal cyst post-surgical care and retinography imaging analysis.

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Future of machine learning in paediatrics

Cited by 14 publications

References 44 publications

Organoids revealed: morphological analysis of the profound next generation in-vitro model with artificial intelligence

Organoids revealed: morphological analysis of the profound next generation in-vitro model with artificial intelligence

Precision diagnostics in children

Secur-e-Health Project: Towards Federated Learning for Smart Pediatric Care

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