Application and potential of artificial intelligence in neonatal medicine

Kwok, T'ng Chang; Henry, Caroline; Saffaran, Sina; Meeus, M.T.H.; Bates, Declan G.; Laere, David Van; Boylan, Geraldine B.; Boardman, James P.; Sharkey, Don

doi:10.1016/j.siny.2022.101346

Cited by 27 publications

(10 citation statements)

References 48 publications

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“…Another capability of AI in decision making for disease management was "diagnosis", which refers to identifying the conditions that explain a person's symptoms and signs, which are normally obtained from the patient's history and physical examination. For example, we found that AI has considerable ability in diagnosing myocardial infarction (MI), diagnosing periapical lesions, diagnosing thyroid nodules, diagnosis and staging of prostate cancer (PCA), diagnosis of oesophageal cancer, diagnosis of outcomes for heart failure (HF), endodontic diagnosis and therapy, and neurological diagnostic support and novel image recognition technologies (32,46,(55)(56)(57)(58)(59). Thus, AI can serve as a reliable assistance for clinicians in disease diagnosis.…”

Section: Discussionmentioning

confidence: 99%

Artificial Intelligence Applications in Decision Making for Disease Management

Abdekhoda

Ranjbaran

2023

Preprint

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Background: Artificial intelligence (AI) has several potential applications in medicine, creating opportunities for reliable and evidence based decision making in disease management. Thus, the practical aspects of AI in decision-making should be identified. This study was conducted to identify AI applications in decision making for disease management. Method: This study was a systematic review using the PRISMA-ScR checklist. Data collection was carried out by searching the related keywords in WOS and Scopus in May 2023. Results: Regarding the AI applications in decision making for disease management, we found 80 sub-themes which were categorized into six themes, i.e. 1) Processing and managing data, 2) Characterization and analysis, 3) Prediction and risk stratification, 4) Screening, 5) Prognosis, and 6) Diagnosis. Conclusion: AI has considerable capability in disease treatment and would be an integral part of medicine in the future. This study clearly identified six main themes that addressed AI capability in decision making for disease management. The use of AI can help in making medical decisions with more trust and confidence and thus make medical interventions more accurate and effective.

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Section: Discussionmentioning

confidence: 99%

Artificial Intelligence Applications in Decision Making for Disease Management

Abdekhoda

Ranjbaran

2023

Preprint

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“…Careful consideration of the role of AI in neonatal care is needed but the huge potential to advance and support treatments offers significant opportunities. Exploration of this field is beyond the scope of this review and readers are directed to a recent review of this topic [93].…”

Section: Artificial Intelligence (Ai)mentioning

confidence: 99%

The Critical Role of Technologies in Neonatal Care

Taha,

Simpson,

Sharkey

2023

Preprint

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“…Existing studies primarily focus on pain cries, which exhibit greater variations in F0 ( Bellieni et al, 2004 ; Zamzmi et al, 2018 ). Additionally, several recent studies focused on the development of AI tools in neonatal medicine highlighting its potential as a powerful tool to support clinical decision making, personalized care, precise prognostics, and enhance patient safety ( Kwok et al, 2022 ).…”

Section: Introductionmentioning

confidence: 99%

How can cry acoustics associate newborns’ distress levels with neurophysiological and behavioral signals?

Laguna,

Pusil,

Acero-Pousa

et al. 2023

Front. Neurosci.

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IntroductionEven though infant crying is a common phenomenon in humans’ early life, it is still a challenge for researchers to properly understand it as a reflection of complex neurophysiological functions. Our study aims to determine the association between neonatal cry acoustics with neurophysiological signals and behavioral features according to different cry distress levels of newborns.MethodsMultimodal data from 25 healthy term newborns were collected simultaneously recording infant cry vocalizations, electroencephalography (EEG), near-infrared spectroscopy (NIRS) and videos of facial expressions and body movements. Statistical analysis was conducted on this dataset to identify correlations among variables during three different infant conditions (i.e., resting, cry, and distress). A Deep Learning (DL) algorithm was used to objectively and automatically evaluate the level of cry distress in infants.ResultsWe found correlations between most of the features extracted from the signals depending on the infant’s arousal state, among them: fundamental frequency (F0), brain activity (delta, theta, and alpha frequency bands), cerebral and body oxygenation, heart rate, facial tension, and body rigidity. Additionally, these associations reinforce that what is occurring at an acoustic level can be characterized by behavioral and neurophysiological patterns. Finally, the DL audio model developed was able to classify the different levels of distress achieving 93% accuracy.ConclusionOur findings strengthen the potential of crying as a biomarker evidencing the physical, emotional and health status of the infant becoming a crucial tool for caregivers and clinicians.

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Application and potential of artificial intelligence in neonatal medicine

Cited by 27 publications

References 48 publications

Artificial Intelligence Applications in Decision Making for Disease Management

Artificial Intelligence Applications in Decision Making for Disease Management

The Critical Role of Technologies in Neonatal Care

How can cry acoustics associate newborns’ distress levels with neurophysiological and behavioral signals?

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