The current diagnosis of Congenital Heart Disease (CHD) in neonates relies on echocardiography. Its limited availability requires alternative screening procedures to prioritise newborns awaiting ultrasound. The routine screening for CHD is performed using a multidimensional clinical examination including (but not limited to) auscultation and pulse oximetry. While auscultation might be subjective with some heart abnormalities not always audible it increases the ability to detect heart defects. This work aims at developing an objective clinical decision support tool based on machine learning (ML) to facilitate differentiation of sounds with signatures of Patent Ductus Arteriosus (PDA)/CHDs, in clinical settings. The heart sounds are pre-processed and segmented, followed by feature extraction. The features are fed into a boosted decision tree classifier to estimate the probability of PDA or CHDs. Several mechanisms to combine information from different auscultation points, as well as consecutive sound cycles, are presented. The system is evaluated on a large clinical dataset of heart sounds from 265 term and late-preterm newborns recorded within the first six days of life. The developed system reaches an area under the curve (AUC) of 78% at detecting CHD and 77% at detecting PDA. The obtained results for PDA detection compare favourably with the level of accuracy achieved by an experienced neonatologist when assessed on the same cohort.

The study proposes a novel method to empower healthcare professionals to interact and leverage AI decision support in an intuitive manner using auditory senses. The method’s suitability is assessed through acoustic detection of the presence of neonatal seizures in electroencephalography (EEG). Neurophysiologists use EEG recordings to identify seizures visually. However, neurophysiological expertise is expensive and not available 24/7, even in tertiary hospitals. Other neonatal and pediatric medical professionals (nurses, doctors, etc.) can make erroneous interpretations of highly complex EEG signals. While artificial intelligence (AI) has been widely used to provide objective decision support for EEG analysis, AI decisions are not always explainable. This work developed a solution to combine AI algorithms with a human-centric intuitive EEG interpretation method. Specifically, EEG is converted to sound using an AI-driven attention mechanism. The perceptual characteristics of seizure events can be heard using this method, and an hour of EEG can be analysed in five seconds. A survey that has been conducted among targeted end-users on a publicly available dataset has demonstrated that not only does it drastically reduce the burden of reviewing the EEG data, but also the obtained accuracy is on par with experienced neurophysiologists trained to interpret neonatal EEG. It is also shown that the proposed communion of a medical professional and AI outperforms AI alone by empowering the human with little or no experience to leverage AI attention mechanisms to enhance the perceptual characteristics of seizure events.

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The study proposes a novel method to enable healthcare professionals interact and leverage AI decision support in an intuitive manner using auditory senses. The method suitability is assessed through acoustic detection of the presence of neonatal seizures in electroencephalography (EEG). Neurophysiologists use EEG recordings to visually identify seizures. However,neurophysiological expertise is expensive and not available 24/7, even in tertiary hospitals. Nurses, neonatologists, paediatricians can make frequent misdiagnosis when interpreting complex EEG signals. While artificialintelligence (AI) has been widely used to provide objective decision support for EEG analysis, AI decisionsare not always explainable. A solution is developed in this study to combine AI algorithms with a human-centric intuitive EEGinterpretation method. Specifically, EEG is converted to sound using an AI-driven attention mechanism. Using this method perceptual characteristics of seizure events can be heard and an hour of EEG can beanalysed in five seconds. A survey that has been conducted among targeted end-users on a publicly available dataset has demonstratedthat not only does it drastically reduce the burden of reviewing the EEG data, but also the obtained accuracy is on par with experiencedneurophysiologists trained to interpret neonatal EEG. It is also shown that the proposed communion of a medical professional and AIoutperforms AI alone through empowering the human with little or no experience to leverage AI attention mechanisms to enhance theperceptual characteristics of seizure events.