Utilising Cot‐Side Cameras in Neonatal Intensive Care Unit for Deep Learning–Assisted General Movement Assessment

Aim Neonatal units are increasingly utilising cot‐side cameras to connect parents with their infants. Combined with deep learning, video obtained through cot‐side cameras could assist clinicians in conducting seamless general movement assessment (GMA) of the writhing age. Method A literature search was conducted using PubMed, Embase and SCOPUS with the following keywords: cot‐side cameras, deep learning, artificial intelligence, general movement assessment and writhing age. Results Methods for acquiring and classifying human movement are categorised into contact, non‐contact and hybrid approaches. Contact modalities typically include wearable sensors placed on the body to represent human posture, while hybrid modalities combine wearable sensors or markers with non‐contact sensors. Non‐contact approaches include radar‐based and vision‐based methods, which are the most common and accessible for motion capture, employing standard or specialised cameras to capture video data. Cot‐side cameras used in neonatal clinics are primarily standard red‐green‐blue (RGB) devices and are the leading candidates for automated GMA. Advances in deep learning can enhance motion assessment with video data through appearance‐ and pose‐based methods, supporting computer‐aided GMA. Conclusion Advances in deep learning can enhance the motion assessment of RGB video data, offering a scalable and non‐invasive solution for computer‐aided GMA that could reshape early neurodevelopmental screening.