The article discusses how artificial intelligence (AI) is being used in Africa to detect medical anomalies in baby cries. Researchers and startups are leveraging the sounds of infants' cries to screen and monitor neurological and respiratory conditions early on.

- Researchers at Northern Illinois University developed an algorithm based on automatic speech recognition to differentiate normal and abnormal cry signals, extracting meaningful health-related information from the differences in cry sounds.

- Ubenwa, a startup co-founded by Charles Onu, utilizes AI and machine learning to interpret infants' cries for medical purposes. Inspired by his cousin's health issues, Onu aims to bridge communication gaps between experts and babies, creating a non-invasive method for monitoring medical conditions through cry analysis.

- Ubenwa's software identifies signs of birth asphyxia and could potentially determine developmental milestones based on cry patterns. The startup collaborates closely with hospitals to build a diverse database of annotated infant cry sounds, essential for developing audio-based biomarkers.

- Ubenwa's cry analysis demonstrates around a 40% improvement over traditional APGAR scoring, providing a fast, cost-effective, and non-invasive alternative for assessing infants' neurological well-being.

- The startup raised $2.5 million in pre-seed funding and is focusing not only on product development but also on training medical professionals in underserved areas, enhancing their ability to provide better care through upskilling.

- Ubenwa was recognized by the World Health Organization as one of the top 30 healthcare innovators in Africa in 2019.

Overall, AI-driven cry analysis shows promise in revolutionizing infant healthcare by enabling early detection of medical conditions and enhancing communication between medical experts and infants.

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