Welcome
Revolutionizing Neurodevelopmental Screening Through New-Born Feeding Data
AI-powered analysis of infant sucking patterns for early risk identification.
What is neurolacto AI?
What does NeuroLacto measure?
NeuroLacto measures and analyzes a comprehensive set of key parameters related to infant sucking, including:
• Sucking strength
• Sucking intensity
• Number of sucks
• Sucking rhythm and pattern
• Sucking effectiveness
• Latch quality
• Neuro-muscular coordination during sucking
• And more
These parameters not only reflect the quality and effectiveness of infant feeding but also provide valuable insights into the functional status of the infant’s neuro-motor and oral-motor systems.
Through AI-driven data analysis, NeuroLacto enables the early identification of abnormal patterns and potential feeding or neuro-motor dysfunctions, supporting timely intervention and improved developmental outcomes.
An Innovative AI-based Framework
NeuroLacto is an innovative AI-based framework designed for the objective and scientific assessment of infant sucking patterns—both nutritive and non-nutritive—during breastfeeding.
Infant sucking is not merely a feeding process; it is one of the earliest and most critical neuro-motor activities, reflecting the coordinated function of the brain, cranial nerves, and oral muscles.
By precisely analyzing sucking parameters and applying advanced artificial intelligence algorithms, NeuroLacto not only evaluates the effectiveness of infant feeding but also provides valuable insights into the functional integrity of the oral neuro-motor system.
The goal of NeuroLacto is to transform breastfeeding assessment from a subjective, observation-based process into a data-driven, measurable evaluation directly linked to an infant’s nutritional status and neurodevelopment.
A growing body of scientific evidence has also demonstrated a strong association between early sucking patterns and later cognitive, speech, and motor development.
About NeuroLacto
Current child-health follow-up systems provide essential support through vaccination visits, growth monitoring, and developmental milestone assessment. However, early physiological signs of feeding difficulty and neurodevelopmental vulnerability may remain unnoticed during the neonatal period.
In many clinical settings, infant sucking assessment is still mainly observational. This creates an opportunity to add an objective neonatal feeding assessment layer before discharge, supporting earlier identification of infants who may benefit from monitoring or feeding support.
Infant sucking is more than a feeding behavior. It is one of the earliest coordinated neuro-motor activities in human development and reflects the integrated function of the brainstem, cranial nerves, oral muscles, respiration, and sensory feedback.
Safe and effective feeding requires precise coordination between sucking, swallowing, and breathing. This coordination supports milk transfer, airway protection, feeding efficiency, and early oral-motor development.
Because sucking depends on complex neural and motor networks, early sucking patterns may provide valuable insight into feeding readiness and the functional status of the infant’s oral neuro-motor system.
Objective assessment of sucking behavior can help clinicians move beyond subjective observation and support earlier identification of infants who may benefit from closer feeding monitoring, breastfeeding support, or follow-up evaluation.
NeuroLacto.AI uses this principle to transform early feeding signals into structured clinical information, supporting more informed decision-making during the neonatal period.
NeuroLacto.AI uses artificial intelligence algorithms to analyze infant sucking patterns and classify feeding performance into clinically meaningful categories.
In Version 1, the platform supports structured classification of sucking performance into:
Normal | Borderline | At Risk | High Risk
These classifications are intended to support clinical assessment, monitoring, and referral decisions. NeuroLacto.AI does not diagnose disease; instead, it provides objective feeding data and structured reports to assist healthcare professionals in early identification and follow-up planning.
NeuroLacto.AI supports healthcare professionals by providing:
- Objective and measurable neonatal feeding data
- Structured sucking-pattern reports
- Early identification of feeding-risk patterns
- Support for breastfeeding guidance and family counselling
- More consistent clinical documentation
- Data to support follow-up prioritization
- A foundation for future predictive child-health screening models
- Standardized datasets for neonatal feeding research
What Benefits Does NeuroLacto Offer to Mothers?
- Screening the newborn on the first day of life for sucking adequacy (the earliest possible assessment)
- Ensuring the infant receives sufficient nutrition
- Reducing maternal anxiety and worry
- Providing objective and scientific feedback during breastfeeding
- Assisting in the early detection of breastfeeding difficulties
- Preventing complications associated with ineffective breastfeeding
- Increasing maternal confidence in the breastfeeding process
- Raising awareness of the infant’s sucking performance and oral-motor development
This information helps mothers support their infant’s nutritional health and sucking performance with greater confidence and awareness.