Clinical superiority of artificial intelligence-enabled jaundice monitoring follow-up combined with end-tidal carbon monoxide measurement in neonatal jaundice management.

OBJECTIVE

To evaluate the clinical superiority of artificial intelligence (AI)-enabled jaundice monitoring follow-up combined with end-tidal carbon monoxide (ETCO) measurement in the management of neonatal jaundice.

METHODS

Sixty jaundiced neonates were studied, with 25 assigned to conventional monitoring and follow-up (control group) and 35 to an AI-enabled jaundice monitoring follow-up plus ETCO assessment (research group). Evaluated endpoints comprised bilirubin concentrations (both serum and transcutaneous), prevalence of hyperbilirubinemia (HB), bilirubin encephalopathy (BE), and ABO hemolytic disease, monitoring/procedure-related adverse events, and time to symptom improvement. The assessment also covered alterations (pre- vs. post-intervention) in the Neonatal Behavioral Neurological Assessment (NBNA), Mental Development Index (MDI), and Psychomotor Development Index (PDI) scores, as well as maternal psychological well-being (Self-Rating Anxiety Scale (SAS) and Self-Rating Depression Scale (SDS)) and parental satisfaction.

RESULTS

Post-intervention, the research group exhibited greater reductions in bilirubin, transcutaneous bilirubin, SAS/SDS scores, HB/BE incidence, and overall complications than the control group, coupled with accelerated symptom relief (all P<0.05). Post-intervention assessments also confirmed markedly higher NBNA, MDI, and PDI scores, as well as enhanced parental satisfaction in the research group (all P<0.05).

CONCLUSION

AI-enabled jaundice monitoring follow-up combined with ETCO measurement offers significant clinical advantages in neonatal jaundice management, supporting its broader clinical adoption.