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AI for Good-Innovate for Impact



                      Use case – 46: Improving early detection of neonatal asphyxia with

                      smartphone-based AI technologies













                      Country: Bangladesh
                      Organization: Bangladesh Open University


                      Contact person: Samrat Kumar Dey (samrat.sst@ bou .ac .bd, +8801823267937)

                      46�1� Use case summary table


                       Domain                         healthcare
                       The problem to be addressed    Development of more robust and reliable AI algorithms
                                                      for neonatal asphyxia detection on smartphones

                       Key aspects of the solution    Voice sample analysis to identify asphyxia
                       Technology keywords            Healthcare, asphyxia, voice, machine learning, deep
                                                      learning
                       Data availability              Private right now.

                       Metadata (type of data)        Audio sample from Mexico.
                                                      1049 audio samples.
                                                      1s samples.
                       Model Training and fine-tuning   At the moment, using ML (regression models, ensemble
                                                      models)
                                                      May use DL in future (RNN-LSTM type models)
                       Testbeds or pilot deployments   In 4 different hospitals from 4 different divisions.



                      46�2  Use-case description


                      46�2�1  Description

                      This use case explores leveraging smartphones and artificial intelligence (AI) for early detection
                      of neonatal asphyxia, a critical condition where newborns experience oxygen deprivation
                      during birth. Timely diagnosis is paramount for effective interventions and improved outcomes.
                      However, current methods in resource-constrained settings often rely on subjective assessments
                      or expensive equipment.

                      Intended Use: This AI-powered solution targets healthcare professionals, particularly midwives
                      and frontline workers, in low- and middle-income countries, where access to specialized





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