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In a landmark achievement for Ghana’s scientific community, a team of three researchers led by Dr. Frempong Acheampong has developed the nation’s first indigenous machine learning model for forensic age estimation. This technological breakthrough is specifically designed to assess the age of adolescents, filling a critical gap in forensic science within the West African context. By leveraging local data, the model aims to provide a more accurate alternative to the foreign standards that have historically been used in Ghana, which often fail to account for the biological nuances of African populations. The research was conducted in the Ho Municipality and involved a study group of 265 adolescents between the ages of 14 and 19. Utilizing non-invasive imaging techniques, the researchers tested various algorithms to determine which provided the most reliable results. The findings revealed that the Random Forest model achieved the highest level of accuracy, demonstrating the power of tailored machine learning solutions in solving complex biological and legal challenges. This indigenous approach ensures that age verification is grounded in the physiological realities of the local population, rather than relying on datasets derived from Western or Asian demographics. Beyond its technical success, this project holds significant implications for legal and social systems across sub-Saharan Africa. Accurate age estimation is vital for judicial proceedings, sports eligibility, and access to social services. By developing a tool that is both locally relevant and scientifically robust, Dr. Acheampong and his team have paved the way for more equitable forensic practices. The researchers anticipate that this model could eventually be scaled for use across the continent, positioning Ghana as a leader in the development of forensic technologies that serve the specific needs of African societies.