Semi-Automated Medical Image Interpretation

This project explores the fusion of Artificial Intelligence (AI) and medical image interpretation. Our mission is to revolutionize the field of radiology by harnessing the power of cutting-edge technology to assist healthcare professionals in making faster, more accurate diagnoses.

Interpreting medical images can be complex and time-consuming, even for experienced radiologists. That's where our innovative machine learning model comes in. This project is dedicated to creating an intelligent system that complements the expertise of human specialists. By leveraging the vast amounts of data and advanced algorithms, our AI model aims to provide radiologists and other medical specialists with valuable insights, helping them make well-informed decisions about patient care.

This project holds enormous potential to revolutionize the medical field. Envision a future where doctors can depend on AI-powered support for swift and precise medical image analysis, resulting in faster diagnoses and more efficient treatments. As a result, patients will experience reduced waiting times and improved healthcare outcomes, while healthcare providers can optimize their resources, leading to enhanced overall patient care. The impact of this venture promises to reshape the landscape of radiology and set a new standard for advanced healthcare practices.

Project Tools and Services

  • Google Colab
  • Kaggle
  • Python

AI-Supported Workflows

  • Pneumonia detection in chest x-rays(Coming soon).

Project Members

Lighton Phiri
Academic Staff
University of Zambia
Ernest Obbie Zulu
Academic Staff
University Teaching Hospitals
Andrew Shawa
Postgraduate Student
University of Zambia
Malaizyo Muzumala
Postgraduate Student
University of Zambia
Peter Chibuta
Academic Staff
University Teaching Hospitals

Publications

2024
Phiri, Lighton, Ernest Obbie Zulu, Elijah Chileshe, Andrew Shawa, Wilkins Sikazwe, John Mwanza, and Brighton Mwaba. 2024. “User Centred Design And Implementation Of Useful Picture Archiving And Communication Systems For Effective Radiological Workflows In Public Health Facilities In Zambia”. In South African Institute Of Computer Scientists And Information Technologists. South Africa.
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2023
Shawa, Andrew, and Lighton Phiri. (2023) 2023. “Software Tools For Supporting Automatic Interpretation Of Medical Images”. In 2023 Pan African Conference On Science, Computing And Telecommunications (Pact 2023). Lusaka, Zambia: Zambia ICT Journal. https://ictjournal.icict.org.zm/index.php/icict/article/view/278.
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