Talat Zehra

About Talat Zehra

As a medical doctor, I am deeply concerned about disparities in healthcare infrastructure worldwide. More than two-thirds of the global population lives in developing regions, where both neoplastic and non-neoplastic diseases are common. Yet, paradoxically, fewer medical graduates are choosing careers in pathology – particularly anatomic and surgical pathology – even as cancer cases and complex disease diagnoses continue to rise. Precision medicine demands increasingly detailed histopathology reports, along with ancillary and molecular studies, making the expertise of pathologists more critical than ever.

Digital pathology has transformed the field by enabling telepathology, knowledge sharing, education, and even primary diagnosis. Artificial intelligence and deep learning models are emerging as valuable tools to support pathologists in their work. However, most of these benefits remain concentrated in developed countries, where institutions can afford the high costs of digital and computational pathology systems. For many low-resource settings, these technologies remain out of reach.

In our own work, we sought to push forward using the resources available. By connecting simple cameras to microscopes, we digitized slides at various magnifications to capture regions of interest. These images were then applied to education, research, and knowledge-sharing efforts. We used open-source tools to stitch images into whole-slide formats and applied AI models for tasks such as mitosis identification, perineural invasion detection, parasite recognition (schistosomiasis and malaria), and Ki-67 quantification. Despite limited resources, these projects demonstrated that innovation is possible outside high-income settings.

These efforts are not a replacement for advanced digital pathology platforms, but they prove that meaningful progress can be made with creativity and determination. By leveraging basic tools, open-source platforms, and AI, resource-limited organizations can begin their journey into digital and computational pathology – advancing education, research, knowledge-sharing, and even diagnostic support.