The global burden of schistosomiasis and soil-transmitted helminth (STH) infections is substantial, with millions at risk, particularly in sub-Saharan Africa. Traditional diagnostic methods using optical microscopy are labour intensive, operator dependent, and often inaccessible in remote areas. This thesis focuses on the development and validation of an AI-based digital microscope, the Schistoscope, designed to enhance the diagnosis of these infections, particularly in resource-limited settings. The aim is to bridge existing diagnostic gaps by developing an automated system that reduces reliance on skilled personnel, enhances diagnostic accuracy, and improves accessibility.

The research integrates innovations in hardware design, digital imaging, and artificial intelligence (AI) to create an affordable, efficient, and accurate diagnostic tool that addresses the limitations of traditional microscopy. The primary objectives include designing and developing cost-effective digital microscope prototypes, integrating AI algorithms for automated detection and classification of parasite eggs, and validating the diagnostic performance of the system through field studies. The development process began with two low-cost digital microscope prototypes: the Raspberry Pi-based Schistoscope and the smartphone-based Schistoscope. Both designs focused on affordability, portability, and ease of use, with key innovations such as the integration of 3D-printed components and locally sourced materials to ensure sustainability and ease of maintenance in endemic regions.

Subsequent iterations led to the development of the Schistoscope 5.0, which featured significant improvements in imaging quality, automation, and user interface. The device incorporates a whole slide imaging system with an advanced autofocusing algorithm, enhancing image clarity and diagnostic accuracy. A cornerstone of the thesis is the integration of AI for automated detection and quantification of Schistosoma haematobium and intestinal helminth eggs. The diagnostic framework employs deep learning models, particularly convolutional neural networks (CNNs), to perform semantic segmentation and object detection. Key components include the DeepLabV3 with a MobileNetV3 backbone, used for semantic segmentation of Schistosoma haematobium eggs, effectively distinguishing eggs from background artifacts, and the EfficientDet model, applied for the detection of intestinal helminth eggs, including Ascaris lumbricoides, Trichuris trichiura, hookworm, and Schistosoma mansoni.

The models were trained on robust datasets collected from field samples, with extensive image annotation to ensure accuracy. The diagnostic system demonstrated high sensitivity and specificity, meeting the World Health Organisation's target product profiles for schistosomiasis and STH control programs. Comprehensive field validation studies were conducted in Nigeria and Gabon to assess the real-world performance of the Schistoscope. These studies compared the device's diagnostic accuracy with conventional microscopy and composite reference standards incorporating real-time PCR and UCP-LF CAA. In Nigeria, the study also focused on the usability and acceptability of the Schistoscope among healthcare workers, demonstrating high acceptance rate of both the semi- and fully automated modes. In Gabon, the diagnostic performance was assessed on fresh and banked urine samples, with the Schistoscope demonstrating comparable accuracy to traditional microscopy, alongside the added benefits of automation and reduced diagnostic time.

Key findings highlight improved diagnostic accuracy, with the Schistoscope achieving high precision and sensitivity in detecting schistosomiasis and STH infections. The integration of AI reduced the need for skilled personnel, thereby enhancing automation and efficiency. The device's cost-effectiveness is underscored by the use of affordable materials and open-source hardware/software, making it accessible for low-resource settings. Field readiness was confirmed through validation under real-world conditions, attesting to the device's robustness and reliability.

Despite these advancements, the research identified several challenges and limitations. Some images were affected by sub-optimal autofocusing, impacting diagnostic accuracy in certain cases. The AI models require further training on more diverse datasets to improve generalisation across different environmental conditions. Additionally, operational challenges such as mechanical issues in the autofocusing mechanism were noted, necessitating further hardware refinements.

Building on these successes and lessons learned, future research will focus on hardware and software refinement to improve mechanical stability and AI model robustness. The Schistoscope will be adapted for the diagnosis of other parasitic diseases, with enhanced field validation through extensive trials in diverse settings. Sustainable deployment strategies will explore local manufacturing options to reduce costs and improve accessibility. Furthermore, the development of cloud-based data storage and analysis systems will support large-scale public health initiatives.

In conclusion, this thesis demonstrates that the integration of AI with digital microscopy can revolutionise the diagnosis of schistosomiasis and STH infections. The Schistoscope not only matches traditional microscopy in diagnostic accuracy but also offers significant advantages in terms of automation, cost-effectiveness, and field applicability. By addressing key challenges in parasitic disease diagnostics, this research contributes to global efforts in controlling and eliminating neglected tropical diseases (NTD), with the potential to improve health outcomes in some of the world's most vulnerable populations. Therefore, accelerating the achievement of the elimination targets as enshrined in the WHO’s NTD Road Map, 2021-2030 in resource-limited settings.