The Ugandan sickle Pan-African research consortium registry: design, development, and lessons.

BACKGROUND: Sub-Saharan Africa bears the highest burden of sickle cell disease (SCD) globally with Nigeria, Democratic Republic of Congo, Tanzania, Uganda being the most affected countries. Uganda reports approximately 20,000 SCD births annually, constituting 6.67% of reported global SCD births. Despite this, there is a paucity of comprehensive data on SCD from the African continent. SCD registries offer a promising avenue for conducting prospective studies, elucidating disease severity patterns, and evaluating the intricate interplay of social, environmental, and genetic factors. This paper describes the establishment of the Sickle Pan Africa Research Consortium (SPARCo) Uganda registry, encompassing its design, development, data collection, and key insights learned, aligning with collaborative efforts in Nigeria, Tanzania, and Ghana SPARCo registries. METHODS: The registry was created using pre-existing case report forms harmonized from the SPARCo data dictionary and ontology to fit Uganda clinical needs. The case report forms were developed with SCD data elements of interest including demographics, consent, baseline, clinical, laboratory and others. That data was then parsed into a customized REDCap database, configured to suit the optimized ontologies and support retrieval aggregations and analyses. Patients were enrolled from one national referral and three regional referral hospitals in Uganda. RESULTS: A nationwide electronic patient-consented registry for SCD was established from four regional hospitals. A total of 5,655 patients were enrolled from Mulago National Referral Hospital (58%), Jinja Regional Referral (14.4%), Mbale Regional Referral (16.9%), and Lira Regional Referral (10.7%) hospitals between June 2022 and October 2023. CONCLUSION: Uganda has been able to develop a SCD registry consistent with data from Tanzania, Nigeria and Ghana. Our findings demonstrate that it's feasible to develop longitudinal SCD registries in sub-Saharan Africa. These registries will be crucial for facilitating a range of studies, including the analysis of SCD clinical phenotypes and patient outcomes, newborn screening, and evaluation of hydroxyurea use, among others. This initiative underscores the potential for developing comprehensive disease registries in resource-limited settings, fostering collaborative, data-driven research efforts aimed at addressing the multifaceted challenges of SCD in Africa.

Using machine learning for image-based analysis of sweetpotato root sensory attributes.

The sweetpotato breeding process involves assessing different phenotypic traits, such as the sensory attributes, to decide which varieties to progress to the next stage during the breeding cycle. Sensory attributes like appearance, taste, colour and mealiness are important for consumer acceptability and adoption of new varieties. Therefore, measuring these sensory attributes is critical to inform the selection of varieties during breeding. Current methods using a trained human panel enable screening of different sweetpotato sensory attributes. Despite this, such methods are costly and time-consuming, leading to low throughput, which remains the biggest challenge for breeders. In this paper, we describe an approach to apply machine learning techniques with image-based analysis to predict flesh-colour and mealiness sweetpotato sensory attributes. The developed models can be used as high-throughput methods to augment existing approaches for the evaluation of flesh-colour and mealiness for different sweetpotato varieties. The work involved capturing images of boiled sweetpotato cross-sections using the DigiEye imaging system, data pre-processing for background elimination and feature extraction to develop machine learning models to predict the flesh-colour and mealiness sensory attributes of different sweetpotato varieties. For flesh-colour the trained Linear Regression and Random Forest Regression models attained R2 values of 0.92 and 0.87, respectively, against the ground truth values given by a human sensory panel. In contrast, the Random Forest Regressor and Gradient Boosting model attained R2 values of 0.85 and 0.80, respectively, for the prediction of mealiness. The performance of the models matched the desirable R2 threshold of 0.80 for acceptable comparability to the human sensory panel showing that this approach can be used for the prediction of these attributes with high accuracy. The machine learning models were deployed and tested by the sweetpotato breeding team at the International Potato Center in Uganda. This solution can automate and increase throughput for analysing flesh-colour and mealiness sweetpotato sensory attributes. Using machine learning tools for analysis can inform and quicken the selection of promising varieties that can be progressed for participatory evaluation during breeding cycles and potentially lead to increased chances of adoption of the varieties by consumers.