Developers proposing new machine learning for health (ML4H) tools often pledge to match or even surpass the performance of existing tools, yet the reality is usually more complicated. Reliable deployment of ML4H to the real world is challenging as examples from diabetic retinopathy or Covid-19 screening show. We envision an integrated framework of algorithm auditing and quality control that provides a path towards the effective and reliable application of ML systems in healthcare. In this editorial, we give a summary of ongoing work towards that vision and announce a call for participation to the special issue  Machine Learning for Health: Algorithm Auditing & Quality Control in this journal to advance the practice of ML4H auditing.

Effective determination of malaria parasitemia is paramount in aiding clinicians to accurately estimate the severity of malaria and guide the response for quality treatment. Microscopy by thick smear blood films is the conventional method for malaria parasitemia determination. Despite its edge over other existing methods of malaria parasitemia determination, it has been critiqued for being laborious, time consuming and equally requires expert knowledge for an efficient manual quantification of the parasitemia. This pauses a big challenge to most low developing countries as they are not only highly endemic but equally low resourced in terms of technical personnel in medical laboratories This study presents an end-to-end deep learning approach to automate the localization and count of P.falciparum parasites and White Blood Cells (WBCs) for effective parasitemia determination. The method involved building computer vision models on a dataset of annotated thick blood smear images. These computer vision models were built based on pre-trained deep learning models including Faster Regional Convolutional Neural Network (Faster R-CNN) and Single Shot Multibox Detector (SSD) models that help process the obtained digital images. To improve model performance due to a limited dataset, data augmentation was applied. Results from the evaluation of our approach showed that it reliably detected and returned a count of parasites and WBCs with good precision and recall. A strong correlation was observed between our model-generated counts and the manual counts done by microscopy experts (posting a spear man correlation of ρ = 0.998 for parasites and ρ = 0.987 for WBCs). Additionally, our proposed SSD model was quantized and deployed on a mobile smartphone-based inference app to detect malaria parasites and WBCs in situ. Our proposed method can be applied to support malaria diagnostics in settings with few trained Microscopy Experts yet constrained with large volume of patients to diagnose.

One of the deadly endemic diseases in sub-Saharan Africa is malaria. Its prevalence is promoted by lack of sufficient expertise to carry out accurate and timely diagnosis using the standard microscopy method. Where lab technicians are available, the results are usually subjective due to variations in expert judgement. To address this challenge, prompt interventions to improve disease control are needed. The emerging technologies of machine learning that can learn complex image patterns have accelerated research in medical image analysis. In this study, on a dataset of thick blood smear images, we evaluate and compare performance of three pre-trained deep learning architectures namely; faster regional convolutional neural network (faster R-CNN), single-shot multi-box detector (SSD) and RetinaNet through a Tensorflow object detection API. Data augmentation method was applied to optimise performance of the meta architectures. The possibility for mobile phone detector deployment was also investigated. The results revealed that faster R-CNN was the best trained model with a mean average precision of over 0.94 and SSD, was the best model for mobile deployment. We therefore deduce that faster R-CNN is best suited for obtaining high rates of accuracy in malaria detection while SDD is best suited for mobile deployment.

No abstract

No abstract