In sub-Saharan Africa, acute-onset severe malaria anaemia (SMA) is a critical challenge, particularly affecting children under five. The acute drop in haematocrit in SMA is thought to be driven by an increased phagocytotic pathological process in the spleen, leading to the presence of distinct red blood cells (RBCs) with altered morphological characteristics. We hypothesized that these RBCs could be detected systematically and at scale in peripheral blood films (PBFs) by harnessing the capabilities of deep learning models. Assessment of PBFs by a microscopist does not scale for this task and is subject to variability. Here we introduce a deep learning model, leveraging a weakly supervised Multiple Instance Learning framework, to Identify SMA (MILISMA) through the presence of morphologically changed RBCs. MILISMA achieved a classification accuracy of 83% (receiver operating characteristic area under the curve [AUC] of 87%; precision-recall AUC of 76%). More importantly, MILISMA\'s capabilities extend to identifying statistically significant morphological distinctions (p < 0.01) in RBCs descriptors. Our findings are enriched by visual analyses, which underscore the unique morphological features of SMA-affected RBCs when compared to non-SMA cells. This model aided detection and characterization of RBC alterations could enhance the understanding of SMA\'s pathology and refine SMA diagnostic and prognostic evaluation processes at scale.

Artificial intelligence (AI) and its application in classification of blood cells in the peripheral blood film is an evolving field in haematology. We performed a rapid review of the literature on AI and peripheral blood films, evaluating the condition studied, image datasets, machine learning models, training set size, testing set size and accuracy. A total of 283 studies were identified, encompassing 6 broad domains: malaria (n = 95), leukemia (n = 81), leukocytes (n = 72), mixed (n = 25), erythrocytes (n = 15) or Myelodysplastic syndrome (MDS) (n = 1). These publications have demonstrated high self-reported mean accuracy rates across various studies (95.5% for malaria, 96.0% for leukemia, 94.4% for leukocytes, 95.2% for mixed studies and 91.2% for erythrocytes), with an overall mean accuracy of 95.1%. Despite the high accuracy, the challenges toward real world translational usage of these AI trained models include the need for well-validated multicentre data, data standardisation, and studies on less common cell types and non-malarial blood-borne parasites.

BACKGROUND: The Mindray CAL 8000 is a cellular analysis line that consists of the BC-6800, an automated hematology analyzer, and the SC-120, an automated slidemaker/stainer. We evaluated the performances of the BC-6800 and the SC-120.
METHODS: Four hundred and eight normal and abnormal samples were analyzed. The performance of the BC-6800 and Sysmex XE-2100 were compared, and blood films by the SC-120 and manual method were compared according to the CLSI guideline H26-A2 and H20-A2.
RESULTS: Most parameters measured by the BC-6800 matched well with the XE-2100 and manual differential. The flag efficiency of the BC-6800 for blasts (95.3%) and atypical lymphocytes (92.6%) were higher while immature granulocytes (89.7%) and NRBCs (94.1%) were lower than that of the XE-2100. Additionally, the BC-6800 detected four of five samples infected with plasmodium parasites. The SC-120 showed no carry-over and expected repeatability. There was good agreement on the five-part differential including abnormal cells between blood films by the SC-120 and manually prepared blood films. The shape of the RBC was also comparable between blood films.
CONCLUSIONS: The CAL-8000 analysis line is beneficial for precise, fast hematology work, and even more useful in malaria endemic areas.