{Reference Type}: Journal Article
{Title}: From microscope to micropixels: A rapid review of artificial intelligence for the peripheral blood film.
{Author}: Fan BE;Yong BSJ;Li R;Wang SSY;Aw MYN;Chia MF;Chen DTY;Neo YS;Occhipinti B;Ling RR;Ramanathan K;Ong YX;Lim KGE;Wong WYK;Lim SP;Latiff STBA;Shanmugam H;Wong MS;Ponnudurai K;Winkler S;
{Journal}: Blood Rev
{Volume}: 64
{Issue}: 0
{Year}: 2024 Mar 19
{Factor}: 10.626
{DOI}: 10.1016/j.blre.2023.101144
{Abstract}: 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.