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https://www.selleckchem.com/products/ms-275.html Digital microscopy systems are beginning to replace traditional light microscopes for morphologic analysis of blood films, but these are geographically restricted to individual computers and technically limited by manufacturer's constraints. We explored the use of a scanner-agnostic web-based artificial intelligence (AI) system to assess the accuracy of white blood cell (WBC) differentials and blast identification in haematological malignancies. Digitized images of 20 normal and 124 abnormal peripheral blood films were uploaded to the web-based platform (Techcyte©) and WBC differentials performed using the online AI software. Digital images were viewed for accuracy and manual cell reassignment was performed where necessary. Results were correlated to the 'gold standard' of manual microscopy for each WBC class, and sensitivity and specificity of blast identification were calculated. The AI digital differential was very strongly correlated to microscopy (r>.8) for most normal cell types and did not require any manual reassignment. The AI digital differential was less reliable for abnormal blood films (r=.50-.87), but could be greatly improved by manual assessment of digital images for most cell types (r>.95) with the exception of immature granulocytes (r=.62). For blast identification, initial AI digital differentials showed 96% sensitivity and 25% specificity, which was improved to 99% and 84%, respectively, after manual digital review. The Techcyte platform allowed remote viewing and manual analysis of digitized slides that was comparable to microscopy. The AI software produced adequate WBC differentials for normal films and had high sensitivity for blast identification in malignant films. The Techcyte platform allowed remote viewing and manual analysis of digitized slides that was comparable to microscopy. The AI software produced adequate WBC differentials for normal films and had high sensitivity for blast identifi

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