In a world that demands meticulous attention to detail and accuracy, the speciality of hematology is certainly no exception. The accuracy of platelet (PLT) estimates from peripheral blood smears is essential for reliable reporting. Presence of PLT clumps can introduce uncertainty, compromising the reliability of numerical estimates generated by automated whole blood analyzers. To address this, an effective system should be able to detect and identify PLT clumps with a sensitivity approaching 100%.
In today’s world it is all eyes on AI with Artificial Intelligence having catapulted us into a new fast-paced reality. This tenet holds especially true within the healthcare setting, whereby this transformative force holds the golden promise of heightened diagnostic accuracy, reduced costs, and improved patient response times and treatment outcomes.
