A machine-learning-based algorithm for bone marrow cell differential counting.

Background: Differential counting (DC) of different cell types in bone marrow (BM) aspiration smears is crucial for diagnosing hematological diseases. However, a clinically applicable method for automatic DC has yet to be developed.

Objective: This study developed and validated an artificial intelligence (AI)-based algorithm for identifying and classifying nucleated cells in BM smears.

Bone Marrow Histology in Hemophagocytic Lymphohistiocytosis.

Context.—: Bone marrow (BM) samples are obtained through aspiration and trephine biopsy. Hemophagocytic lymphohistiocytosis (HLH) has been largely studied in BM aspirate smears.

Urine sediment examination: a comparison of automated urinalysis systems and manual microscopy.

Background: Several automated instruments examining urine sediment have been introduced. We compared the performance of Sysmex UF-100 and Iris iQ200 with manual microscopy in urine sediment testing.

Methods: Four hundred and thirty-six urine samples were collected.