Neural network reveals platelet age from fluorescence microscopy images.

Area of Science:

• Hematology
• Artificial Intelligence
• Medical Imaging

Background:

• Platelets are crucial for hemostasis and thrombus formation.
• Platelet age correlates with clinical outcomes, but no reliable in vitro tests exist to predict age or assess transfusion product fitness.
• Current methods lack the ability to determine platelet age in vivo or during storage.

Purpose of the Study:

• To develop a convolutional neural network (CNN) model capable of predicting the chronological age of platelets from confocal microscopic images.
• To validate the model's accuracy in predicting platelet age both in vitro during storage and in vivo.

Main Methods:

• A CNN model was trained using confocal microscopic images of platelets stored in platelet-rich plasma (up to 8 hours) and routine platelet concentrates (up to 10 days).
• The model's performance was evaluated by predicting the chronological age of stored platelets.
• The model was further tested in vivo on a cohort of acute myeloid leukemia patients undergoing chemotherapy-induced thrombocytopenia.

Main Results:

• The CNN model achieved >97% accuracy in predicting the chronological age of stored platelets.
• The model successfully distinguished between younger and older platelets in vivo in patients during chemotherapy treatment.
• This demonstrates the model's capability for both in vitro and in vivo platelet age determination.

Conclusions:

• The developed AI model can accurately predict platelet chronological age from microscopic images.
• This technology holds significant potential for improving clinical transfusion medicine by assessing platelet viability.
• The model may also aid in the diagnosis and management of platelet disorders.