AI-Enhanced Detection of Cellular Aggregate Biomarkers For Point-of-Care Using Digital Holographic Microscopy

In the acute clinical setting, stratification of patients presenting with fever is a challenge that often results in high diagnostic costs and inpatient monitoring, leading to substantial resource utilization. Cellular aggregation patterns promise to reveal information about the underlying immunology mechanisms of fever and provide new biomarkers to facilitate triage and diagnosis. However, clinical utilization requires rapid imaging and measurement of these patterns, currently these tools are unavailable. This study introduces the YOLO-Graph model for the fast analysis of the aggregates from high-throughput cellular imaging data, to enhance early diagnostic capabilities in clinical practice. The core of our methodology is built on integrating digital holographic microscopy with the YOLOv8x-p2 neural network and graph model, enabling precise morphological detection and quantitative assessment of cellular aggregates. Our detector achieved 91.14% accuracy for aggregate identification, marking a 13.92% improvement over the benchmark pipeline.Using this rapid analysis of clinical samples, our YOLOGraph model identified specific cellular aggregate patterns during immune reactions. We showed that the mean value of platelet-platelet and leukocyte-platelet aggregates from healthy controls is lower than that of fever and pneumonia patients. Identifying specific patterns associated with clinically important diagnoses and outcomes will allow the development of biomarkers for early triage and diagnosis.