Differential diagnosis of hereditary anemias from a fraction of blood drop by digital holography and hierarchical machine learning

• DOI: 10.1016/j.bios.2021.113945
• Publication Date: 2022-01-04T16:53:10Z
• Authors: Pasquale Memmolo; Genny Aprea; Vittorio Bianco; Roberta Russo; Immacolata Andolfo; Martina Mugnano; Francesco Merola; Lisa Miccio; Achille Iolascon; Pietro Ferraro
• Keywords: Blood cells; 0303 health sciences; Hereditary anemias; Holography; Digital holography; Point-of care diagnostics; Blood cells; Digital holography; Hereditary anemias; Machine learning; Point-of care diagnostics; Artificial Intelligence; Diagnosis, Differential; Female; Humans; Machine Learning; Pregnancy; Anemia; Biosensing Techniques; Holography; Anemia; Biosensing Techniques; Digital holography; Hereditary anemias; Machine learning; Point-of care diagnostics; 3. Good health; Diagnosis, Differential; Machine Learning; 03 medical and health sciences; Artificial Intelligence; Pregnancy; Machine learning; Diagnosis; Differential; Humans; Female

Anemia affects about the 25% of the global population and can provoke severe diseases, ranging from weakness and dizziness to pregnancy problems, arrhythmias and hearth failures. About 10% of the patients are affected by rare anemias of which 80% are hereditary. Early differential diagnosis of anemia enables prescribing patients a proper treatment and diet, which is effective to mitigate the associated symptoms. Nevertheless, the differential diagnosis of these conditions is often difficult due to shared and overlapping phenotypes. Indeed, the complete blood count and unaided peripheral blood smear observation cannot always provide a reliable differential diagnosis, so that biomedical assays and genetic tests are needed. These procedures are not error-free, require skilled personnel, and severely impact the financial resources of national health systems. Here we show a differential screening system for hereditary anemias that relies on holographic imaging and artificial intelligence. Label-free holographic imaging is aided by a hierarchical machine learning decider that works even in the presence of a very limited dataset but is enough accurate for discerning between different anemia classes with minimal morphological dissimilarities. It is worth to notice that only a few tens of cells from each patient are sufficient to obtain a correct diagnosis, with the advantage of significantly limiting the volume of blood drawn. This work paves the way to a wider use of home screening systems for point of care blood testing and telemedicine with lab-on-chip platforms.