Descrizione del progetto
Una migliore interpretazione dei vetrini digitali grazie all’intelligenza artificiale
Le neoplasie ematologiche maligne, come la leucemia e il linfoma, colpiscono ogni anno milioni di adulti e bambini, spesso con conseguenze letali. La diagnosi si basa su anomalie cellulari, e i citologi sono specializzati proprio nell’individuare le malattie dalla composizione delle singole cellule di sangue, midollo osseo e tessuti linfoidi. Dopo 150 anni di ricerca sulle malattie del sangue, i medici si affidano ancora ai loro occhi per l’interpretazione dei vetrini istopatologici. Il progetto CompHematoPathology, finanziato dall’UE, sta portando il potere dell’intelligenza artificiale e della modellizzazione matematica nella diagnosi delle neoplasie ematologiche maligne. Utilizzando questi strumenti e dati di immagine annotati da esperti, CompHematoPathology prevede di sviluppare un modello basato sui dati per prevedere le dinamiche ematiche in salute e malattia. La promessa è quella di aumentare la produttività, migliorare le diagnosi e, in ultima analisi, migliorare il trattamento dei pazienti con neoplasie ematologiche maligne.
Obiettivo
Identifying hematologic malignancies still relies on the time-consuming and subjective visual assessment of images. Every day, cytologists and pathologists are confronted with rare diagnostic cells, ever-increasing image data, and heterogeneous disease manifestations. Although we understand blood better than any other human tissue, we are unable to quantitatively predict a patient’s blood dynamics from a measurement. Diagnosis thus depends on rough staging schemes and the expertise and intuition of the clinician.
In my proposal, I address these challenges by establishing computational hematopathology, a combination of artificial intelligence algorithms and mathematical models that will boost the currently prevailing manual assessment. Based on my experience in using these methods for scrutinizing stem cell differentiation I will combine the power of deep learning and mathematical modeling with digitized and expertly annotated image data. My unique approach enables me to design and parametrize a data-driven model to predict hematopoietic dynamics in health and disease. Since the interpretation of digitized slides is becoming the clinical standard, novel algorithms for standardized disease classification and improved diagnosis are critically needed now.
This interdisciplinary project merges methods from digital pathology, machine learning, image processing, and mathematical modeling. ComHematoPathology will provide novel approaches and software tools for automated classification of hematopathology image data, allowing for reproducible and precise diagnosis at an unprecedented level. This will increase throughput and standardize the diagnosis of blood diseases and will thus improve the treatment of patients suffering from hematologic malignancies.
Campo scientifico
- medical and health sciencesmedical biotechnologycells technologiesstem cells
- medical and health sciencesbasic medicinepathology
- medical and health sciencesclinical medicinehematology
- natural sciencescomputer and information sciencesartificial intelligencemachine learningdeep learning
- natural sciencesmathematicsapplied mathematicsmathematical model
Programma(i)
Argomento(i)
Meccanismo di finanziamento
ERC-COG - Consolidator GrantIstituzione ospitante
85764 Neuherberg
Germania