Rapid diagnosis of hereditary haemolytic anaemias using automated rheoscopy and supervised machine learning

Recent advancements in machine learning for bone marrow cell morphology analysis

As machine learning progresses, techniques such as neural networks, decision trees, and support vector machines are being increasingly applied in the medical domain, especially for tasks involving large datasets, such as cell detection, recognition, classification, and visualization. Within the domain of bone marrow cell morphology analysis, deep learning offers substantial benefits due to its robustness, ability for automatic feature learning, and strong image characterization capabilities. Deep neural networks are a machine learning paradigm specifically tailored for image processing applications. Artificial intelligence serves as a potent tool in supporting the diagnostic process of clinical bone marrow cell morphology. Despite the potential of artificial intelligence to augment clinical diagnostics in this domain, manual analysis of bone marrow cell morphology remains the gold standard and an indispensable tool for identifying, diagnosing, and assessing the efficacy of hematologic disorders. However, the traditional manual approach is not without limitations and shortcomings, necessitating, the exploration of automated solutions for examining and analyzing bone marrow cytomorphology. This review provides a multidimensional account of six bone marrow cell morphology processes: automated bone marrow cell morphology detection, automated bone marrow cell morphology segmentation, automated bone marrow cell morphology identification, automated bone marrow cell morphology classification, automated bone marrow cell morphology enumeration, and automated bone marrow cell morphology diagnosis. Highlighting the attractiveness and potential of machine learning systems based on bone marrow cell morphology, the review synthesizes current research and recent advances in the application of machine learning in this field. The objective of this review is to offer recommendations to hematologists for selecting the most suitable machine learning algorithms to automate bone marrow cell morphology examinations, enabling swift and precise analysis of bone marrow cytopathic trends for early disease identification and diagnosis. Furthermore, the review endeavors to delineate potential future research avenues for machine learning-based applications in bone marrow cell morphology analysis.

Integrating artificial intelligence into haematology training and practice: Opportunities, threats and proposed solutions

There remains a limited emphasis on the use beyond the research domain of artificial intelligence (AI) in haematology and it does not feature significantly in postgraduate medical education and training. This perspective article considers recent developments in the field of AI research in haematology and anticipates the potential benefits and risks associated with its deeper integration into the specialty. Anxiety towards the greater use of AI in healthcare stems from legitimate concerns surrounding data protection, lack of transparency in clinical decision-making, and erosion of the doctor-patient relationship. The specialty of haematology has successfully embraced multiple disruptive innovations. We are at the cusp of a new era of closer integration of AI into routine haematology practice that will ultimately benefit patient care but to harness its benefits the next generation of haematologists will need access to bespoke learning opportunities with input from data scientists.

Screening tools for hereditary hemolytic anemia: new concepts and strategies

INTRODUCTION

Hereditary hemolytic anemias are a group of rare and heterogeneous disorders due to abnormalities in structure, metabolism, and transport functions of erythrocytes; they may overlap in clinical and hematological features making differential diagnosis difficult, particularly in mild and atypical forms.

AREAS COVERED

In the present review, the main tools currently adopted in routine hematologic investigation for the diagnosis of hereditary hemolytic anemias are described, together with the new diagnostic approaches that are being to be developed in the next future. Available recommendations in this field together with a systematic review through MEDLINE, EMBASE, and PubMED for publications in English from 2000 to 2020 in regards to diagnostic aspects of hereditary hemolytic anemias have been considered.

EXPERT OPINION

The recent development of specific molecules and treatments for hereditary hemolytic anemias and the increased interest in translational research raised the attention on differential diagnosis and the demand for novel diagnostic assays and devices. Automatic blood cell analyzers, omic-approaches including NGS technologies, and development of new automated tools based on artificial neural networks definitely represent the future strategies in this field.