Translational Applications of Artificial Intelligence and Machine Learning for Diagnostic Pathology in Lymphoid Neoplasms: A Comprehensive and Evolutive Analysis.

Julia Moran-Sanchez,Julia Moran-Sanchez,Antonio Santisteban-Espejo,Antonio Santisteban-Espejo,Antonio Santisteban-Espejo,Jose Perez-Requena,Marcial Garcia-Rojo,Marcial Garcia-Rojo,Miguel Angel Martin-Piedra

doi:10.3390/biom11060793

Abstract

Genomic analysis and digitalization of medical records have led to a big data scenario within hematopathology. Artificial intelligence and machine learning tools are increasingly used to integrate clinical, histopathological, and genomic data in lymphoid neoplasms. In this study, we identified global trends, cognitive, and social framework of this field from 1990 to 2020. Metadata were obtained from the Clarivate Analytics Web of Science database in January 2021. A total of 525 documents were assessed by document type, research areas, source titles, organizations, and countries. SciMAT and VOSviewer package were used to perform scientific mapping analysis. Geographical distribution showed the USA and People’s Republic of China as the most productive countries, reporting up to 190 (36.19%) of all documents. A third-degree polynomic equation predicts that future global production in this area will be three-fold the current number, near 2031. Thematically, current research is focused on the integration of digital image analysis and genomic sequencing in Non-Hodgkin lymphomas, prediction of chemotherapy response and validation of new prognostic models. These findings can serve pathology departments to depict future clinical and research avenues, but also, public institutions and administrations to promote synergies and optimize funding allocation.

Highlights

The storage of clinical information in the electronic medical record and the incorporation of omics data into the patient’s history have led to a novel scenario within pathology departments
A third-degree polynomic model defined by the equation y = 0.0518x3 − 1.511x2 + 17.345x − 35.972 (R2 = 0.9701) predicted that literature would double in 2027, and it would be three-fold the current number near 2031 (Figure 2C)
non-Hodgkin lymphoma (NHL) accounts for the most of research conducted during the whole period, here we reported machine learning (ML) approaches to identify chronic lymphocytic leukemia (CLL) patients at high risk of infection [68] and to optimize CLL diagnosis through Gene expression profiling (GEP) and artificial neural networks (NNs) (ANNs) [69]

Summary

Introduction

The storage of clinical information in the electronic medical record and the incorporation of omics data (genomic, transcriptomic, and proteomic) into the patient’s history have led to a novel scenario within pathology departments. Large volumes of information are available for investigators and clinicians, who need to process, integrate, and translate them into daily medical practice. This data-driven paradigm of 4P medicine (predictive, personalized, preventive, and participative) [1] requires the implementation of computer systems able to process this huge amount of clinical information. In this setting, artificial intelligence (AI) and machine learning (ML) tools have the potential to meliorate diagnostic precision and improve prediction accuracy, and, contribute to a better planification of personalized therapeutic strategies [2]. The European Union (EU) [6], the United

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