ALICE: a hybrid AI paradigm with enhanced connectivity and cybersecurity for a serendipitous encounter with circulating hybrid cells
| Autor: | Kaili Qin, Rongbin Pan, Ray P. S. Han, Ying Jing Ng, Zhipeng Wang, Huaping Pan, Kok Suen Cheng, Xuan Liao, Benson Kiprono Kosgei, Huan Xing, Binglin Li, Stephene Shadrack Meena |
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| Rok vydání: | 2020 |
| Předmět: |
Male
0301 basic medicine hybrid artificial intelligence Computer science Internet of Things circulating hybrid cells Medicine (miscellaneous) Cell Count Computer security computer.software_genre Machine Learning cell phenotyping software 03 medical and health sciences ALICE 0302 clinical medicine Circulating tumor cell Predictive Value of Tests Biomarkers Tumor Image Processing Computer-Assisted Enumeration Humans Average recall Liquid biopsy Pharmacology Toxicology and Pharmaceutics (miscellaneous) Computer Security Aged computer.programming_language Principal Component Analysis Liquid Biopsy Reproducibility of Results image forgery detection Middle Aged Neoplastic Cells Circulating Random forest Pancreatic Neoplasms Support vector machine 030104 developmental biology Microscopy Fluorescence 030220 oncology & carcinogenesis Female Alice (programming language) Robust principal component analysis computer Software Research Paper |
| Zdroj: | Theranostics |
| ISSN: | 1838-7640 |
| Popis: | A fully automated and accurate assay of rare cell phenotypes in densely-packed fluorescently-labeled liquid biopsy images remains elusive. Methods: Employing a hybrid artificial intelligence (AI) paradigm that combines traditional rule-based morphological manipulations with modern statistical machine learning, we deployed a next generation software, ALICE (Automated Liquid Biopsy Cell Enumerator) to identify and enumerate minute amounts of tumor cell phenotypes bestrewed in massive populations of leukocytes. As a code designed for futurity, ALICE is armed with internet of things (IOT) connectivity to promote pedagogy and continuing education and also, an advanced cybersecurity system to safeguard against digital attacks from malicious data tampering. Results: By combining robust principal component analysis, random forest classifier and cubic support vector machine, ALICE was able to detect synthetic, anomalous and tampered input images with an average recall and precision of 0.840 and 0.752, respectively. In terms of phenotyping enumeration, ALICE was able to enumerate various circulating tumor cell (CTC) phenotypes with a reliability ranging from 0.725 (substantial agreement) to 0.961 (almost perfect) as compared to human analysts. Further, two subpopulations of circulating hybrid cells (CHCs) were serendipitously discovered and labeled as CHC-1 (DAPI+/CD45+/E-cadherin+/vimentin-) and CHC-2 (DAPI+ /CD45+/E-cadherin+/vimentin+) in the peripheral blood of pancreatic cancer patients. CHC-1 was found to correlate with nodal staging and was able to classify lymph node metastasis with a sensitivity of 0.615 (95% CI: 0.374-0.898) and specificity of 1.000 (95% CI: 1.000-1.000). Conclusion: This study presented a machine-learning-augmented rule-based hybrid AI algorithm with enhanced cybersecurity and connectivity for the automatic and flexibly-adapting enumeration of cellular liquid biopsies. ALICE has the potential to be used in a clinical setting for an accurate and reliable enumeration of CTC phenotypes. |
| Databáze: | OpenAIRE |
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