1. Machine learning to predict mesenchymal stem cell efficacy for cartilage repair
- Author
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Gareth Conduit, Steve Oh, Yin Lu, Yu Yang Fredrik Liu, Liu, Yu Yang Fredrik [0000-0002-8591-2599], Conduit, Gareth J. [0000-0003-3807-6361], Apollo - University of Cambridge Repository, and Conduit, Gareth J [0000-0003-3807-6361]
- Subjects
0301 basic medicine ,FOS: Computer and information sciences ,Cancer Research ,Medical Implants ,Physiology ,Swine ,medicine.medical_treatment ,Engineering and technology ,computer.software_genre ,Regenerative medicine ,Machine Learning ,0302 clinical medicine ,Animal Cells ,Medicine ,Immunology and Allergy ,Biology (General) ,Genetics (clinical) ,Computer and information sciences ,Ecology ,Stem Cells ,Stem Cell Therapy ,Stem-cell therapy ,Cell biology ,medicine.anatomical_structure ,Computational Theory and Mathematics ,Oncology ,Connective Tissue ,Modeling and Simulation ,Rabbits ,Stem cell ,Anatomy ,Cellular Types ,Biotechnology ,Research Article ,Drug Research and Development ,Neural Networks ,QH301-705.5 ,Immunology ,Bioengineering ,Biology ,Machine learning ,Mesenchymal Stem Cell Transplantation ,Models, Biological ,03 medical and health sciences ,Cellular and Molecular Neuroscience ,Text mining ,Artificial Intelligence ,Tissue Repair ,Genetics ,Animals ,Humans ,Clinical Trials ,Cartilage repair ,Molecular Biology ,Ecology, Evolution, Behavior and Systematics ,Clinical Genetics ,Pharmacology ,Medicine and health sciences ,Transplantation ,Tissue Engineering ,Biology and life sciences ,business.industry ,Cartilage ,Mesenchymal stem cell ,Computational Biology ,Mesenchymal Stem Cells ,Cell Biology ,FOS: Engineering and technology ,Rats ,Clinical trial ,Research and analysis methods ,030104 developmental biology ,Biological Tissue ,Medical Devices and Equipment ,Artificial intelligence ,Clinical Medicine ,business ,Physiological Processes ,computer ,030217 neurology & neurosurgery ,Neuroscience - Abstract
Inconsistent therapeutic efficacy of mesenchymal stem cells (MSCs) in regenerative medicine has been documented in many clinical trials. Precise prediction on the therapeutic outcome of a MSC therapy based on the patient’s conditions would provide valuable references for clinicians to decide the treatment strategies. In this article, we performed a meta-analysis on MSC therapies for cartilage repair using machine learning. A small database was generated from published in vivo and clinical studies. The unique features of our neural network model in handling missing data and calculating prediction uncertainty enabled precise prediction of post-treatment cartilage repair scores with coefficient of determination of 0.637 ± 0.005. From this model, we identified defect area percentage, defect depth percentage, implantation cell number, body weight, tissue source, and the type of cartilage damage as critical properties that significant impact cartilage repair. A dosage of 17 − 25 million MSCs was found to achieve optimal cartilage repair. Further, critical thresholds at 6% and 64% of cartilage damage in area, and 22% and 56% in depth were predicted to significantly compromise on the efficacy of MSC therapy. This study, for the first time, demonstrated machine learning of patient-specific cartilage repair post MSC therapy. This approach can be applied to identify and investigate more critical properties involved in MSC-induced cartilage repair, and adapted for other clinical indications., Author summary Cartilage damage affects the life quality of hundreds of millions of people, causing chronic joint pain and disability. Cartilage has poor regenerative capacity. Only minor damage could improve on its own or with passive treatments, while more severe damage often requires surgery. In recent decades, stem cell therapy has become a promising treatment option to reduce pain and repair cartilage. However, with complex mechanisms and various factors involved, efficient and consistent cartilage regeneration remains elusive. Our neural network learns information from clinical trials and animal studies to predict therapeutic outcomes along with the confidence level based on the patient’s condition. This machine learning approach provides an important reference and significant insights into the optimization of treatment strategies.
- Published
- 2020
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