Machine learning to predict mesenchymal stem cell efficacy for cartilage repair
We collected data from 36 published articles on PubMed [13–48] to train and validate our machine learning models. Some articles comprised more than one type of cartilage injury models or treatment condition. In total, 15 clinical trial conditions and 29 animal 66 model conditions (1 goat, 6 pigs, 2 dogs, 9 rabbits, 9 rats, and 2 mice) on osteochondral injury or osteoarthritis were included, where MSCs were transplanted to repair the cartilage tissue. We documented each case with specific treatment condition into an entry by considering the cell- and treatment target-related factors as input properties, including species, body weight, tissue source, cell number, cell concentration, defect area, defect depth, and type of cartilage damage. The therapeutic outcomes were considered as output properties, which were evaluated using integrated clinical and histological cartilage repair scores, including the international cartilage repair society (ICRS) scoring system, the O’Driscoll score, the Pineda score, the Mankin score, the osteoarthritis research society international (OARSI) scoring system, the international knee documentation committee (IKDC) score, the visual analog score (VAS) for pain, the knee injury and osteoarthritis outcome score (KOOS), the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC), and Lyscholm score. In this study, these scores were linearly normalized to a number between 0 and 1, with 0 representing the worst damage or pain, and 1 representing the completely healthy tissue. The list of entries was combined together to form a database.
We have provided the details for the imputation algorithm in the subsection Handling missing data under Methods and a flowchart in Fig 2. Data imputation algorithm for the vector x was added in the manuscript for illustration. The pseudo-code for uncertainty calculation was shown in S1 Algorithm: A ensemble model to measure the ANN's prediction uncertainty. The original database gathered from the literature, and a ‘complete’ database with missing information filled from our neural network are also included, along with a sample neural network architecture file in Python.
Here we provide a Python notebook comprising a neural network that delivers the performance and results described in the manuscript. Documentation in the form of comments and installation guide is included in the Python notebook. This Python notebook along with the methods described in the manuscript provides sufficient details for other interested readers to either extend this script or write their own scripts and reproduce the results in the paper.