Yuanyuan Gao1, Uwe Kruger2, Xavier Intes2, Steven Schwaitzberg3, Suvranu De4. 1. Center for Modeling, Simulation and Imaging in Medicine, Rensselaer Polytechnic Institute, Troy, NY. 2. Center for Modeling, Simulation and Imaging in Medicine, Rensselaer Polytechnic Institute, Troy, NY; Department of Biomedical Engineering, Rensselaer Polytechnic Institute, Troy, NY. 3. Jacobs School of Medicine and Biomedical Sciences, The State University of New York, Buffalo, NY; Department of Surgery, The State University of New York, Buffalo, NY; Buffalo General Hospital, NY. 4. Center for Modeling, Simulation and Imaging in Medicine, Rensselaer Polytechnic Institute, Troy, NY; Department of Biomedical Engineering, Rensselaer Polytechnic Institute, Troy, NY. Electronic address: des@rpi.edu.
Abstract
BACKGROUND: Contemporary surgical training programs rely on the repetition of selected surgical motor tasks. Such methodology is inherently open ended with no control on the time taken to attain a set level of proficiency, given the trainees' intrinsic differences in initial skill levels and learning abilities. Hence, an efficient training program should aim at tailoring the surgical training protocols to each trainee. In this regard, a predictive model using information from the initial learning stage to predict learning curve characteristics should facilitate the whole surgical training process. METHODS: This paper analyzes learning curve data to train a multivariate supervised machine learning model. One factor is extracted to define the trainees' learning ability. An unsupervised machine learning model is also utilized for trainee classification. When established, the model can predict robustly the learning curve characteristics based on the first few trials. RESULTS: We show that the information present in the first 10 trials of surgical tasks can be utilized to predict the number of trials required to achieve proficiency (R2=0.72) and the final performance level (R2=0.89). Furthermore, only a single factor, learning index, is required to describe the learning process and to classify learners with unique learning characteristics. CONCLUSION: Using machine learning models, we show, for the first time, that the first few trials contain sufficient information to predict learning curve characteristics and that a single factor can capture the complex learning behavior. Using such models holds the potential for personalization of training regimens, leading to greater efficiency and lower costs.
BACKGROUND: Contemporary surgical training programs rely on the repetition of selected surgical motor tasks. Such methodology is inherently open ended with no control on the time taken to attain a set level of proficiency, given the trainees' intrinsic differences in initial skill levels and learning abilities. Hence, an efficient training program should aim at tailoring the surgical training protocols to each trainee. In this regard, a predictive model using information from the initial learning stage to predict learning curve characteristics should facilitate the whole surgical training process. METHODS: This paper analyzes learning curve data to train a multivariate supervised machine learning model. One factor is extracted to define the trainees' learning ability. An unsupervised machine learning model is also utilized for trainee classification. When established, the model can predict robustly the learning curve characteristics based on the first few trials. RESULTS: We show that the information present in the first 10 trials of surgical tasks can be utilized to predict the number of trials required to achieve proficiency (R2=0.72) and the final performance level (R2=0.89). Furthermore, only a single factor, learning index, is required to describe the learning process and to classify learners with unique learning characteristics. CONCLUSION: Using machine learning models, we show, for the first time, that the first few trials contain sufficient information to predict learning curve characteristics and that a single factor can capture the complex learning behavior. Using such models holds the potential for personalization of training regimens, leading to greater efficiency and lower costs.
Authors: Lei Tian; Brady Hunt; Muyinatu A Lediju Bell; Ji Yi; Jason T Smith; Marien Ochoa; Xavier Intes; Nicholas J Durr Journal: Lasers Surg Med Date: 2021-05-20