In January, 2020, the research paper of B&VIIT AI was published in TVST(Transitional Vision Science & Technology) under the subject of ‘Explainable Machine Learning Approach as a Tool to Understand Factors Used to Select the Refractive Surgery Technique on the Expert Level.’
Abstract
Purpose: Recently, laser refractive surgery options, including laser epithelial keratomileusis, laser in situ keratomileusis, and small incision lenticule extraction, successfully improved patients’ quality of life. Evidence-based recommendation for an optimal surgery technique is valuable in increasing patient satisfaction. We developed an interpretable multiclass machine learning model that selects the laser surgery option on the expert level.
Methods: A multiclass XGBoost model was constructed to classify patients into four categories including laser epithelial keratomileusis, laser in situ keratomileusis, small incision lenticule extraction, and contraindication groups. The analysis included 18,480 subjects who intended to undergo refractive surgery at the B&VIIT Eye center. Training (n = 10,561) and internal validation (n = 2640) were performed using subjects who visited between 2016 and 2017. The model was trained based on clinical decisions of highly experienced experts and ophthalmic measurements. External validation (n = 5279) was conducted using subjects who visited in 2018. The SHapley Additive ex-Planations technique was adopted to explain the output of the XGBoost model.
Results: The multiclass XGBoost model exhibited an accuracy of 81.0% and 78.9% when tested on the internal and external validation datasets, respectively. The SHapley Additive ex-Planations explanations for the results were consistent with prior knowledge from ophthalmologists. The explanation from one-versus-one and one-versus-rest XGBoost classifiers was effective for easily understanding users in the multicategorical classification problem.
Conclusions: This study suggests an expert-level multiclass machine learning model for selecting the refractive surgery for patients. It also provided a clinical understanding in a multiclass problem based on an explainable artificial intelligence technique.
Translational Relevance: Explainable machine learning exhibits a promising future for increasing the practical use of artificial intelligence in ophthalmic clinics.
Introduction
Refractive surgery techniques were developed during the past decade and successfully improved patients’ quality of life. Laser refractive surgery procedures including laser epithelial keratomileusis (LASEK), laser in situ keratomileusis (LASIK), and small incision lenticule extraction (SMILE) produced excellent visual outcomes for patients with refractive error.1 Currently, a selection of refractive surgery options are available in most eye clinics to treat refractive error by considering each patient’s ophthalmologic information. Each surgical option exhibits advantages and disadvantages, and thus a surgeon should recommend an optimal option after carefully reviewing patient data.2 Recently, machine learning, which is an area of artificial intelligence research, is increasingly popular in clinical medicine due to its ability to handle large data with high accuracy. It constructs statistical prediction models from datasets and estimates a new data instance. Support vector machines (SVMs), random forests (RFs), artificial neural networks (ANNs), and least absolute shrinkage and selection operator (LASSO) constitute widely used approaches in machine learning.3,4 A previous study indicated that the machine learning technique can evaluate medical information to identify candidates for corneal refractive surgery.5 However, previous machine learning models are considered as a black box and lack an explicit knowledge representation.6 They are unable to provide reasoning and explanations on a decision in a manner similar to human experts. Currently, the concept of explainable artificial intelligence is introduced in the field of medicine.7 The explainable model allows users to focus on a rational decision and to verify if the model operates properly. The SHapley Additive ex-Planations (SHAP) is a promising solution to construct an explainable system.8 This technique is used in several tasks in data mining research while selecting informative variables and predicting clinical values with higher interpretability. With advances in the visualization method for SHAP values, the technique is widely used to analyze data.9 However, previous methods were limited in explaining the result of a single instance in a multicategorical problem because it is impossible for a single SHAP value to indicate 3 or more classes.10 To determine the optimal surgical technique based on medical evidence and patient’s expectation for surgery and recovery, surgeons should consider several ocular measurements and patient factors such as dry eye, lifestyle, and budget. In the study, we constructed an expert-level decision support system to recommend the surgical option based on large clinical data and machine learning. An explainable machine learning method was adopted to demonstrate as to why the machine learning model should decide the surgery technique in each case. Specifically, we construct a multicategorical prediction model because there are multiple surgical options, including LASEK, LASIK, SMILE, and contraindication to corneal laser surgery. The machine learning model was constructed based on clinical decisions of highly experienced experts and was validated in a Korean population.
The study proposes using an explainable artificial intelligence technique, specifically the method mentioned above, to construct an explainable system for selecting the optimal surgical technique. This approach allows users to focus on a rational decision and verify if the model is operating properly. Thank you for sharing such an insightful article. Hope to read more content just like this in the future.