Predictive modeling of proliferative vitreoretinopathy using automated machine learning by ophthalmologists without coding experience

Autor: Julia Sebag, Mikael Sebag, Renaud Duval, Anthony Fanous, Razek Georges Coussa, Ghofril Kahwati, Fares Antaki
Jazyk: angličtina
Rok vydání: 2020
Předmět:
Pars plana
Male
Proliferative vitreoretinopathy
lcsh:Medicine
Feature selection
Machine learning
computer.software_genre
Predictive markers
Article
Machine Learning
03 medical and health sciences
Naive Bayes classifier
0302 clinical medicine
Postoperative Complications
Vitrectomy
medicine
Humans
Diagnosis
Computer-Assisted
lcsh:Science
Aged
Retrospective Studies
Multidisciplinary
Ophthalmologists
business.industry
Vitreoretinopathy
Proliferative
lcsh:R
Retinal Detachment
Retinal detachment
Retrospective cohort study
Middle Aged
medicine.disease
eye diseases
Support vector machine
medicine.anatomical_structure
Risk factors
Outcomes research
030221 ophthalmology & optometry
Female
lcsh:Q
Artificial intelligence
sense organs
business
computer
030217 neurology & neurosurgery
Algorithms
Coding (social sciences)
Zdroj: Scientific Reports, Vol 10, Iss 1, Pp 1-10 (2020)
Scientific Reports
ISSN: 2045-2322
Popis: We aimed to assess the feasibility of machine learning (ML) algorithm design to predict proliferative vitreoretinopathy (PVR) by ophthalmologists without coding experience using automated ML (AutoML). The study was a retrospective cohort study of 506 eyes who underwent pars plana vitrectomy for rhegmatogenous retinal detachment (RRD) by a single surgeon at a tertiary-care hospital between 2012 and 2019. Two ophthalmologists without coding experience used an interactive application in MATLAB to build and evaluate ML algorithms for the prediction of postoperative PVR using clinical data from the electronic health records. The clinical features associated with postoperative PVR were determined by univariate feature selection. The area under the curve (AUC) for predicting postoperative PVR was better for models that included pre-existing PVR as an input. The quadratic support vector machine (SVM) model built using all selected clinical features had an AUC of 0.90, a sensitivity of 63.0%, and a specificity of 97.8%. An optimized Naïve Bayes algorithm that did not include pre-existing PVR as an input feature had an AUC of 0.81, a sensitivity of 54.3%, and a specificity of 92.4%. In conclusion, the development of ML models for the prediction of PVR by ophthalmologists without coding experience is feasible. Input from a data scientist might still be needed to tackle class imbalance—a common challenge in ML classification using real-world clinical data.
Databáze: OpenAIRE
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