Development of Predictive Models in Patients with Epiphora Using Lacrimal Scintigraphy and Machine Learning View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2019-02-07

AUTHORS

Yong-Jin Park, Ji Hoon Bae, Mu Heon Shin, Seung Hyup Hyun, Young Seok Cho, Yearn Seong Choe, Joon Young Choi, Kyung-Han Lee, Byung-Tae Kim, Seung Hwan Moon

ABSTRACT

We developed predictive models using different programming languages and different computing platforms for machine learning (ML) and deep learning (DL) that classify clinical diagnoses in patients with epiphora. We evaluated the diagnostic performance of these models. Between January 2016 and September 2017, 250 patients with epiphora who underwent dacryocystography (DCG) and lacrimal scintigraphy (LS) were included in the study. We developed five different predictive models using ML tools, Python-based TensorFlow, R, and Microsoft Azure Machine Learning Studio (MAMLS). A total of 27 clinical characteristics and parameters including variables related to epiphora (VE) and variables related to dacryocystography (VDCG) were used as input data. Apart from this, we developed two predictive convolutional neural network (CNN) models for diagnosing LS images. We conducted this study using supervised learning. Among 500 eyes of 250 patients, 59 eyes had anatomical obstruction, 338 eyes had functional obstruction, and the remaining 103 eyes were normal. For the data set that excluded VE and VDCG, the test accuracies in Python-based TensorFlow, R, multiclass logistic regression in MAMLS, multiclass neural network in MAMLS, and nuclear medicine physician were 81.70%, 80.60%, 81.70%, 73.10%, and 80.60%, respectively. The test accuracies of CNN models in three-class classification diagnosis and binary classification diagnosis were 72.00% and 77.42%, respectively. ML-based predictive models using different programming languages and different computing platforms were useful for classifying clinical diagnoses in patients with epiphora and were similar to a clinician’s diagnostic ability. More... »

PAGES

1-11

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s13139-019-00574-1

DOI

http://dx.doi.org/10.1007/s13139-019-00574-1

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1111949729


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