Development of Predictive Models in Patients with Epiphora Using Lacrimal Scintigraphy and Machine Learning
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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.
KeywordsEpiphora Dacryocystography Lacrimal scintigraphy Machine learning Deep learning Convolutional neural network
Compliance with Ethical Standards
Conflict of Interest
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, and Seung Hwan Moon declare that they have no conflict of interest.
All procedures performed in studies involving human participants were in accordance with the ethical standards of our institutional review board and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.
The institutional review board of our institute approved this retrospective study, and the requirement to obtain informed consent was waived.
- 3.Fau KK, Wu D, Fau WD, Gong K, Fau GK, Dutta J, et al. Penalized PET reconstruction using deep learning prior and local linear fitting. IEEE Trans Med Imaging. 2018;37.Google Scholar
- 7.Juarez-Orozco LE, Knol RJJ, Sanchez-Catasus CA, Martinez-Manzanera O, van der Zant FM, Knuuti J. Machine learning in the integration of simple variables for identifying patients with myocardial ischemia. J Nucl Cardiol 2018.Google Scholar
- 8.Betancur J, Otaki Y, Motwani M, Fish MB, Lemley M, Dey D, et al. Prognostic value of combined clinical and myocardial perfusion imaging data using machine learning. J Am Coll Cardiol Img 2017.Google Scholar
- 18.Fontenla-Romero O, Perez-Sanchez B, Guijarro-Berdinas B. LANN-SVD: a non-iterative SVD-based learning algorithm for one-layer neural networks. IEEE Trans Neural Netw Learn Syst 2017:1–6.Google Scholar