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

  • 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 MoonEmail author
Original Article



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.


Epiphora 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.

Ethical Approval

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.

Informed Consent

The institutional review board of our institute approved this retrospective study, and the requirement to obtain informed consent was waived.

Supplementary material

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Copyright information

© Korean Society of Nuclear Medicine 2019

Authors and Affiliations

  • Yong-Jin Park
    • 1
  • Ji Hoon Bae
    • 1
  • Mu Heon Shin
    • 1
  • Seung Hyup Hyun
    • 1
  • Young Seok Cho
    • 1
  • Yearn Seong Choe
    • 1
  • Joon Young Choi
    • 1
  • Kyung-Han Lee
    • 1
  • Byung-Tae Kim
    • 1
  • Seung Hwan Moon
    • 1
    Email author
  1. 1.Departments of Nuclear Medicine, Samsung Medical CenterSungkyunkwan University School of MedicineSeoulSouth Korea

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