Osaka University, Japan
SC16 BOOTH 1437

Towards a Smart Healthcare System for Orthodontic and Dental Treatment
Motivation

Challenges● Increasing demand and costs for healthcare, exacerbated by● A great amount of interests and motivation in providing effective● A relative shortage of doctors or clinical manpower is also a● D o c to r s a r e r e q ui r e d to p r ov i d e i m m e d i a t e a n d a c c u ra t e● Large amounts of heterogeneous medical data have become● The rapidly increasing availability of Big and Complex EHR data isageing populations, are serious concerns worldwide.
problem that leads to increase their workload.

available in various healthcare organizations.
Electronic Healthcare Records (EHR) are the fundamental
resource to support medical practices or help derive healthcare
insights.
M ost of the medical prac tices are c ompleted by medical
professionals backed by their experiences.
Clinical researches are conducted by researchers via
painstaking designed and costly experiments.

healthcare services through Smarter Healthcare Systems.
diagnoses and proper treatments for patients.

bec oming the driving force for the adoption of data- driven
approaches to automate healthcare related tasks.

● It is also a challenge to achieve earlier disease detection, moreaccurate prognosis, faster clinical research advance and the best
ﬁt for patient management.

ClusterSupercomputer
Virtualization
Systems

Aim

Secure VPN
connecting CMC andany offices
in Department● This project aims to enhance the medical operation efficiency and● It is overburdened for doctors to properly manage a sequence ofoperations including hearing, diagnosis, surger y, progress
checkup, counselling, treatment, etc. for all patients.
Especially, doctors spend a lot of time and effort to manuallydiagnose by looking at massive number of oral and face photo
images and x-rays.
extract morphological features of face from CT scan and MRI.
make plans of orthodontic procedure or treatment for patients.

Smart Healthcare System

Front End

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Dept. of Orthodontics andDentofacial Orthopedics

Cybermedia Center (CMC)

Figure 1. Secure and high performance smart healthcare systems

Data Analytics

improve the quality of healthcare services leveraging highperformance computing resources and advanced machine learningtechnologies.

Healthcare Services & Applications
Visualization

Complex Data Analytics Methods
FeatureSelection

Analytic Model
Selection

PredictiveAnalysis

Statistical
Learning

Deep Learning

Correlation
Learning

High Performance
Computing Resources● Smar t Healthcare System operates Data Curation and Data

Obtained raw EMR data is probably heterogeneous composed ofstructured data, free-text data (such as doctors’ notes), image
data (such as MRI images) and sensor data. Hence, data
extraction is of great concern for further analysis.
Data cleansing is necessary to remove inconsistencies anderrors.
Data annotation with medical experts’ assistance contributes toef fe c t i venes s an d ef ﬁ c i en cy of this wh ol e p ro c es s fromacquisition to extraction and cleansing.
Data integration is employed to combine various sources of data,such as different hospitals’ data for the same patient.
Finally, statistical, descriptive and predictive analysis of different
types will be performed on processed EMR data.
The analysis results are interpreted and visualized, and are usedto construct medical knowledge and ontology for better andmore accurate analysis.

Data Curation

Analytics, supported by High Performance Computing Resources.

● Figure 2 illustrates the pipeline for Big healthcare data analysis.

Structured Data
Data
Extraction

Input Data

Sensor Data

Data
Annotation

Data
Integration

Convolution & Pooling operations

BG

Multi-layer Perceptron

IOTN(Labels)R

Convolutional Neural Network
Features / Labels

The specific objectives are to develop a system that automatically

Lookup Table
Word
Embedding● computes Index of Orthodontic Treatment Priority (IOTN), one of

the severity measures for malocclusion and jaw abnormality, which
determines whether orthodontic treatment is necessary;● extracts facial morphological features (e.g., points and measure);

Knowledge base(text documents)● generates medical certificates or checkup lists;from oral and face photo images and x-rays or cephalogram.

Unstructured Data

Data
Cleansing

Figure 2. An overview of system architecture

Example Applications● provides a set of necessary procedure/treatment recommendations;

Medical Knowledge / Ontology

Raw Data Curated Data Knowledge baseWord1/
Phrase1

LSTM

Word2/
Phrase2

Word2/
Phrase2

LSTM

Word3/
Phrase3

LSTM

WordN/
PhraseN@Koseki Orthodontic Office

Text Generating Recurrent Network
Figure 3. An illustration of example applications

Cybermedia Center & School of Dentistry @ Osaka University

http://www.cmc.osaka-u.ac.jp/