Dental Asia November/December 2023

UNDER THE SPOTLIGHT
demographics so as to not sway results
towards a particular group. A variety of IOS
from different companies was also used in
the data collection so the model is not centred
on a single user or single company.
Another training procedure of the models is
that they are trained in three different phases,
where the results accuracy is validated by
an expert clinician. “In every phase several
dental surgeons were involved in manual
reannotation and result validation. With all
these steps, we make sure that we have a
good final prediction,” said Dr Furqan.
This ‘iteration process’ would have a
downstream effect of improving the overall
care quality throughout the NUHS cluster,
noted Dr Lu. “It takes around 10 years to train
a novice clinician to become an expert in
diagnosing a clinical problem accurately and
making the most appropriate evidence-based
recommendation for the patient. With this
iteration process, we can eventually deploy
AI-assisted detection and planning tools
modelled after clinical experts across all
community dental services. Deploying clinical
expertise throughout the community dental
services is something which is currently
not possible due to limitations in dental
manpower.”
“NUCOHS is at a position where we are
involved in the end-to-end pipeline of patient
care and dental workforce training. We have
tertiary specialist centres, primary dental care
at the polyclinics, and the Faculty of Dentistry
and School of Oral Health Therapy that oversee
the training of the future dental workforce. The
primary care dental physicians and therapists
are the ones who identify oral diseases at the
community level. As we transform our dental
care model in the healthcare service, we will
need to fine-tune the dental training curriculum
to keep abreast with developments in the
industry that has evolved with the increasing
adoption of digital technology,” he said.
In the future, Dr Lu added, all data gathered and
analysed paints a fuller picture of the patient
which allows for more holistic and customised
treatment plans. In addition, risk prediction,
preventive measures, and early disease
detection will be performed more accurately as
we leverage on digital tools and platforms to
promote dental health.
“In precision dentistry, patient data will be
analysed regularly through dental imaging
modalities, wearables and biosensors
that will monitor for changes in dental and
oral conditions like tooth wear and saliva
biomarkers, social behaviours such as
toothbrushing habits, and lifestyle modifiers
like diet composition and smoking. Targeted
measures to modify behaviours and restore
dental health will be recommended based on
the presenting data.”
A PREVENTIVE VISION OF AI IN
PRIMARY DENTAL CARE
To reach this vision of AI as a preventive tool
for population health, Dr Lu highlighted that
time is still needed for systemic shifts, beyond
the dental clinic. At the national level, data
management practices can be further defined
and harmonised across the industry.
Dr Furqan highlighted that a major issue
faced was in collating data from different
data silos. It took them three years to onboard
all stakeholders and consolidate what was
needed.
“In AI, what we need is a large and good
quality of data sets. We can always go for the
synthetic data or open-source data, but it will
always be very far from what is happening on
the ground,” he said. “If you compare the dental
clinical data from the American or European
patients, we can see significant difference
when compared to an Asian population.”
In other words, the American and European
data sets will perform differently from the
Asian data sets, and hence cannot be used.
Furthermore, security protocols in dealing with
patient data and difficulty in aligning interest
with commercial solutions necessitated the
development of in-house solutions.
Challenges such as institutional internet
separation places commercial AI solutions out
of reach. “What will help us in the future is to
revisit current rules and regulations to make
it more conducive to use existing commercial
solutions on patient identifiable data. If
enterprise need to share the solution with
hospitals, they need to package their solution
such that it has no dependencies and can be
deploy in restricted environments. Currently
there are very few or even none out there that
we can conveniently plug into the hospital’s
network,” said Dr Lu.
Data management practices and security concerns posed challenges for Dr Furqan and Dr Lu in
developing SMILE AI