IEEE Systems, Man and Cybernetics Magazine - July 2023 - 25

Figure 8. For landmark detection, a deep
learning model based on CNN architecture
is deployed. Figure 9 shows the predicted
landmark points. The obtained
results will be used to compute the different
clinical measurements needed to
characterize the skull shape and extract
the anomalies.
L1
L2
L3
L4
L5
L6
L7
L8
L9
Treatment Suggestion Using the
Semantic Network
The diagnostic reports generated by the
dental and skeletal modules will be used
to recognize the disease and suggest the
appropriate treatment. In this work, a list
of diseases and treatments is shown
based on the medical literature to build
the initial database needed for treatment
suggestions [26], [27], [28]. The suggestion
of treatment for a specific patient has
three levels. First, the system suggests
the recent successful treatment if the patient was previously
treated for the same disease. Second, and if the
patient was not affected by the disease before, the system
will suggest the successful treatment of the most similar
patient from the database. If not available, third, the system
will suggest the most commonly used treatment for
the diagnosed disease.
The generated system diagnosis and the suggested
treatment are then updated to the semantic network, creating
additional nodes if applicable. Figure 10 presents two
examples of adding new nodes to the semantic network.
Knowledge Translation
The collaborating partner dentists from Canada and international
clinics will provide sample images (with consent)
and diagnosis and treatment data, which will
support the research team to build training data and associated
analysis. The interviews with expert dentists and
dental data providers will offer expertise in the validation
and analysis of images, diagnosis, and treatment details,
which will be transferred to the research team. Obtaining
medical data from 20 patients is expected each year. In
addition, we will conduct around 28 interview sessions
with dentists and experts to annotate the collected data
and get their opinion about the algorithms, approach, and
integrated solutions. Thanks to the interaction with
experts and practitioners, the proposed toolbox is
enabled with an interactive user interface. Thus, the
experts can correct the wrong predictions of the AI models
to boost their performance. Moreover, we propose
handling the lack of an annotated data set by developing
an incremental model training framework that keeps
updating the annotated data from recent interactions with
the expert. All of these interactions between the toolbox
and the expert will be saved to the database and used to
L10
L11
L12
L13
L14
L15
L16
L17
L18
L19
Sella
Nasion
Orbitale
Porion
Subspinale
Supramentale
Pogonion
Menton
Gnathion
Gonion
Lower Incisal Incision
Upper Incisal Incision
Upper Lip
Lower Lip
Subnasale
Soft Tissue Pogonion
Posterior Nasal Spine
Anterior Nasal Spine
Articulate
Figure 8. Cephalogram annotation example showing the 19
landmarks (source [17]).
further train the deep learning models. We will communicate
with the Canadian Dental Association to get more
views and expertise on our solution and potential implementation
guidelines. Our team will communicate with
the Canadian Dental Regulatory Authorities Federation to
gain experience and application of automation in view of
the regulatory framework.
Figure 9. Example of skeletal landmark locations
detection: predicted landmarks (green) ground-truth
of landmarks (red).
July 2023 IEEE SYSTEMS, MAN, & CYBERNETICS MAGAZINE 25
IEEE Systems, Man and Cybernetics Magazine - July 2023

Table of Contents for the Digital Edition of IEEE Systems, Man and Cybernetics Magazine - July 2023
