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Roy et al. Art Int Surg 2024;4:427-34 https://dx.doi.org/10.20517/ais.2024.69 Page 429

[16]
dimensional photographs for model building in which image orientation had a large impact on outcome .
A team at the Hospital for Sick Children, Toronto, is also developing a model compatible with three-
dimensional photographic analysis for diagnostic purposes and is validating it into a mobile capture for
[17]
widespread use and patient empowerment .

Craniofacial trauma
The detection of facial trauma on imaging has been an area of interest for the introduction of AI . Overall
[18]
performance of models being developed, such as the DeepCT, has been excellent with high reported
sensitivity (89%) and specificity (95%) while using two-dimensional models with CT scan images .
[19]
Detection of facial fractures using three-dimensional images is also being explored within the biotechnology
[20]
literature . Various models have been used, including CNNs and deep learning systems using a one-stage
detection called you only look once (YOLO) [18,21,22] .

Facial reanimation
Thus far, AI represents a great promise of revolutionizing outcomes assessment for facial reanimation.
Traditionally, researchers and surgeons have used static images of smile commissure excursion using
various tools to provide an outcome assessment . With the use of ML technology, facial expression
[23]
tracking and, therefore, dynamic facial assessment are becoming possible. Video analysis of cross-facial
nerve graft (CFNG) and free gracilis muscle patients took place in a proof of concept study by Boonipat
et al. . This methodology enabled the authors to analyze symmetry, excursion, and overall facial
[24]
movements via the review of more than 500 facial landmarks. Another aspect of facial reanimation surgery
that has remained difficult to capture with existing outcome measures includes smile spontaneity.
Dusseldorp et al. compared controls with patients having undergone CFNG, masseter nerve coaptation, and
dual innervation to assess the feasibility of using such a tool to review spontaneity . Although promising
[25]
innovative outcomes analyses are being developed, the surgical reconstruction of facial palsy focuses on the
lower face and smile, whereas AI technology considers the face as a whole.

Clinical promises
To many clinicians, AI represents a new technological advancement that can revolutionize their practice,
yet also a poorly understood and intimidating one. Such paradoxical perceptions of AI can be explained by
the power that is inherent to it and the often lack of transparency behind its use on our electronic devices,
social media, and more. Transparency, safety, and methodological rigor are central to evidence-based
medicine and can be enabled with established reporting standards tools. A new initiative, the MINimum
Information for Medical AI Reporting (MINIMAR), proposes such reporting standards . MINIMAR
[2]
highlights four fundamental areas of transparency and reporting: (1) study population and setting; (2)
patient demographic characteristics; (3) model architecture; and (4) model evaluation. This concept is
[26]
further explored by research done by Sendak et al. with the “Model Facts” label . To maximize the effective
and positive implementation of an AI model, adherence to such standards of reporting is highly
recommended.

Some of the highly anticipated promises of AI are centered around the optimization of patient-centered care
and outcomes. The hope is that detailed and precise ML algorithms can help enhance a clinician’s
diagnostic, management, and prognostic capabilities, as well as monitor and decrease medical errors. AI
may also allow patients to have a sense of improved ownership of their data and empower them with the
ability to interpret their health information . Such optimization of care would have a system-level impact
[1]
and could allow for more efficient workflow, better resource allocation, and improved clinical outcomes, to
name a few .
[1]