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Page 309 Brenac et al. Art Int Surg 2024;4:296-315 https://dx.doi.org/10.20517/ais.2024.49

ML has also been applied to perform automatic detection of craniofacial conditions, facilitating early
diagnosis based on photographic images and annotated datasets. In an early study by Geisler et al., neural
networks have successfully achieved an overall testing accuracy of 90.6% for the detection of
[41]
craniosynostosis, opening the field for earlier diagnosis and minimizing the need for CT scans . Another
study by Knoops et al. described a computer-assisted model framework involving supervised learning for
[42]
diagnostic, predictive outcome and treatment stimulation in craniofacial surgery . The algorithm was
trained on non-ionizing 3D face scans of healthy faces and orthognathic patients, and it provided an
[42]
accurate classification with a 95.5% sensitivity and 95.2% specificity . The algorithm was also able to
stimulate patient-specific postoperative outcomes with a mean accuracy of 1.1 +/- 0.3 mm. compared to
conventional surgical planning, suggesting that the model could predict the postoperative shape of the face
[42]
in a single step and reduce the time for the planning process .
In addition to data classification, the advent of generative AI tools such as DALL-E2 can enable the creation
[43]
of various types of synthetic images or text on demand . Cosmetic surgery, given its inherently visual
nature, can, therefore, take advantage of generative AI to simulate post-surgery results even prior to the
procedure. In one study by Lim et al., DALL-E2, Midjourney and Blue Willow were evaluated in their utility
to provide images clinically relevant after cosmetic surgery . In future cases, surgeons could virtually
[44]
simulate different interventions and examine the AI outcomes with patients for preoperative scoring and
evaluation, which may help surgeons fine-tune the planned procedure and aim for specific modeled
outcomes.

Hand surgery
Similar to radiology and other imaging-dominated disciplines, AI has been applied in hand surgery for
fracture detection . Despite the performance of standard clinical examination and X-ray characterization,
[45]
scaphoid fractures, representing 15% of acute wrist fractures, are missed initially in nearly 16% of cases .
[45]
Therefore, there is an existing need for ML algorithms to improve the detection of scaphoid fractures, wrist
fractures, and other cases within emergency departments. In a recent study by Ozkaya et al., an ANN model
for scaphoid fracture detection on anteroposterior wrist radiographs was compared to three physicians (two
orthopedic specialists and one physician in the emergency department) . The ANN showed a 76%
[46]
sensitivity and 92% specificity, which exceeded the performance of emergency department physicians, but
still lagged behind that of an experienced orthopedic specialist . However, the addition of clinical
[46]
examination findings in the algorithm, as well as lateral views, could enhance the sensitivity of the ML
algorithm. Further, Oeding et al. discovered that recent AI models have demonstrated excellent
performance in detecting scaphoid fractures and radius fractures, with AUC values of 0.77-0.96 and 0.90-
0.99, respectively . The majority of AI models have currently demonstrated comparable or better
[47]
performance than many clinical experts, and further improvements in speed and performance may result
from larger data sets, more powerful computing resources, and increasingly open-source code toolboxes .
[47]
Based on patient-specific data such as age, smoking status, dominant hand, occupation, and subtype of
fracture, ML could also provide useful information in the acute setting, such as deciding whether to perform
one type of hand surgery over another (e.g., replantation vs. amputation) [48,49] . Recently, two studies by
Hoogendam et al. and Loos et al. from the Hand and Wrist Study Group in the Netherlands have
introduced user-friendly graphical applications, supported by ML algorithms, for predicting postoperative
function [48,49] . These ML approaches were applied to data from 2,119 patients with carpal tunnel syndrome
(CTS) and 2,653 patients with thumb carpometacarpal osteoarthritis [48,49] . These applications calculated the
probability of functional improvement 6 months after CTS surgery and 12 months after first
carpometacarpal joint surgery, based on preoperative patient-reported outcome measures (PROMs). While
these ML algorithms are freely available, it is crucial to note that such online applications often lack external