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Brenac et al. Art Int Surg 2024;4:296-315 https://dx.doi.org/10.20517/ais.2024.49 Page 310

validation, which is essential for ML algorithms to be generalized to other patient cohorts.

Wound healing and burn surgery
While postoperative wound assessment and management are essential to ensure optimal treatment, no
“gold standard” for scar evaluation currently exists . Challenges in scar evaluation may arise from the
[50]
variability in clinical assessments and difficulty in achieving consistent accuracy in follow-up evaluations. A
recent study by Kim et al. introduced DNN models to classify postoperative scars based on scar severity.
This model was trained with both an image-based AI model and a model based on clinical variables related
[50]
to postoperative scars, such as patient demographic data, scar age, and symptoms . Four scar severity
groups were successfully classified using these image-based AI models at a performance level comparable to
[50]
that of board-certified dermatologists, underscoring the efficacy of AI in clinical assessments . A pilot
study by Squiers et al. separately demonstrated the utility of combining image-based analysis with ML risk
factor assessment in predicting the healing outcomes of primary amputation wounds . The level of
[51]
amputation was determined by the subject’s surgeon prior to imaging, and was based on clinical judgment
[51]
such as patient history, physical exam, and any perfusion studies . Multispectral imaging of the subjects’
lower extremity planned for amputation was also conducted on postoperative day 30 . Analysis of
[51]
multispectral imaging demonstrated greater effectiveness in predicting primary amputation wound healing
relative to surgeon judgment, with an 88% accuracy rate compared to 56% . If further evaluation and/or
[51]
external validation confirm these findings, this type of ML tool may enhance the decision-making process in
wound healing treatments.

Certain complex wounds, such as those from diabetes and burns, are particularly susceptible to
complications and delayed healing due to impaired circulation and increased risk of infection. Recently,
advancements in burn management have been integrated with AI tools to enhance the treatment of burn
wounds . DL-based analysis models were able to identify the depth of early burn wounds using inputs
[19]
based on clinical photographs of the wound. Furthermore, in a study by Robb et al., ANNs provided
accurate diagnoses of burn injuries based on color attributes and successfully classified burns into
[52]
standardized categories, achieving a diagnostic accuracy of approximately 80% . Apart from their
beneficial role in burn assessment, AI algorithms have the potential to aid in clinical decision making by
accurately predicting clinical outcomes in wound healing, such as the need for skin grafts or amputation .
[52]
Diabetic wounds similarly pose significant challenges in the clinic due to their higher complications and
[53]
slow healing, which may be addressed using AI techniques for modeling and therapeutic discovery . In
one early example, Xue et al. used AI tools to identify a novel therapeutic agent for diabetic wound healing
by predicting molecular interactions between the drug Trichostatin A and its receptors at the wound site .
[53]
Ultimately, AI tools can support the personalized treatment of wounds and burns by integrating clinical
data, patient-specific risk factors, biological modeling, and prediction of potential postoperative
complications.

3D TISSUE MODELING AND PRINTING
3D and predictive modeling
Given the unique size and geometry of each patient’s surgical site, 3D modeling has been utilized for
[42]
patient-specific surgical planning . Similarly, the advent of 3D printing technologies has enabled the
design of increasingly site- and patient-specific constructs for surgical implantation . Digital models of a
[54]
surgical site can be reconstructed using traditional medical imaging techniques such as CT or magnetic
resonance imaging (MRI) . In addition to supporting the preoperative planning of incisions, positioning,
[42]
and other factors, these models can utilize AI methods to simulate patient-specific changes in tissue
geometry (e.g., facial shape) that may result from a procedure [42,55] . In one example, Knoops et al. utilized
finite element modeling to develop patient-specific predictions of maxillofacial transformation based on