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Page 428                                                               Roy et al. Art Int Surg 2024;4:427-34  https://dx.doi.org/10.20517/ais.2024.69

               SURGERY: CONSIDERATIONS FOR ADOPTION AND IMPLEMENTATION
               The volume and complexity of clinical data are growing rapidly across all fields of medicine. In parallel,
               computational power is expanding and becoming more accessible, while human resources continue to
               remain relatively stagnant and limited in healthcare . It has been predicted that artificial intelligence (AI)
                                                           [1]
                                                                           [1]
               and machine learning (ML) will be ubiquitous in future clinical care . Significant areas of interest that
               could revolutionize care include processing of electronic health record data, image classification, and
               identification of medical errors . The promising appeal to incorporate AI into clinical practice must be
                                          [2,3]
               contextualized, and the intrinsic limitations to the use of ML algorithms acknowledged. Clinical translation
               of ML tools and prospective validation articles are scarce to date and a new set of considerations for
               adoption and implementation have been unveiled. This article reviews the current ML landscape in
               craniofacial surgery and highlights promises, challenges, and considerations for successful clinical
               translation.

               Current landscape of ML in craniofacial surgery
               The role of ML and AI in craniofacial surgery has previously been thoroughly reviewed . In a scoping
                                                                                            [4-9]
               review by Mak et al. (2020), the authors identified numerous craniofacial-based studies developing ML
               models . ML is particularly relevant to craniofacial surgery as it is a specialty that: (1) relies on imaging for
                     [4]
               diagnostic purposes; (2) uses standardized and universal anatomical landmarks (soft tissue and bony); (3)
               benefits from three-dimensional planning and surgical navigation; and (4) has variable outcomes and
               benefits from risk prediction . Thus far, published craniofacial surgery studies using AI have been
                                         [9]
               experimental and theoretical in nature, mostly relying on retrospective datasets with very limited sample
               sizes and generally single-centered.


               Cleft surgery
               The potential benefit of integrating AI into cleft care spans numerous facets of clinical care due to the varied
               presentations (from cleft lip to velopharyngeal insufficiency and dentoalveolar discrepancies) and evolution
               over patients’ growth and development. A scoping review identified previously explored areas for
               implementation of AI in cleft care : prediction of risk of developing cleft lip or palate, diagnosis (prenatal
                                            [10]
               cleft  presence),  severity  of  morphological  deformities  of  nose , speech  evaluation  (presence  of
                                                                          [11]
                                                   [12]
               hypernasality, assessment of intelligibility) , surgical planning (estimation of volumetric defect of alveolar
               cleft), prediction of need for orthognathic surgery, and more.

               Orthognathic surgery
               Orthognathic surgery lands itself well to be augmented by AI, although few studies have been published
               thus far. A review of possible applications demonstrated areas of interest for future studies, including :
                                                                                                       [13]
               complex diagnoses (superimposing numerous diagnostic tools for measurement of upper airways and
               management of obstructive sleep apnea), common diagnoses (lateral cephalogram review), treatment
               planning (taking into account the symbiotic relationship with orthodontic changes), creation of custom
               dental appliances, and much more. An early study (2019) within the field of orthognathic surgery proposed
               a proof of concept that AI (specifically convolutional neural networks) can be used in order to score facial
                                                                       [14]
               attractiveness and apparent age in orthognathic surgery patients . Assistance in diagnosis and surgical
               planning has also been proposed using CT scan images and comparing discrepancies in cephalometric
               measures between the AI-generated plan and the postoperative images .
                                                                          [15]

               Craniosynostosis and head shape difference
               Head shape differences are ubiquitous, and the rarity of craniosynostosis and the clinical ramifications of
               later diagnosis generate huge diagnostic importance, which can be aided by AI. A review from 2023
               explored the existing literature on the topic, highlighting that most studies thus far have used two-
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