Page 110 - Read Online
P. 110

Shen et al. Art Int Surg. 2025;5:150-9  https://dx.doi.org/10.20517/ais.2024.71                                                             Page 152

               Predictive analytics powered by ML models have emerged as crucial tools in preoperative risk assessment
               and management. In a study by O’Neill et al., a ML model was used to predict flap failure in microvascular
               breast reconstruction. It identified high-risk patient groups, such as those with obesity, comorbidities, and
                                                                               [4]
               smoking habits, allowing for targeted interventions and improved outcomes . With tools like this, surgeons
               can immediately stratify the risk of each patient and provide personalized counseling to high-risk patients
               on lifestyle modifications to prevent flap failure.

               The Artificial Intelligence-Based Multimodal Risk Assessment Model for Surgical Site Infection (AMRAMS)
                                                            [5]
               exemplifies the potential of AI in risk management . By incorporating demographics, preoperative lab
               results, and free-text preoperative notes, AMRAMS leveraged deep learning methods, including CNNs, to
               enhance accuracy. Compared with the National Nosocomial Infections Surveillance risk index manually
               scored by surgeons, AMRAMS offered significantly higher accuracy. The inclusion of semantic embeddings
               of preoperative notes further improves model performance, suggesting that AMRAMS could potentially
               replace traditional risk indices to provide highly personalized guidance for preoperative interventions.


               AI-aided surgical planning is also transforming preoperative consultations. Traditional preoperative
               consultations often involve the use of diagrams, photographs, and verbal descriptions, which may not
               effectively render the potential outcomes of the surgery. In a study published by Chartier et al., BreastGAN,
               an AI tool driven by generative adversarial neural networks (GANs), was able to simulate breast
                                                             [6]
               augmentation outcomes based on preoperative images . The use of another complementary technology in
               tandem with AI is particularly revolutionizing this space. Augmented reality (AR) overlays digital content
               onto the real world. When combined with AI that provides computational analysis and predictive
               capabilities, AR creates more immersive and comprehensive ways to tangibly visualize surgical outcomes,
               helping center patient expectations and improve postoperative satisfaction.


               A prospective study with patients undergoing breast augmentation reported that patients were satisfied with
               preoperative 3D simulation using Arbrea Breast Software (Arbrea Labs, Zurich, Switzerland, 2018) and
               postoperative outcomes, measured with a visual satisfaction analog scale and BREAST-Q Augmentation
                      [7]
               module . Arbrea, similar to BreastGAN, uses a type of generative AI called GANs in combination with AR
               and 3D simulation to help patients visualize surgical outcomes. Additionally, in another cohort of 40
               patients undergoing breast reconstruction, virtual reality (VR) tools (3D imaging) and external sizers were
                                                                                                        [8]
               shown to be the most effective among the five methods tested in choosing the implant volume .
               Furthermore, the Crisalix portable 3D surface imagers, driven by deep learning, predicted breast volume
               with accuracy closely matching estimates from experienced plastic surgeons and actual intraoperative
               specimen weights .
                              [9]
               Despite these exciting results, there are several barriers hindering the wider adoption of AI and AR in
               preoperative consultations. Users report that AR tools like the 3D HoloLens can be cumbersome and
               challenging to use . In terms of breast volume prediction, discrepancies in accuracy may exist between
                               [10]
               different AI softwares, highlighting the need for additional testing and head-to-head comparison when
               choosing the application carefully . Moreover, the cost of implementing AR technology for preoperative
                                            [11]
               planning remains unclear, largely due to its scarce implementation thus far . However, the cost of AR
                                                                                 [12]
               modalities that may implement AI is relatively fixed, such as the Microsoft 3D HoloLens, which retails for
               about  $3,500;  thus,  their  gradually  decreasing  cost  per  use  may  be  beneficial  for  long-term
               implementation . Effective utilization also requires specialized training for surgeons. Therefore, future
                             [13]
               studies should focus on addressing these limitations by exploring cost-effective solutions and developing
               standardized training protocols. Further, large-scale randomized controlled trials are necessary to validate
   105   106   107   108   109   110   111   112   113   114   115