Machine learning systems have become integrated into some of the most vital decision-making aspects of humanity, including hiring decisions, loan applications, and automobile safety, to name just a few. As applications increase in both gravity and complexity, the data quality and algorithmic interpretability of the systems must rise to meet those challenges. This is especially vital for navigating the nuances of health care, particularly among the high stakes of surgical operations. In addition to inherent ethical challenges of enabling a “black box” system to influence decision-making in patient care, the creation of biased datasets leads to biased algorithms with the power to perpetuate discrimination and reinforce disparities. Transparency and responsibility are paramount to the implementation of artificial intelligence in surgical decision-making and autonomous robotic surgery. Machine learning has been permeating health care across diverse clinical and surgical contexts but continues to face sizable obstacles, including apprehension from patients and providers alike. To integrate the technology fully while upholding standard of care and patient-provider trust, one must acknowledge and address the ethical, financial, and legal implications of using artificial intelligence for patient care.
Deep learning (DL) is a subset of machine learning that is rapidly gaining traction in surgical fields. Its tremendous capacity for powerful data-driven problem-solving has generated computational breakthroughs in many realms, with the fields of medicine and surgery becoming increasingly prominent avenues. Through its multi-layer architecture of interconnected neural networks, DL enables feature extraction and pattern recognition of highly complex and large-volume data. Across various surgical specialties, DL is being applied to optimize both preoperative planning and intraoperative performance in new and innovative ways. Surgeons are now able to integrate deep learning tools into their practice to improve patient safety and outcomes. Through this review, we explore the applications of deep learning in surgery and related subspecialties with an aim to shed light on the practical utilization of this technology in the present and near future.
The vast and ever-growing volume of electronic health records (EHR) have generated a wealth of information-rich data. Traditional, non-machine learning data extraction techniques are error-prone and laborious, hindering the analytical potential of these massive data sources. Equipped with natural language processing (NLP) tools, surgeons are better able to automate, and customize their review to investigate and implement surgical solutions. We identify current perioperative applications of NLP algorithms as well as research limitations and future avenues to outline the impact and potential of this technology for progressing surgical innovation.
Background: Two-stage implant-based breast reconstruction remains the most commonly performed reconstructive modality following mastectomy. Although prior studies have explored the relationship between tissue expander (TE) features and permanent implant (PI) size in subpectoral reconstruction, no such study exists in prepectoral reconstruction. This study aims to identify pertinent TE characteristics and evaluate their correlations with PI size for prepectoral implant-based reconstruction. Methods: This study analyzed patients who underwent two-stage prepectoral tissue expansion for breast reconstruction followed by implant placement. Patient demographics and oncologic characteristics were recorded. TE and PI features were evaluated. Significant predictors for PI volume were identified using linear and multivariate regression analyses. Results: We identified 177 patients and 296 breast reconstructions that met inclusion criteria. All reconstructions were performed in the prepectoral plane with the majority using acellular dermal matrix (93.8%) and primarily silicone implants (94.3%). Mean TE size was 485.4 cm3 with mean initial fill of 245.8 cm3 and mean final fill of 454.4 cm3. Mean PI size was 502.9 cm3 with a differential fill volume (PI-TE) of 11.7 cm3. Multivariate analysis identified significant features for PI size prediction, including TE size (R2 = 0.60; P < 0.0001) and TE final fill volume (R2 = 0.57; P < 0.0001). The prediction expression for TE final fill and TE size was calculated as 26.6 + 0.38*(TE final fill) + 0.61*(TE size). Conclusions: TE size and final expansion volume were significant variables for implant size prediction. With prepectoral implant placement gaining popularity, the predictive formula may help optimize preoperative planning and decision-making in prepectoral reconstructions.
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