The use of machine learning for investigating the role of plastic surgeons in anatomical injuries: A retrospective observational study

Abstract

While plastic surgeons have been historically indispensable in the reconstruction of posttraumatic defects, their role in trauma centers worldwide has not been clearly defined. Therefore, we aimed to investigate the contribution of plastic surgeons in trauma care using machine learning from an anatomic injury viewpoint. We conducted a retrospective study reviewing the data for all trauma patients of our hospital from March 2019 to February 2021. In total, 4809 patients were classified in duplicate according to the 17 trauma-related departments while conducting the initial treatment. We evaluated several covariates, including age, sex, cause of trauma, treatment outcomes, surgical data, and severity indices, such as the Injury Severity Score and Abbreviated Injury Scale (AIS). A random forest algorithm was used to rank the relevance of 17 trauma-related departments in each category for the AIS and outcomes. Additionally, t test and chi-square test were performed to compare two groups, which were based on whether the patients had received initial treatment in the trauma bay from the plastic surgery department (PS group) or not (non-PS group), in each AIS category. The department of PS was ranked first in the face and external categories after analyzing the relevance of the 17 trauma-related departments in six categories of AIS, through the random forest algorithm. Of the 1108 patients in the face category of AIS, the PS group was not correlated with all outcomes, except for the rate of discharge to home (P < .0001). Upon re-verifying the results using random forest, we found that PS did not affect the outcomes. In the external category in AIS, there were 30 patients in the PS group and 56 patients in the non-PS group, and there was no statistically significant difference between the two groups when comparing the outcomes. PS has contributed considerably to the face and external regions among the six AIS categories; however, there was no correlation between plastic surgical treatment and the outcome of trauma patients. We investigated the plastic surgeons' role based on anatomical injury, using machine learning for the first time in the field of trauma care.

Applications of artificial intelligence in facial plastic and reconstructive surgery: a systematic review

“…Lim et al [17] used three artificial intelligence GANs and two LLMs to generate images and descriptions, respectively, of cosmetically optimal noses, faces, and eyelids. While all artificial intelligence tools accomplished the objective, GAN models demonstrated inherent bias by producing images exclusively of women with lighter skin, an assumed BMI less than 20, and age less than 50 [17].…”

“…Artificial intelligence has shown promise in preoperative surgical planning, cross-sectional imaging analysis, facial palsy assessment, and developing inclusive sex, race, and aging evaluation tools [4 when benchmarking outcomes of FPRS procedures [10][11][12][13][14][15]. In a nonclinical setting, artificial intelligence has been used as a tool to drive research, supplement medical education, and provide digestible insights into care for patients [16][17][18][19][20][21].…”

“…In a postoperative setting, artificial intelligence can provide objectivity when benchmarking outcomes of FPRS procedures [10–15]. In a nonclinical setting, artificial intelligence has been used as a tool to drive research, supplement medical education, and provide digestible insights into care for patients [16–21].…”