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Artificial intelligence (AI) has emerged as a versatile health-technology tool revolutionizing medical services through the implementation of predictive, preventative, individualized, and participatory approaches. AI encompasses different computational concepts such as machine learning, deep learning techniques, and neural networks. AI also presents a broad platform for improving preoperative planning, intraoperative workflow, and postoperative patient outcomes in the field of oral and maxillofacial surgery (OMFS). The purpose of this review is to present a comprehensive summary of the existing scientific knowledge. The authors thoroughly reviewed English-language PubMed/MEDLINE and Embase papers from their establishment to 1 December 2022. The search terms were (1) "OMFS" OR "oral and maxillofacial" OR "oral and maxillofacial surgery" OR "oral surgery" AND (2) "AI" OR "artificial intelligence". The search format was tailored to each database's syntax. To find pertinent material, each retrieved article and systematic review's reference list was thoroughly examined. According to the literature, AI is already being used in certain areas of OMFS, such as radiographic image quality improvement, diagnosis of cysts and tumors, and localization of cephalometric landmarks. Through additional research, it may be possible to provide practitioners in numerous disciplines with additional assistance to enhance preoperative planning, intraoperative screening, and postoperative monitoring. Overall, AI carries promising potential to advance the field of OMFS and generate novel solution possibilities for persisting clinical challenges. Herein, this review provides a comprehensive summary of AI in OMFS and sheds light on future research efforts. Further, the advanced analysis of complex medical imaging data can support surgeons in preoperative assessments, virtual surgical simulations, and individualized treatment strategies. AI also assists surgeons during intraoperative decision-making by offering immediate feedback and guidance to enhance surgical accuracy and reduce complication rates, for instance by predicting the risk of bleeding.
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Facial vascularized composite allotransplantation (FVCA) is an emerging field of reconstructive surgery that represents a dogmatic shift in the surgical treatment of patients with severe facial disfigurements. While conventional reconstructive strategies were previously considered the goldstandard for patients with devastating facial trauma, FVCA has demonstrated promising short- and long-term outcomes. Yet, there remain several obstacles that complicate the integration of FVCA procedures into the standard workflow for facial trauma patients. Artificial intelligence (AI) has been shown to provide targeted and resource-effective solutions for persisting clinical challenges in various specialties. However, there is a paucity of studies elucidating the combination of FVCA and AI to overcome such hurdles. Here, we delineate the application possibilities of AI in the field of FVCA and discuss the use of AI technology for FVCA outcome simulation, diagnosis and prediction of rejection episodes, and malignancy screening. This line of research may serve as a fundament for future studies linking these two revolutionary biotechnologies.
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BACKGROUND: The grading process in facial palsy (FP) patients is crucial for time- and cost-effective therapy decision-making. The House-Brackmann scale (HBS) represents the most commonly used classification system in FP diagnostics. This study investigated the benefits of linking machine learning (ML) techniques with the HBS. METHODS: Image datasets of 51 patients seen at the Department of Plastic, Hand, and Reconstructive Surgery at the University Hospital Regensburg, Germany, between June 2020 and May 2021, were used to build the neural network. A total of nine facial poses per patient were used to automatically determine the HBS. RESULTS: The algorithm had an accuracy of 98%. The algorithm processed the real patient image series (i.e., nine images per patient) in 112 ms. For optimized accuracy, we found 30 training runs to be the most effective training length. CONCLUSION: We have developed an easy-to-use, time- and cost-efficient algorithm that provides highly accurate automated grading of FP patient images. In combination with our application, the algorithm may facilitate the FP surgeon's clinical workflow.
RESUMO
Background: Colorectal carcinoma (CRC) is a heterogeneous disease, and differences in outcomes have been reported among patients diagnosed with the same disease stage. Prognostic and predictive biomarkers provide information for patient risk stratification and guide treatment selection. Although numerous studies have analyzed the effects of systemic inflammatory factors on CRC outcomes, clinical significance remains to be elucidated. In particular, the treatment strategy of colon cancer patients is different from that of rectal cancer due to outcome and recurrence differences. The identification of patients with a poor prognosis who might benefit from intensive treatment approaches is clinically necessary. Methods: This study aimed to evaluate the value of different blood-based markers and assess the significance of our newly developed inflammatory-nutrition-related biomarker (NCR = BMI × albumin/CRP) in patients with colon cancer. A two-stage design was used with 212 patients with colon cancer (CC) in the discovery cohort (n = 159) and in an external validation cohort (n = 53). Results: A lower preoperative NCR level was significantly correlated with a worse prognosis, sidedness, undifferentiated histology, nodal involvement, and advanced UICC stage. We compared the NCR with other established prognostic indices and showed that the NCR is a more reliable indicator of a poor prognosis for patients with CC. Patients with low NCR levels experienced a significantly shorter Overall Survival (OS) than patients with high levels. Multivariate analysis confirmed preoperative NCR levels as an independent predictor for overall survival with a hazard ratio of 3.3 (95% confidence interval 1.628−6.709, p < 0.001). Finally, we confirmed the predictive value of the NCR in an independent validation cohort and confirmed NCR as an independent prognostic factor for OS. Conclusion: Taken together, we discovered a new prognostic index (NCR) based on BMI, albumin, and CRP levels as an independent prognostic predictor of OS in patients with colon cancer. In all UICC stages, our newly developed NCR marker is able to distinguish patients with better and worse prognoses. We, therefore, propose that NCR may serve as a supplement to the TNM staging system to optimize the risk stratification in CC patients towards personalized oncology. In particular, NCR can be used in clinical trials to stratify patients with UICC II and III tumors and help better select patients who might benefit from adjuvant treatment.