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.

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.

Introduction: Breast cancer represents a traumatic experience with a psychological burden. The prevalence of psychological distress (which include depression and anxiety) among breast cancer patients is estimated to be 15 to 54%, but studies have shown that applying some psychological treatments has contributed to decreasing depression and anxiety. So, it is crucial to diagnose and treat patients with the appropriate means. After reviewing the literature, no studies discussed depression and anxiety among Syrian breast cancer patients. Methods: A cross-sectional study in Al-Bairouni hospital in Damascus, Syria carried out using face-to-face interviews based on a structured questionnaire. All breast cancer patients were included, except who refused to participate, and responses with missing data were excluded. The questionnaire consisted of 2 sections: the first included Socio-demographic characteristics, and the other evaluated patients' depression using PHQ-2 and GAD-2 scales. Data were gathered using the Kobo toolbox app and then entered into an Excel sheet. Results: Five hundred patients were interviewed. 35.6% of the patients had a GAD-2 score greater than or equal to 3.00, while 35% had a PHQ-2 score greater than or equal to 3.00. There is a significant negative relationship between the age of the patient and the GAD-2 score and PHQ-2 score, which means the older the patient is, the lower the GAD-2 and PHQ-2 scores are. A multivariable regression model showed that younger (age ≤ 45 years) and being widowed were associated with being positive for further evaluation for generalized anxiety disorder. Similarly, patients younger than 45 are significantly associated with the need for further evaluation for major depressive disorder (MDD). Social status had a stronger association with the need for further assessment for MDD, with divorced women showing the strongest association, followed by widowed and married women compared to single women. Conclusion: This study showed high anxiety and depression among breast cancer patients. The patient’s age and social status were significant factors in determining the need for further psychological assessment. In General, Younger patients showed higher levels of depression and anxiety, the size of the tumor did not show significant association with psychological distress.

Management of the skin-soft tissue envelope (SSTE) in rhinoplasty is challenging and critical for the overall outcome. The preoperative identification of patients with thick or thin skin and with preexisting skin conditions may contribute significantly to the postsurgical result. Most publications and textbooks focus on the management of the osseocartilaginous framework, but the SSTE management is often not addressed in detail. However, nonsurgical treatments, such as skin preconditioning, topical steroid-injections, oral isotretinoin, and topical vitamin A derivatives, may provide strong benefits. In this article, we reviewed the literature to present a comprehensive review on the available surgical and nonsurgical approaches pre-, intra-, and postoperatively dealing with SSTE in rhinoplasty.

Objective: To determine the distribution of female physicians throughout the rank from resident to leadership positions in German Otolaryngology‐Head and Neck Surgery (OHNS) departments.Methods: This cross‐sectional study collected data about the physician workforce in the German OHNS.The primary outcome was the proportion of female physicians. Data were collected from 165 departments from January 2022 to February 2022. The physician workforce was stratified based on gender and leadership.Results: We included 2089 physicians from 165 departments of OHNS in German hospitals. Female residents and specialists outnumbered male peers (484/872 [55.5%] vs. 388/872 [44.5%] and 224/363 [61.71%] vs. 139/363 [38.29%], respectively). However, the women proportion decreased gradually with elevated hierarchical rank starting at the attending physician level to reaching its lowest extreme (14/165 [7.23%]) at the head of the department level. Holding a leadership position was associated with being male (n = 282 [85.2%] vs. n = 49 [14.8%], p < .0001). This persisted even after controlling the academic rank in a multivariable regression model (OR: 5.027, 95% CI: 3.536–7.146). The gap between the two genders in favor of men regarding leadership persisted in all kinds of hospitals. However, this disparity was lowest in university hospitals, (male: n = 83 [78.3%] vs. female: n = 23, [21.7%], p < .0001).Conclusions: Even though women outnumbered men in resident and specialist positions, they are still underrepresented in leadership positions in OHNS. Continuous surveillance is needed to watch the dynamic changes in the gap between the two genders and address it with more sex equality‐supporting policies.Level of Evidence: IV.

Rhinoplasty is considered a very challenging surgery since minimal changes of this central area of the face may significantly impact a person's appearance and self‐awareness. This is even more challenging in thick‐skinned patients because results are less predictable, and changes to the osseocartilaginous framework (OCF) may not be sufficiently visible due to the blanket effect of the thick skin. Furthermore, pre‐existing skin conditions may exacerbate following surgery. Therefore, managing patients with extremely thick skin or patients who suffer from pre‐existing dermatological conditions such as rosacea or acne requires a synergy of surgeons and dermatologists to achieve optimal results. In this article, we review the most significant pre‐ and post‐surgical regimens that surgeons and dermatologists should apply in selected patients to achieve optimal results after rhinoplasty.